Target component acquisition method and device, computer equipment, readable storage medium and program product

Abstract

The invention relates to a target component acquisition method and device, computer equipment, a readable storage medium and a program product. The method comprises the steps of performing information extraction on demand information of a user to obtain a plurality of pieces of parameter information, screening candidate components according to the parameter information to obtain reference components, obtaining attribute indexes corresponding to the reference components, determining index weights corresponding to the attribute indexes, and aiming at each reference component, determining an index weight corresponding to each attribute index; and obtaining attribute parameters corresponding to the attribute indexes, obtaining a standardization matrix according to the attribute parameters of all the reference components, obtaining model selection scores of all the reference components based on the standardization matrix and the index weights, and obtaining a target component from the reference components according to the sorting order of the model selection scores. By adopting the method, component type selection can be accurately and efficiently realized.

Description

Technical Field

[0001] The present application relates to the technical field of component selection, and in particular to a method, apparatus, computer equipment, readable storage medium, and program product for obtaining a target component. Background Art

[0002] The sheer variety and sheer volume of electronic components presents users with an extremely complex and volatile market environment when selecting the right components for their needs. Traditionally, component selection software systems have primarily screened components based on basic parameters (such as category, name, and manufacturer). Designers then manually compare and analyze key information, such as performance parameters and applicable ranges, based on their expertise and specific needs.

[0003] However, although using selection software for preliminary screening can narrow the selection range, in the subsequent manual comparison and analysis stage, faced with massive amounts of electronic component data, manually identifying and judging the differences in each parameter attribute one by one is not only inefficient but also easily leads to deviations in the selection results. Summary of the Invention

[0004] Based on this, it is necessary to provide a method, device, computer equipment, readable storage medium and program product for obtaining target components that can accurately and efficiently realize component selection in response to the above technical problems.

[0005] In a first aspect, the present application provides a method for obtaining a target component, comprising:

[0006] Extract user demand information to obtain multiple parameter information, and select reference components from candidate components based on the parameter information;

[0007] Obtaining attribute indicators corresponding to reference components and determining the indicator weights corresponding to each attribute indicator;

[0008] For each reference component, obtain the attribute parameters corresponding to the attribute index, and obtain a standardized matrix based on the attribute parameters of all reference components;

[0009] Based on the standardized matrix and indicator weights, the selection score of each reference component is obtained, and the target component is obtained from the reference components according to the sorting order of the selection score.

[0010] In one embodiment, the step of determining the indicator weight corresponding to each attribute indicator includes:

[0011] Determine the reference index from all attribute indicators, and obtain the importance scale corresponding to each attribute indicator based on the importance of each attribute indicator relative to the reference index;

[0012] According to the importance scales corresponding to all attribute indicators, a judgment matrix is obtained; the rows of the judgment matrix represent different attribute indicators, and the columns of the judgment matrix represent the importance of different attribute indicators relative to each attribute indicator;

[0013] For each element in the judgment matrix, the quotient of the element and the sum of all elements in the column where the element is located is used as the normalization parameter corresponding to the element;

[0014] For the matrix formed by all normalized parameters, all normalized parameters in the row where the selection index is located are averaged to obtain the index weight corresponding to the selection index.

[0015] In one embodiment, the step of obtaining the attribute parameter corresponding to the attribute indicator includes:

[0016] For each attribute indicator, obtain attribute information corresponding to the attribute indicator;

[0017] Parameters are extracted from the attribute information to obtain the attribute parameters corresponding to the attribute indicators.

[0018] In one embodiment, the step of obtaining a standardized matrix based on the property parameters of all reference components includes:

[0019] According to the attribute parameters of all reference components, an original matrix is obtained; the rows of the original matrix represent different reference components, and the columns of the original matrix represent the attribute parameters corresponding to different attribute indicators of the reference components;

[0020] For each element in the original matrix, the quotient of the element and the sum of the squares of all elements in the column where the element is located is used as the normalization parameter corresponding to the element, and a normalized matrix is obtained according to all the normalization parameters.

[0021] In one embodiment, the step of obtaining a selection score for each reference component based on a standardized matrix and indicator weights includes:

[0022] According to the standardization matrix, the standard value corresponding to each attribute indicator is obtained; the standard value includes the maximum standard value and the minimum standard value;

[0023] Obtain the scoring matrix corresponding to the reference components based on the standard values and indicator weights;

[0024] The scoring matrix is normalized to obtain the selection scores of reference components.

[0025] In one embodiment, the step of obtaining a scoring matrix corresponding to a reference component according to the standard value and the indicator weight includes:

[0026] For each attribute index of the reference component, obtain the maximum deviation value corresponding to the attribute index according to the index weight and the maximum standard value, and obtain the minimum deviation value corresponding to the attribute index according to the index weight and the minimum standard value;

[0027] According to the maximum deviation value and the minimum deviation value, the index score corresponding to the attribute index is obtained, and according to all the index scores, the score matrix corresponding to the reference component is obtained.

[0028] In a second aspect, the present application further provides a target component acquisition device, comprising:

[0029] An information extraction module is used to extract user demand information, obtain multiple parameter information, and select reference components from candidate components based on the parameter information;

[0030] A weight determination module is used to obtain attribute indicators corresponding to reference components and determine the indicator weight corresponding to each attribute indicator;

[0031] A matrix acquisition module is used to obtain the attribute parameters corresponding to the attribute indicators for each reference component, and obtain a standardized matrix based on the attribute parameters of all reference components;

[0032] The component scoring module is used to obtain the selection score of each reference component based on the standardized matrix and indicator weight, and obtain the target component from the reference components according to the sorting order of the selection score.

[0033] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements any one of the method steps in the first aspect when executing the computer program.

[0034] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which implements any one of the method steps in the first aspect when the computer program is executed by a processor.

[0035] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, which implements any one of the method steps in the first aspect when executed by a processor.

[0036] The above-mentioned target component acquisition method, device, computer equipment, readable storage medium and program product extract multiple parameter information from the user's demand information, can quickly narrow the scope of components based on the parameter information, and screen out reference components. By comprehensively considering multiple attribute indicators of the reference components and the corresponding indicator weights, it is possible to comprehensively and comprehensively evaluate the degree of match between each reference component and the user's needs, thereby accurately obtaining the selection score of the reference component and accurately and efficiently realizing component selection. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments of the present application or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying any creative work.

[0038] Figure 1 A diagram illustrating an application environment of a target component acquisition method according to an embodiment;

[0039] Figure 2 1 is a flow chart of a method for acquiring a target component in one embodiment;

[0040] Figure 3 Schematic diagram of a flow chart of a method for acquiring a target component in another embodiment;

[0041] Figure 4 This is a structural block diagram of a target component acquisition device in one embodiment;

[0042] Figure 5 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0044] The target component acquisition method provided in the embodiment of the present application can be applied to Figure 1In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store data that server 104 needs to process. The data storage system can be integrated on server 104, or placed on a cloud or other network server. Terminal 102 is used to obtain user demand information from server 104, extract information from the user demand information, obtain multiple parameter information, and screen reference components from candidate components based on the parameter information, obtain attribute indicators corresponding to the reference components, and determine the indicator weights corresponding to each attribute indicator. For each reference component, attribute parameters corresponding to the attribute indicators are obtained, and a standardized matrix is obtained based on the attribute parameters of all reference components. Based on the standardized matrix and the indicator weights, a selection score for each reference component is obtained, and a target component is obtained from the reference components based on the sort order of the selection scores. Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can be smart speakers, smart TVs, smart air conditioners, smart car devices, projectors, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. Head-mounted devices can be virtual reality (VR) devices, augmented reality (AR) devices, smart glasses, etc. Server 104 can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0045] In an exemplary embodiment, Figure 2 As shown, a method for obtaining target components is provided, which is applied to Figure 1 The terminal 102 in the example is used as an example to illustrate the process, including the following steps 202 to 208. Among them:

[0046] S202: extracting user demand information to obtain a plurality of parameter information, and screening candidate components to obtain reference components based on the parameter information.

[0047] Optionally, the demand information is the basic information of components input by the user when selecting. By extracting information from the demand information, multiple parameter information is obtained, where the parameter information includes component product type, price cost control, production cycle range, operating temperature, performance parameters, etc. The parameter information can be used to narrow the range of candidate components and screen out reference components.

[0048] S204: Acquire attribute indicators corresponding to reference components, and determine indicator weights corresponding to each attribute indicator.

[0049] Optionally, attribute indicators are used to characterize attribute information of reference components, such as the number of alternative products, number of failures, price and cost, overall performance, application history, supply channels, and after-sales service. Due to differences in component structure and process, the attribute indicators of each component are not exactly the same. For each reference component, the indicator weight corresponding to each attribute indicator is obtained. The indicator weight is used to represent the degree of influence of the attribute indicator on the component performance.

[0050] S206: For each reference component, obtain the attribute parameter corresponding to the attribute index, and obtain a standardized matrix based on the attribute parameters of all reference components.

[0051] Optionally, for each reference component, obtain the specific attribute parameter values corresponding to each attribute indicator. Because different attribute indicators may have different dimensions and numerical ranges, these attribute parameters need to be standardized and converted into dimensionless values to facilitate comparison and calculation. By standardizing the attribute parameters of all reference components, a standardized matrix is obtained, in which each row represents a reference component, and each column represents the standardized parameter value of an attribute indicator.

[0052] S208: Based on the standardized matrix and the indicator weight, a selection score of each reference component is obtained, and a target component is obtained from the reference components according to the sorting order of the selection score.

[0053] Optionally, for each reference component, a selection score of the reference component is calculated based on the standardized matrix and the indicator weight corresponding to the reference component. After obtaining the selection scores of all reference components, they are sorted in descending order according to the scores, so that the user can obtain the target component from the reference components according to the sorting.

[0054] In the above-mentioned target component acquisition method, multiple parameter information is extracted from the user's demand information. According to the parameter information, the scope of components can be quickly narrowed down, and reference components can be screened. By comprehensively considering multiple attribute indicators of the reference components and the corresponding indicator weights, the degree of match between each reference component and the user's needs can be comprehensively and comprehensively evaluated, thereby accurately obtaining the selection score of the reference component and realizing component selection accurately and efficiently.

[0055] In an exemplary embodiment, the step of determining the indicator weight corresponding to each attribute indicator includes: determining a reference indicator from all attribute indicators, and obtaining the importance scale corresponding to each attribute indicator based on the importance of each attribute indicator relative to the reference indicator; obtaining a judgment matrix based on the importance scales corresponding to all attribute indicators; the rows of the judgment matrix represent different attribute indicators, and the columns of the judgment matrix represent the importance of different attribute indicators relative to each attribute indicator; for each element in the judgment matrix, the quotient of the element and the sum of all elements in the column where the element is located is used as the normalization parameter corresponding to the element; for the matrix formed by all normalization parameters, all normalization parameters in the row where the selection indicator is located are averaged to obtain the indicator weight corresponding to the selection indicator.

[0056] Alternatively, one attribute indicator can be selected as a reference indicator. Based on the importance of each attribute indicator relative to the reference indicator, an importance scale can be assigned to each attribute indicator, thereby quantifying the importance differences between different attribute indicators. For example, if the number of alternative products is selected as the reference indicator, then the attribute indicator of failure count can be assigned a corresponding importance scale value based on its relative importance to stability in practical applications.

[0057] Furthermore, a judgment matrix is constructed based on the importance scales corresponding to all attribute indicators. The rows and columns of the judgment matrix represent different attribute indicators, and the elements in the matrix indicate the importance of the attribute indicator in the corresponding row relative to the attribute indicator in the corresponding column. For each element in the judgment matrix, the quotient of the element and the sum of all elements in the column in which the element resides is used as the corresponding normalization parameter. This converts importance scales of different magnitudes and meanings into comparable normalized values, allowing the importance of different attribute indicators to be measured on the same scale.

[0058] Furthermore, for the matrix formed by all normalized parameters, all normalized parameters in the row where the selection indicator is located are averaged to obtain the indicator weight corresponding to the selection indicator. By averaging the normalized parameters in each row, the normalized importance of the attribute indicator relative to all other attribute indicators can be comprehensively considered, thereby obtaining a weight value that can accurately reflect the importance of the attribute indicator in the entire selection process.

[0059] For example, based on the selected candidate components, users can further select one of the attribute indicators as a reference according to their own selection needs, and divide the selection priority according to several levels such as slightly important, obviously important, strongly important, and extremely important. The specific judgment basis is shown in Table 1.

[0060] Table 1 Judgment basis of attribute indicators

[0061]

[0062] Furthermore, according to the judgment basis in Table 1, the importance scale of each attribute indicator is obtained. Assuming that there are n attribute indicators, the constructed judgment matrix is an n×n matrix X:

[0063]

[0064] Among them, the rows of the judgment matrix represent different attribute indicators, and the columns represent the importance of different attribute indicators relative to each attribute indicator.

[0065] Furthermore, the judgment matrix X is normalized, that is, each element is divided by the sum of its column. The calculation formula is:

[0066]

[0067] Among them, the normalized matrix X' is:

[0068]

[0069] Furthermore, for each attribute indicator, all elements in its row are averaged to obtain the indicator weight w corresponding to the attribute indicator. i :

[0070]

[0071] In this embodiment, by determining the reference indicator from all attribute indicators and obtaining the importance scale corresponding to each attribute indicator according to the importance of each attribute indicator relative to the reference indicator, the importance of all attribute indicators can be accurately quantified, and then the indicator weights can be calculated based on the established judgment matrix, which can accurately reflect the importance of each attribute indicator and improve the accuracy of the selection score calculation.

[0072] In an exemplary embodiment, the step of obtaining attribute parameters corresponding to the attribute indicators includes: for each attribute indicator, obtaining attribute information corresponding to the attribute indicator; and performing parameter extraction on the attribute information to obtain attribute parameters corresponding to the attribute indicator.

[0073] Optionally, for each attribute indicator, the attribute information corresponding to the attribute indicator is obtained. Since the attribute information is represented in various ways, for example, an extremely large parameter may reflect its characteristic of being the larger the better through some descriptions, an extremely small parameter may have a description of being the smaller the better, and an interval parameter may have content that mentions that the indicator data is best when it falls within a specific interval, etc. This information may come from product manuals, technical specifications, industry standard documents, etc., and is the basis for understanding the characteristics of attribute indicators.

[0074] Furthermore, from the acquired attribute information, different parameters are extracted based on their representation and characteristics. For extremely large parameters, their numerical values are directly confirmed, as the larger the better, and no conversion is required, so they can be used directly as attribute parameters. For extremely small parameters, a forward conversion is performed according to the given formula: Y = maximum value + minimum value - y, where y represents the original value of the reverse indicator, Y represents the processed indicator value, maximum value represents the maximum value of the parameter, and minimum value represents the minimum value of the parameter. For interval parameters, the optimal interval range is determined, and the data related to this range is used as the attribute parameter. For descriptive parameters, since these parameters are text and cannot be directly converted into forward indicators, such as the quality grade and implementation standards of components, they can be converted into numerical values based on the built-in algorithms of industry standards. For example, if the parameter description is "C1", it is converted to 1, and if the parameter description is "C2", it is converted to 2.

[0075] Among them, for interval parameters, the formula for determining their attribute parameters is:

[0076]

[0077] Among them, [a,b] is the optimal interval of performance parameter x, is the maximum tolerance range.

[0078] In this embodiment, by extracting parameters from the attribute information and obtaining attribute parameters corresponding to the attribute indicators, the attribute parameters of different representations and properties can be normalized to more accurately reflect the performance characteristics of the components and provide accurate data support for the subsequent calculation of the component selection score.

[0079] In an exemplary embodiment, the step of obtaining a standardized matrix based on the attribute parameters of all reference components includes: obtaining an original matrix based on the attribute parameters of all reference components; the rows of the original matrix represent different reference components, and the columns of the original matrix represent the attribute parameters corresponding to different attribute indicators of the reference components; for each element in the original matrix, the quotient of the element and the sum of the squares of all elements in the column where the element is located is used as the standardized parameter corresponding to the element, and the standardized matrix is obtained based on all the standardized parameters.

[0080] Optionally, an original matrix is established based on the attribute parameters of all reference components, where each row of the matrix represents a different reference component, and each column represents the attribute parameters corresponding to different attribute indicators of the reference component. For each element in the original matrix, its corresponding standardized parameter is obtained through a specific calculation method. Specifically, the element is divided by the sum of the squares of all elements in its column, and the quotient obtained is the standardized parameter of the element. By performing such calculations on all elements in the original matrix, a standardized matrix consisting of all standardized parameters is finally obtained. The standardized matrix normalizes the data while maintaining the relative relationship of the original data, so that the parameters of different attribute indicators are comparable.

[0081] For example, assuming there are n reference components and m attribute indicators, the constructed original matrix X is:

[0082]

[0083] Furthermore, the original matrix is normalized to obtain a normalized matrix Z, where each element Z in the normalized matrix Z is i,j , and its calculation formula is:

[0084]

[0085] Finally, the obtained normalized matrix Z is expressed as:

[0086]

[0087] In this embodiment, an original matrix is established based on the attribute parameters and the original matrix is standardized to obtain a standardized matrix, so that the data of all reference components on various attribute indicators are on the same scale, thereby more accurately determining the overall performance of each reference component and improving the accuracy of component selection.

[0088] In an exemplary embodiment, the step of obtaining the selection score of each reference component based on the standardized matrix and indicator weights includes: obtaining the standard value corresponding to each attribute indicator according to the standardized matrix; the standard value includes a maximum standard value and a minimum standard value; obtaining the scoring matrix corresponding to the reference component according to the standard value and the indicator weight; and normalizing the scoring matrix to obtain the selection score of the reference component.

[0089] Optionally, the maximum and minimum standard values of each attribute index are determined from the standardized matrix. These standard values represent the optimal and worst levels achieved by each attribute index among all reference components. Based on the previously obtained standard values and the indicator weights corresponding to each attribute index, the scores of each reference component on each attribute index are calculated to form a scoring matrix. The scoring matrix is normalized to unify the scores of each component into a specific range for intuitive comparison and sorting. After normalization, the resulting selection score can directly reflect the relative advantages and disadvantages of each reference component among all candidate components. The higher the score, the more the component meets the selection requirements.

[0090] In this embodiment, by combining the standardized matrix and indicator weights to calculate the selection score, the impact of multiple attribute indicators on component selection can be fully considered. Among multiple candidate components, by comparing the selection scores, it is possible to quickly determine which components have better comprehensive performance, thereby accurately and efficiently achieving component selection.

[0091] In an exemplary embodiment, the step of obtaining a scoring matrix corresponding to a reference component based on a standard value and an indicator weight includes: for each attribute indicator of the reference component, obtaining a maximum deviation value corresponding to the attribute indicator based on the indicator weight and the maximum standard value, and obtaining a minimum deviation value corresponding to the attribute indicator based on the indicator weight and the minimum standard value; obtaining an indicator score corresponding to the attribute indicator based on the maximum deviation value and the minimum deviation value, and obtaining a scoring matrix corresponding to the reference component based on all indicator scores.

[0092] For example, the maximum standard value Z is defined as + The calculation formula is:

[0093]

[0094] Define the minimum standard value Z - The calculation formula is:

[0095]

[0096] Furthermore, according to the indicator weight w i and the maximum standard value Z + , the maximum deviation value D corresponding to the attribute index of the i-th reference component + The calculation formula is:

[0097]

[0098] The minimum deviation value D corresponding to the attribute index of the i-th reference component - The calculation formula is:

[0099]

[0100] Furthermore, the selection score S of the i-th reference component i for:

[0101]

[0102] Where 0≤S i ≤1, and S i The bigger, The smaller it is, the closer it is to the maximum value. Therefore, the scoring matrix corresponding to the i-th reference component is:

[0103]

[0104] Furthermore, the scoring matrix is normalized and its calculation formula is:

[0105]

[0106] Then, the selection score obtained after normalization is:

[0107]

[0108] In this embodiment, the maximum deviation value and the minimum deviation value corresponding to the reference component are obtained based on the indicator weight and the maximum standard value and the minimum standard value of the reference component, and then the scoring matrix corresponding to the reference component is obtained. The scoring matrix can be used to integrate the scores of multiple attribute indicators of the reference component, so as to accurately obtain the selection score of the reference component.

[0109] In an exemplary embodiment, Figure 3 As shown, a method for obtaining a target component is provided. Taking the example of selecting a CPU from a current component catalog that requires a component that can operate in an environment of 60 to -20 degrees Celsius for a long period of time, has a core voltage of less than 2V, a current of 10mA, can supply 1,000 units per year, and has a total cost of less than 5 million, the method includes the following steps:

[0110] (1) Obtain parameter information.

[0111] Among them, the user's demand information is extracted, and the parameter information obtained is shown in Table 2:

[0112] Table 2 Parameter information table

[0113]

[0114] (2) Screening components.

[0115] Among them, according to the extracted component parameter information, the component list range is automatically filtered, and the filtered component information is shown in Table 3:

[0116] Table 3 Screened component information

[0117]

[0118] (3) Determine the indicator weights.

[0119] Among them, according to the reference indicators set by the user, the importance scale of each attribute indicator is obtained, as shown in Table 4:

[0120] Table 4 Importance scale of attribute indicators

[0121]

[0122] According to Table 4, the judgment matrix is constructed as follows:

[0123]

[0124] Normalize the judgment matrix X and get the matrix X' expressed as:

[0125]

[0126] Then, the indicator weights of each attribute indicator are shown in Table 5:

[0127] Table 5 Index weight table

[0128]

[0129] (4) Get attribute parameters.

[0130] According to the screening results of step (2), the attribute parameters of the reference components are processed for parameter forwarding, and the obtained attribute parameters are shown in Table 6:

[0131] Table 6 Attribute parameter table of reference components

[0132]

[0133] (5) Standardization processing.

[0134] Among them, the original matrix constructed according to the attribute parameters of the reference components is expressed as:

[0135]

[0136] After normalizing the original matrix, the resulting matrix is expressed as:

[0137]

[0138] (6) Calculate the selection score.

[0139] Among them, according to the judgment matrix after standardization, the maximum standard value and the minimum standard value of each attribute indicator are obtained, as shown in Table 7:

[0140] Table 7 Schematic diagram of standard values of attribute indicators

[0141]

[0142] Based on the maximum and minimum standard values, the selection score of the reference components is calculated as follows:

[0143]

[0144] (7) Component sorting.

[0145] Among them, the ranking is performed according to the selection score of each reference component, and the ranking results are shown in Table 8:

[0146] Table 8 Selection score ranking table

[0147]

[0148] In this embodiment, by extracting multiple parameter information from the user's demand information, the scope of components can be quickly narrowed down based on the parameter information, and reference components can be screened. By comprehensively considering multiple attribute indicators of the reference components and the corresponding indicator weights, the degree of match between each reference component and the user's needs can be comprehensively and comprehensively evaluated, thereby accurately obtaining the selection score of the reference component and accurately and efficiently realizing component selection.

[0149] It should be understood that, although the steps in the flowcharts of the above embodiments are shown in sequence as indicated by the arrows, these steps are not necessarily performed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be performed in other orders. Moreover, at least a portion of the steps in the flowcharts of the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily performed at the same time, but can be performed at different times. The execution order of these steps or stages is not necessarily to be performed in sequence, but can be performed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0150] Based on the same inventive concept, the present application also provides a target component acquisition device for implementing the target component acquisition method described above. The solution provided by this device is similar to the solution described in the method described above. Therefore, the specific limitations of one or more target component acquisition device embodiments provided below can be found in the above-mentioned limitations of the target component acquisition method and will not be repeated here.

[0151] In an exemplary embodiment, Figure 4 As shown, a target component acquisition device is provided, comprising: an information extraction module 10, a weight determination module 20, a matrix acquisition module 30 and a component scoring module 40, wherein:

[0152] The information extraction module 10 is used to extract user demand information to obtain a plurality of parameter information, and to screen reference components from candidate components based on the parameter information.

[0153] The weight determination module 20 is used to obtain attribute indicators corresponding to reference components and determine the indicator weight corresponding to each attribute indicator.

[0154] The matrix acquisition module 30 is used to acquire the attribute parameters corresponding to the attribute indicators for each reference component, and to acquire a standardized matrix based on the attribute parameters of all reference components.

[0155] The component scoring module 40 is used to obtain the selection score of each reference component based on the standardized matrix and the indicator weight, and obtain the target component from the reference components according to the sorting order of the selection score.

[0156] In an exemplary embodiment, the weight determination module 20 is also used to determine a reference indicator from all attribute indicators, and obtain the importance scale corresponding to each attribute indicator based on the importance of each attribute indicator relative to the reference indicator; obtain a judgment matrix based on the importance scales corresponding to all attribute indicators; the rows of the judgment matrix represent different attribute indicators, and the columns of the judgment matrix represent the importance of different attribute indicators relative to each attribute indicator; for each element in the judgment matrix, the quotient of the element and the sum of all elements in the column where the element is located is used as the normalization parameter corresponding to the element; for the matrix formed by all normalization parameters, all normalization parameters in the row where the selection indicator is located are averaged to obtain the indicator weight corresponding to the selection indicator.

[0157] In an exemplary embodiment, the matrix acquisition module 30 is further configured to acquire, for each attribute indicator, attribute information corresponding to the attribute indicator; and perform parameter extraction on the attribute information to obtain attribute parameters corresponding to the attribute indicator.

[0158] In an exemplary embodiment, the matrix acquisition module 30 is also used to obtain the original matrix based on the attribute parameters of all reference components; the rows of the original matrix represent different reference components, and the columns of the original matrix represent the attribute parameters corresponding to different attribute indicators of the reference components; for each element in the original matrix, the quotient of the element and the sum of the squares of all elements in the column where the element is located is used as the normalization parameter corresponding to the element, and a normalized matrix is obtained based on all the normalization parameters.

[0159] In an exemplary embodiment, the component scoring module 40 is also used to obtain the standard value corresponding to each attribute indicator based on the standardized matrix; the standard value includes a maximum standard value and a minimum standard value; based on the standard value and the indicator weight, the scoring matrix corresponding to the reference component is obtained; the scoring matrix is normalized to obtain the selection score of the reference component.

[0160] In an exemplary embodiment, the component scoring module 40 is also used to obtain, for each attribute indicator of the reference component, the maximum deviation value corresponding to the attribute indicator based on the indicator weight and the maximum standard value, and to obtain the minimum deviation value corresponding to the attribute indicator based on the indicator weight and the minimum standard value; obtain the indicator score corresponding to the attribute indicator based on the maximum deviation value and the minimum deviation value, and obtain the scoring matrix corresponding to the reference component based on all the indicator scores.

[0161] Each module in the aforementioned target component acquisition device can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in the computer device in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0162] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 5As shown. The computer device includes a processor, memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals via wired or wireless means, and the wireless means can be implemented via Wi-Fi, a mobile cellular network, near-field communication (NFC), or other technologies. When executed by the processor, the computer program implements a method for acquiring a target component. The display unit of the computer device is used to form a visually visible image, and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device casing, or an external keyboard, touchpad or mouse.

[0163] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0164] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the following steps when executing the computer program: extracting user demand information to obtain multiple parameter information, and screening reference components from candidate components based on the parameter information; obtaining attribute indicators corresponding to the reference components, and determining the indicator weights corresponding to each attribute indicator; obtaining attribute parameters corresponding to the attribute indicators for each reference component, and obtaining a standardized matrix based on the attribute parameters of all reference components; obtaining a selection score for each reference component based on the standardized matrix and the indicator weights, and obtaining a target component from the reference components according to the sorting order of the selection scores.

[0165] In one embodiment, when a processor executes a computer program, determining the indicator weight corresponding to each attribute indicator involves: determining a reference indicator from all attribute indicators, and obtaining an importance scale corresponding to each attribute indicator based on the importance of each attribute indicator relative to the reference indicator; obtaining a judgment matrix based on the importance scales corresponding to all attribute indicators; the rows of the judgment matrix represent different attribute indicators, and the columns of the judgment matrix represent the importance of different attribute indicators relative to each attribute indicator; for each element in the judgment matrix, taking the quotient of the element and the sum of all elements in the column where the element is located as the normalization parameter corresponding to the element; for the matrix formed by all normalization parameters, performing mean processing on all normalization parameters in the row where the selection indicator is located to obtain the indicator weight corresponding to the selection indicator.

[0166] In one embodiment, obtaining attribute parameters corresponding to attribute indicators when a processor executes a computer program includes: obtaining attribute information corresponding to each attribute indicator; and performing parameter extraction on the attribute information to obtain attribute parameters corresponding to the attribute indicator.

[0167] In one embodiment, when a processor executes a computer program, the process involves obtaining a standardized matrix based on the property parameters of all reference components, including: obtaining an original matrix based on the property parameters of all reference components; the rows of the original matrix represent different reference components, and the columns of the original matrix represent the property parameters corresponding to different property indicators of the reference components; for each element in the original matrix, the quotient of the element and the sum of the squares of all elements in the column where the element is located is used as the standardized parameter corresponding to the element, and the standardized matrix is obtained based on all the standardized parameters.

[0168] In one embodiment, when a processor executes a computer program, the selection score of each reference component is obtained based on a standardized matrix and indicator weights, including: obtaining a standard value corresponding to each attribute indicator according to the standardized matrix; the standard value includes a maximum standard value and a minimum standard value; obtaining a scoring matrix corresponding to the reference component according to the standard value and the indicator weight; and normalizing the scoring matrix to obtain a selection score for the reference component.

[0169] In one embodiment, when a processor executes a computer program, the steps involved in obtaining a scoring matrix corresponding to a reference component based on a standard value and an indicator weight include: for each attribute indicator of the reference component, obtaining a maximum deviation value corresponding to the attribute indicator based on the indicator weight and the maximum standard value, and obtaining a minimum deviation value corresponding to the attribute indicator based on the indicator weight and the minimum standard value; obtaining an indicator score corresponding to the attribute indicator based on the maximum deviation value and the minimum deviation value, and obtaining a scoring matrix corresponding to the reference component based on all the indicator scores.

[0170] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented: information extraction is performed on user demand information to obtain multiple parameter information, and reference components are screened from candidate components based on the parameter information; attribute indicators corresponding to the reference components are obtained, and indicator weights corresponding to each attribute indicator are determined; for each reference component, attribute parameters corresponding to the attribute indicators are obtained, and a standardized matrix is obtained based on the attribute parameters of all reference components; based on the standardized matrix and the indicator weights, a selection score of each reference component is obtained, and a target component is obtained from the reference components according to the sorting order of the selection scores.

[0171] In one embodiment, when a computer program is executed by a processor, the steps involved in determining the indicator weight corresponding to each attribute indicator include: determining a reference indicator from all attribute indicators, and obtaining an importance scale corresponding to each attribute indicator based on the importance of each attribute indicator relative to the reference indicator; obtaining a judgment matrix based on the importance scales corresponding to all attribute indicators; the rows of the judgment matrix represent different attribute indicators, and the columns of the judgment matrix represent the importance of different attribute indicators relative to each attribute indicator; for each element in the judgment matrix, taking the quotient of the element and the sum of all elements in the column where the element is located as the normalization parameter corresponding to the element; for the matrix formed by all normalization parameters, performing mean processing on all normalization parameters in the row where the selection indicator is located to obtain the indicator weight corresponding to the selection indicator.

[0172] In one embodiment, obtaining attribute parameters corresponding to attribute indicators when a computer program is executed by a processor includes: obtaining attribute information corresponding to each attribute indicator; and performing parameter extraction on the attribute information to obtain attribute parameters corresponding to the attribute indicator.

[0173] In one embodiment, when a computer program is executed by a processor, the steps involved in obtaining a standardized matrix based on the property parameters of all reference components include: obtaining an original matrix based on the property parameters of all reference components; the rows of the original matrix represent different reference components, and the columns of the original matrix represent the property parameters corresponding to different property indicators of the reference components; for each element in the original matrix, taking the quotient of the element and the sum of the squares of all elements in the column where the element is located as the standardized parameter corresponding to the element, and obtaining a standardized matrix based on all the standardized parameters.

[0174] In one embodiment, when a computer program is executed by a processor, the computer program involves obtaining a selection score for each reference component based on a standardized matrix and indicator weights, including: obtaining a standard value corresponding to each attribute indicator according to the standardized matrix; the standard value includes a maximum standard value and a minimum standard value; obtaining a scoring matrix corresponding to the reference component according to the standard value and the indicator weight; and normalizing the scoring matrix to obtain a selection score for the reference component.

[0175] In one embodiment, when a computer program is executed by a processor, the steps involved in obtaining a scoring matrix corresponding to a reference component based on a standard value and an indicator weight include: for each attribute indicator of the reference component, obtaining a maximum deviation value corresponding to the attribute indicator based on the indicator weight and the maximum standard value, and obtaining a minimum deviation value corresponding to the attribute indicator based on the indicator weight and the minimum standard value; obtaining an indicator score corresponding to the attribute indicator based on the maximum deviation value and the minimum deviation value, and obtaining a scoring matrix corresponding to the reference component based on all the indicator scores.

[0176] In one embodiment, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the following steps: extracting user demand information to obtain multiple parameter information, and screening reference components from candidate components based on the parameter information; obtaining attribute indicators corresponding to the reference components, and determining the indicator weights corresponding to the attribute indicators; obtaining attribute parameters corresponding to the attribute indicators for each reference component, and obtaining a standardized matrix based on the attribute parameters of all reference components; obtaining a selection score for each reference component based on the standardized matrix and the indicator weights, and obtaining a target component from the reference components based on the sorting order of the selection scores.

[0177] In one embodiment, when a computer program is executed by a processor, the steps involved in determining the indicator weight corresponding to each attribute indicator include: determining a reference indicator from all attribute indicators, and obtaining an importance scale corresponding to each attribute indicator based on the importance of each attribute indicator relative to the reference indicator; obtaining a judgment matrix based on the importance scales corresponding to all attribute indicators; the rows of the judgment matrix represent different attribute indicators, and the columns of the judgment matrix represent the importance of different attribute indicators relative to each attribute indicator; for each element in the judgment matrix, taking the quotient of the element and the sum of all elements in the column where the element is located as the normalization parameter corresponding to the element; for the matrix formed by all normalization parameters, performing mean processing on all normalization parameters in the row where the selection indicator is located to obtain the indicator weight corresponding to the selection indicator.

[0178] In one embodiment, obtaining attribute parameters corresponding to attribute indicators when a computer program is executed by a processor includes: obtaining attribute information corresponding to each attribute indicator; and performing parameter extraction on the attribute information to obtain attribute parameters corresponding to the attribute indicator.

[0179] In one embodiment, when a computer program is executed by a processor, the steps involved in obtaining a standardized matrix based on the property parameters of all reference components include: obtaining an original matrix based on the property parameters of all reference components; the rows of the original matrix represent different reference components, and the columns of the original matrix represent the property parameters corresponding to different property indicators of the reference components; for each element in the original matrix, taking the quotient of the element and the sum of the squares of all elements in the column where the element is located as the standardized parameter corresponding to the element, and obtaining a standardized matrix based on all the standardized parameters.

[0180] In one embodiment, when a computer program is executed by a processor, the computer program involves obtaining a selection score for each reference component based on a standardized matrix and indicator weights, including: obtaining a standard value corresponding to each attribute indicator according to the standardized matrix; the standard value includes a maximum standard value and a minimum standard value; obtaining a scoring matrix corresponding to the reference component according to the standard value and the indicator weight; and normalizing the scoring matrix to obtain a selection score for the reference component.

[0181] In one embodiment, when a computer program is executed by a processor, the steps involved in obtaining a scoring matrix corresponding to a reference component based on a standard value and an indicator weight include: for each attribute indicator of the reference component, obtaining a maximum deviation value corresponding to the attribute indicator based on the indicator weight and the maximum standard value, and obtaining a minimum deviation value corresponding to the attribute indicator based on the indicator weight and the minimum standard value; obtaining an indicator score corresponding to the attribute indicator based on the maximum deviation value and the minimum deviation value, and obtaining a scoring matrix corresponding to the reference component based on all the indicator scores.

[0182] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), quantum computing-based data processing logic devices, artificial intelligence (AI) processors, and the like.

[0183] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0184] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for obtaining a target component, characterized in that: The method comprises: Extracting user demand information to obtain multiple parameter information, and screening reference components from candidate components based on the parameter information; Obtaining attribute indicators corresponding to the reference components, and determining an indicator weight corresponding to each attribute indicator; For each reference component, obtaining the attribute parameter corresponding to the attribute indicator, and obtaining a standardized matrix based on the attribute parameters of all reference components; Based on the standardized matrix and the indicator weights, a selection score of each of the reference components is obtained, and a target component is obtained from the reference components according to the sorting order of the selection scores.

2. The method according to claim 1, characterized in that Determining the indicator weight corresponding to each of the attribute indicators includes: Determine a reference indicator from all attribute indicators, and obtain an importance scale corresponding to each attribute indicator according to the importance of each attribute indicator relative to the reference indicator; Obtaining a judgment matrix based on the importance scales corresponding to all attribute indicators; wherein the rows of the judgment matrix represent different attribute indicators, and the columns of the judgment matrix represent the importance of different attribute indicators relative to each attribute indicator; For each element in the judgment matrix, taking the quotient of the element and the sum of all elements in the column where the element is located as the normalization parameter corresponding to the element; For the matrix formed by all normalized parameters, all normalized parameters in the row where the selection index is located are averaged to obtain the index weight corresponding to the selection index.

3. The method according to claim 1, characterized in that The acquiring of the attribute parameters corresponding to the attribute indicators includes: For each attribute indicator, obtaining attribute information corresponding to the attribute indicator; Parameters are extracted from the attribute information to obtain attribute parameters corresponding to the attribute indicators.

4. The method according to claim 1, wherein The step of obtaining a standardized matrix based on the attribute parameters of all reference components includes: Obtaining an original matrix based on the attribute parameters of all reference components; wherein the rows of the original matrix represent different reference components, and the columns of the original matrix represent the attribute parameters corresponding to different attribute indicators of the reference components; For each element in the original matrix, the quotient of the element and the sum of the squares of all elements in the column where the element is located is used as the normalization parameter corresponding to the element, and a normalized matrix is obtained according to all the normalization parameters.

5. The method according to claim 1, wherein The obtaining of a selection score for each of the reference components based on the standardized matrix and the indicator weights includes: According to the standardization matrix, obtaining the standard value corresponding to each of the attribute indicators; the standard value includes a maximum standard value and a minimum standard value; Obtaining a scoring matrix corresponding to the reference component according to the standard value and the indicator weight; The scoring matrix is normalized to obtain the selection score of the reference component.

6. The method according to claim 5, characterized in that The step of obtaining a scoring matrix corresponding to the reference component according to the standard value and the indicator weight includes: For each attribute index of the reference component, obtaining a maximum deviation value corresponding to the attribute index according to the index weight and the maximum standard value, and obtaining a minimum deviation value corresponding to the attribute index according to the index weight and the minimum standard value; According to the maximum deviation value and the minimum deviation value, an indicator score corresponding to the attribute indicator is obtained, and according to all the indicator scores, a score matrix corresponding to the reference component is obtained.

7. A target component acquisition device, characterized in that: The device comprises: An information extraction module is used to extract user demand information to obtain a plurality of parameter information, and to select reference components from candidate components based on the parameter information; A weight determination module, configured to obtain attribute indices corresponding to the reference components and determine an indicator weight corresponding to each attribute indices; A matrix acquisition module, configured to acquire, for each reference component, the attribute parameters corresponding to the attribute index, and acquire a standardized matrix based on the attribute parameters of all reference components; A component scoring module is used to obtain a selection score of each of the reference components based on the standardized matrix and the indicator weight, and to obtain a target component from the reference components according to the sorting order of the selection score.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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