Capacitor classification identification method, device, storage medium and electronic device

Abstract

The present application discloses a capacitor classification and identification method, device, storage medium and electronic device, including: obtaining component text, wherein the component text includes the capacitor parameters recorded in the bill of materials, obtaining the capacitor type related to the component text according to the component text, and calculating the sum of scores corresponding to the capacitor type; wherein the sum of scores represents the correlation between the capacitor type and the component text, and according to the numerical value of the sum of scores, determining the capacitor type corresponding to the component text. By obtaining the capacitor type related to the component text and calculating the sum of scores corresponding to the capacitor type, this method can accurately determine the capacitor type corresponding to the component text, thereby improving the accuracy of identification.

Description

Technical Field

[0001] The present application relates to the technical field of component classification and identification, and specifically to a capacitor classification and identification method, device, storage medium and electronic device. Background Art

[0002] At present, the bill of materials generally includes the types and quantities of materials required to assemble or produce a product. For example, the bill of materials includes capacitors and the corresponding parameters of capacitors. As product functions continue to expand, the types of materials included in the product's bill of materials are also increasing. For example, when identifying the type of capacitor in the bill of materials, there may be some useless information or erroneous codes in the bill of materials, which interferes with the identification of the type of capacitor, resulting in the inability to accurately identify the type of capacitor in the bill of materials. Summary of the invention

[0003] In order to solve the above technical problems, the present application is proposed. The embodiments of the present application provide a capacitance classification identification method, device, storage medium and electronic device, thereby solving the problem of low capacitance type identification.

[0004] According to one aspect of the present application, a capacitor classification and identification method is provided, comprising: obtaining a component text; wherein the component text includes the capacitor parameters recorded in a bill of materials; according to the component text, obtaining a capacitor type related to the component text; calculating a sum of scores corresponding to the capacitor type; wherein the sum of scores represents a correlation between the capacitor type and the component text; and determining a capacitor type corresponding to the component text according to a numerical value of the sum of scores.

[0005] In one embodiment, obtaining the capacitor type related to the component text according to the component text includes: performing feature recognition on the component text to obtain multiple feature words; performing attribute labeling on each of the feature words to obtain multiple feature words containing attributes; and obtaining the capacitor type according to the multiple feature words containing attributes.

[0006] In one embodiment, after obtaining the capacitor type according to the multiple feature words containing attributes, the capacitor classification and identification method further includes: selecting necessary features and secondary features from the multiple feature words containing attributes to obtain multiple necessary feature words and multiple secondary feature words.

[0007] In one embodiment, calculating the total score corresponding to the capacitor type includes: determining a first correlation between each of the necessary feature words and the capacitor type; determining a second correlation between each of the secondary feature words and the capacitor type; and calculating the total score corresponding to the capacitor type based on multiple first correlations and multiple second correlations.

[0008] In one embodiment, the step of performing attribute labeling on each of the feature words to obtain a plurality of feature words containing attributes includes: performing semantic analysis on each of the feature words to obtain a plurality of feature words containing attributes.

[0009] In one embodiment, the number of the capacitor types includes multiple types; wherein, determining the capacitor type corresponding to the component text based on the numerical value of the sum of the multiple scores includes: determining the capacitor type with the largest numerical value of the sum of the scores as the capacitor type corresponding to the component text.

[0010] In one embodiment, the feature recognition of the component text to obtain a plurality of feature words includes: parameter recognition of the component text to obtain a plurality of parameter feature words; wherein the parameter recognition includes recognition of symbols or symbol combinations of the component text; and / or keyword recognition of the component text to obtain a plurality of keywords; wherein the keyword recognition includes recognition of words or characters of the text in the bill of materials.

[0011] In one embodiment, the obtaining of the component text includes: extracting a text including the capacitor parameter from the bill of materials; and preprocessing the text to obtain the component text.

[0012] In one embodiment, the preprocessing of the text to obtain the component text includes: performing word segmentation processing on the text to obtain a word segmentation text; wherein the word segmentation text is structured data; and correcting the word segmentation text according to a preset vocabulary to obtain the component text.

[0013] In one embodiment, the preprocessing of the text to obtain the component text includes: encoding the text to obtain encoded text; converting full-width characters in the encoded text into half-width characters to obtain character text; and deleting semantically meaningless words in the character text to obtain the component text.

[0014] According to another aspect of the present application, a capacitor classification and identification device is provided, including: an acquisition module, used to acquire a component text; wherein the component text includes the capacitor parameters recorded in the bill of materials; a type acquisition module, used to acquire a capacitor type related to the component text according to the component text; a calculation module, used to calculate the sum of scores corresponding to the capacitor type; wherein the sum of scores represents the correlation between the capacitor type and the component text; and a determination module, used to determine the capacitor type corresponding to the component text according to the numerical value of the sum of scores.

[0015] According to another aspect of the present application, a computer-readable storage medium is provided, wherein the storage medium stores a computer program, and the computer program is used to execute any of the above-mentioned capacitance classification and identification methods.

[0016] According to another aspect of the present application, an electronic device is provided, comprising: a processor; a memory for storing instructions executable by the processor; and the processor for executing any of the above-mentioned capacitance classification and identification methods.

[0017] The capacitor classification and identification method, device, storage medium and electronic device provided by the present application include: obtaining component text, wherein the component text includes the capacitor parameters recorded in the bill of materials, obtaining the capacitor type related to the component text according to the component text, and calculating the sum of scores corresponding to the capacitor type; wherein the sum of scores represents the correlation between the capacitor type and the component text, and according to the numerical value of the sum of scores, determining the capacitor type corresponding to the component text. By obtaining the capacitor type related to the component text, calculating the sum of scores corresponding to the capacitor type, and determining the capacitor type corresponding to the component text according to the sum, this method can accurately determine the capacitor type corresponding to the component text, thereby improving the accuracy of recognition. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] By describing the embodiments of the present application in more detail in conjunction with the accompanying drawings, the above and other purposes, features and advantages of the present application will become more apparent. The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0019] Figure 1 It is a flowchart of a capacitance classification and identification method provided by an exemplary embodiment of the present application.

[0020] Figure 2 It is a schematic diagram of a flow chart of capacitor type acquisition provided by an exemplary embodiment of the present application.

[0021] Figure 3 It is a flowchart of a method for calculating the fractional sum corresponding to a capacitor type provided by an exemplary embodiment of the present application.

[0022] Figure 4 It is a flowchart of a capacitance classification and identification method provided by another exemplary embodiment of the present application.

[0023] Figure 5 It is a flowchart of a component text acquisition method provided by an exemplary embodiment of the present application.

[0024] Figure 6 It is a flowchart of a component text acquisition method provided by another exemplary embodiment of the present application.

[0025] Figure 7 It is a flowchart of a component text acquisition method provided by another exemplary embodiment of the present application.

[0026] Figure 8 It is a structural schematic diagram of a capacitance classification and identification device provided by an exemplary embodiment of the present application.

[0027] Fig. 9 It is a structural schematic diagram of a capacitance classification and identification device provided by another exemplary embodiment of the present application.

[0028] Fig.10 It is a structural diagram of an electronic device provided by an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0029] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described here.

[0030] Figure 1 FIG. 1 is a flow chart of a capacitance classification and identification method provided by an exemplary embodiment of the present application. Figure 1 As shown, the capacitance classification and identification method includes:

[0031] Step 110: Obtain component text, wherein the component text includes capacitor parameters recorded in the bill of materials.

[0032] The bill of materials is a BOM file (Bill of Material, a file that describes the product structure in a data format. It is a product structure data file that can be recognized by a computer and is also the dominant file of ERP). By identifying the capacitance parameters, it is determined which capacitor types correspond to the component text. The capacitor type can be one or more. For example, by 5 farads, it can be determined that the capacitor type may be an aluminum capacitor and / or a plug-in capacitor, etc. The component text is a text in the bill of materials, and the data in the component text can describe the capacitor structure or type. The component text may include a name, unit, value, etc.

[0033] Step 120: According to the component text, obtain the capacitor type related to the component text.

[0034] The capacitor type related to the component text is obtained through the component text. For example, through 5 volts, it can be determined that the capacitor type may be an aluminum capacitor and / or a plug-in capacitor, etc. A preset database or model can be set, in which the correspondence between the component text and the capacitor type is stored, and the component text can be input into the model to obtain the corresponding capacitor type. The related capacitor type can be one or more.

[0035] Step 130: Calculate the total score corresponding to the capacitor type, where the total score represents the correlation between the capacitor type and the component text.

[0036] If there are multiple capacitor types, calculate the sum of scores corresponding to each capacitor type for subsequent comparison. The sum of scores represents the correlation between the capacitor type and the component text. For example, the correlation between the plug-in capacitor and the component text is 10, and the correlation between the aluminum capacitor and the component text is 20, etc.

[0037] Step 140: Determine the capacitor type corresponding to the component text according to the numerical value of the sum of the scores.

[0038] Calculate the sum of the scores corresponding to each capacitor type, and determine the value to determine which capacitor type the component text belongs to. For example, the relevance between the plug-in capacitor and the component text is 10, and the relevance between the aluminum capacitor and the component text is 20, etc. Then the aluminum capacitor can be used as the capacitor type of the component text. For another example, if the sum of the scores between the plug-in capacitor and the component text is 30, it means that the plug-in capacitor is the capacitor type of the component text.

[0039] The capacitor classification and identification method provided by the present application includes: obtaining a component text, wherein the component text includes the capacitor parameters recorded in the bill of materials, obtaining the capacitor type related to the component text according to the component text, and calculating the sum of scores corresponding to the capacitor type; wherein the sum of scores represents the correlation between the capacitor type and the component text, and according to the numerical value of the sum of scores, determining the capacitor type corresponding to the component text. By obtaining the capacitor type related to the component text and calculating the sum of scores corresponding to the capacitor type, this method can accurately determine the capacitor type corresponding to the component text, thereby improving the accuracy of identification.

[0040] Figure 2 It is a schematic diagram of a flow chart of capacitor type acquisition provided by an exemplary embodiment of the present application.

[0041] like Figure 2 As shown, step 120 may include:

[0042] Step 121: Perform feature recognition on the component text to obtain multiple feature words.

[0043] By performing feature recognition on the component text, multiple feature words may be obtained. For example, the feature words may include 1F (farad), 50Hz (hertz), green, and short.

[0044] Step 122: Attribute labeling is performed on each feature word to obtain a plurality of feature words containing attributes.

[0045] Each feature word is labeled with attributes, so that it is clear what attribute the feature words identified belong to. For example, 1F (farad) may be a capacitance attribute, 50Hz (hertz) may be a frequency attribute, green may be a color attribute, and short may be a length attribute. All identified feature words are labeled with attributes, and the meaning of each feature word can be clearly understood from the attributes of each feature word.

[0046] Step 123: Obtain the capacitor type according to multiple feature words containing attributes.

[0047] Determine which capacitor types the feature words marked with attributes can correspond to in the preset database or model, and obtain the capacitor types related to the attributes. For example, if there are color attributes, capacitance attributes, and length attributes, electrolytic capacitors and plug-in capacitors can be obtained.

[0048] In one embodiment, step 122 may be specifically implemented as: selecting necessary features and minor features from a plurality of feature words containing attributes to obtain a plurality of necessary feature words and a plurality of minor feature words.

[0049] The feature words marked with attributes may belong to the necessary features, secondary features or exclusion features of the capacitor type. The exclusion feature means that the feature words marked with attributes are irrelevant to the capacitor type. Selecting multiple necessary feature words and multiple secondary feature words can improve the accuracy of identifying the capacitor type.

[0050] Figure 3 FIG. 1 is a flow chart of a method for calculating the fractional sum corresponding to a capacitor type provided by an exemplary embodiment of the present application. Figure 3 As shown, step 130 may include:

[0051] Step 131: Determine a first correlation between each necessary feature word and the capacitor type.

[0052] The first correlation between each necessary feature word and the capacitor type can be determined, and the first correlation can be a score value. For example, the necessary feature of aluminum capacitors is capacitor classification, and the first correlation between capacitor classification and aluminum capacitors is 20. The value of capacitor classification is a certain value, because in the component classification, the component text will first be confirmed to which component type it belongs to, where the component type includes resistor and capacitor classification, etc. The capacitor classification corresponds to a score sum, and the capacitor classification is a necessary feature word for identifying the capacitor type.

[0053] Step 132: Determine a second correlation between each secondary feature word and the capacitor type.

[0054] A second correlation of each secondary feature word capacitor type can be determined, and the second correlation can be a fractional value. And it is possible that the second correlation is lower than the first correlation. For example, the secondary feature of an aluminum capacitor is a parameter (the material is aluminum), and the second correlation between the parameter and the aluminum capacitor is 8.

[0055] Step 133: Calculate the sum of scores corresponding to the capacitor types according to the multiple first correlations and the multiple second correlations.

[0056] The similarity of multiple capacitor types may be very high. For example, the similarity of the necessary features and secondary features of SMD capacitors and plug-in capacitors is very high. They have many common features, and their differences lie in the difference in installation methods. Therefore, it is necessary to score each necessary feature and secondary feature to obtain the type with the highest correlation. The type with the highest score is the capacitor type of the component text. For example, the necessary feature of aluminum capacitors is capacitor classification, and the first correlation between capacitor classification and aluminum capacitors is 20. The secondary feature of aluminum capacitors is parameters (the material is aluminum), and the second correlation between parameters and aluminum capacitors is 8, so the total score is 28. If only one capacitor type is obtained, then the capacitor type is aluminum capacitor. If multiple capacitor types are obtained. For example, aluminum capacitors and SMD capacitors are obtained. The necessary feature of aluminum capacitors is capacitor classification, and the first correlation between capacitor classification and aluminum capacitors is 20. The secondary feature of aluminum capacitors is parameters (the material is aluminum), and the second correlation between parameters and aluminum capacitors is 8, so the total score is 28. The necessary feature of the chip capacitor is the capacitor classification. The first correlation between the capacitor classification and the chip point capacitor is 20. The secondary feature of the chip capacitor is the parameter (digital package). The second correlation between the parameter and the aluminum capacitor is 7. The total score is 27. 28 is greater than 27, indicating that the capacitor type of the component text is an aluminum capacitor.

[0057] In one embodiment, step 121 may be specifically implemented as: performing semantic analysis on each feature word to obtain a plurality of feature words containing attributes.

[0058] There are attributes corresponding to each word in the preset dictionary library, or you can select a dictionary at random and add some proper nouns corresponding to the component type in the dictionary, so you can perform semantic analysis on the words in the component text and get the attributes corresponding to the words. For example, 1F is the capacitance, and the capacitance is the attribute of the capacitor.

[0059] Figure 4 FIG. 1 is a flow chart of a capacitance classification and identification method provided by another exemplary embodiment of the present application. Figure 4 As shown, the number of capacitor types includes multiple types, and step 140 may include:

[0060] Step 141: Determine that the capacitor type with the largest total score value is the capacitor type corresponding to the component text.

[0061] Select the capacitor type with the largest total score as the capacitor type corresponding to the component text. The largest total score indicates that the attribute sum corresponding to this capacitor type is the largest. For example, the plug-in capacitor has two attributes, capacitance and brown. The score corresponding to capacitance is 6, and the score corresponding to brown is 2. The score corresponding to the chip capacitor is 8. The aluminum capacitor has three attributes, capacitance, red, and length. The score for capacitance is 5, the score for red is 4, and the score for length is 3. The score corresponding to the electrolytic capacitor is 12. 12 is greater than 8, indicating that the component type corresponding to the component text is an aluminum capacitor.

[0062] In one embodiment, step 121 can be specifically implemented as follows: performing parameter recognition on the component text to obtain multiple parameter feature words, wherein parameter recognition includes recognizing symbols or symbol combinations of the component text, and / or performing keyword recognition on the component text to obtain multiple keywords, wherein keyword recognition includes recognizing words or characters in the text in the bill of materials.

[0063] Parameter recognition is performed on component text, such as recognition of 3Ω (ohm) or 3F (farad). Or symbol recognition is performed, such as recognition of the number 474. 474 is the symbol of the capacitance value. Or keyword recognition is performed on component text, such as recognition of the word brown for capacitors, and the attribute corresponding to the feature is obtained as a color attribute. The best way is to perform parameter recognition and keyword recognition on component text.

[0064] Figure 5 FIG. 1 is a flow chart of a component text acquisition method provided by an exemplary embodiment of the present application. Figure 5 As shown, step 110 may include:

[0065] Step 111: Extract a text containing capacitor parameters from the BOM.

[0066] There are many texts in the bill of materials. Only by extracting a text containing capacitor parameters can the capacitor type corresponding to the text be identified. Or the text containing capacitor parameters and model can get the type of the text more accurately.

[0067] Step 112: Preprocess the text to obtain component text.

[0068] By preprocessing the text, some meaningless words, such as tabs, space symbols, or bullet points, can be deleted. Meaningless words mean that the words are useless for identifying the component text type and will interfere with the subsequent recognition of point capacitance.

[0069] Figure 6 FIG. 1 is a flow chart of a component text acquisition method provided by another exemplary embodiment of the present application. Figure 6 As shown, step 112 may include:

[0070] Step 1121: perform word segmentation processing on the text to obtain word segmentation text, wherein the word segmentation text is structured data.

[0071] The extracted text is an unstructured data, which means that the data in the text is messy and cannot express any meaning. Therefore, the text is segmented to obtain a segmented text, that is, a structured data, and the separated words represent their unique meanings. Unstructured data is essentially all data except structured data. Among them, unstructured data is data with irregular or incomplete data structure, no predefined data model, and it is not convenient to use a two-dimensional logical table in the database, including all formats of office documents, text, pictures, XML, HTML, various reports, images, audio / video information, etc. .

[0072] Step 1122: Modify the segmented text according to the preset vocabulary to obtain the component text.

[0073] The segmented text is corrected through the preset word library that has been set up by the system. For example, 1r can be corrected to 1Ω. Or some words after the segmentation process are typos, for example, grean needs to be corrected to green. After the words after the segmentation are corrected, the final component text is obtained.

[0074] Figure 7 FIG. 1 is a flow chart of a component text acquisition method provided by another exemplary embodiment of the present application. Figure 7 As shown, step 112 may include:

[0075] Step 1123: Convert the text to obtain encoded text.

[0076] The text can be converted to obtain encoded text, for example, text in gb2312 (Chinese character encoding character set for information exchange, GB2312 encoding is suitable for information exchange between systems such as Chinese character processing and Chinese character communication) format can be converted to utf8 format (utf8_encode, a standard mechanism for Unicode to convert wide character values ​​into byte streams).

[0077] Step 1124: Convert the full-width characters in the encoded text into half-width characters to obtain character text.

[0078] Convert the full-width characters in the encoded text into half-width characters to avoid the text taking up too much space. Full-width characters take up two bytes, and half-width characters take up one byte.

[0079] Step 1125: Delete meaningless words in the character text to obtain component text.

[0080] Meaningful text in the character text, such as numbers, words, and some component units, is retained, and meaningless text, such as emoticons, tabs, or invisible characters, is deleted, thereby obtaining a cleaned text of the character text, and the meaningless text is deleted to ensure the accuracy of determining the final component type.

[0081] Figure 8 is a schematic diagram of the structure of a capacitance classification and identification device provided by an exemplary embodiment of the present application. Figure 8 As shown, the capacitor classification and identification device 20 includes: an acquisition module 201, used to acquire component text; wherein the component text includes capacitor parameters recorded in the bill of materials, a type acquisition module 202, used to acquire the capacitor type related to the component text according to the component text, a calculation module 203, used to calculate the sum of scores corresponding to the capacitor type; wherein the sum of scores represents the correlation between the capacitor type and the component text, and a determination module 204, used to determine the capacitor type corresponding to the component text according to the numerical value of the sum of scores.

[0082] The capacitor classification and identification device provided by the present application obtains the component text through the acquisition module 201, wherein the component text includes the capacitor parameters recorded in the bill of materials, the type acquisition module 202 obtains the capacitor type related to the component text according to the component text, and the calculation module 203 calculates the sum of scores corresponding to the capacitor type; wherein the sum of scores represents the correlation between the capacitor type and the component text, and the determination module 204 determines the capacitor type corresponding to the component text according to the numerical value of the sum of scores. By obtaining the capacitor type related to the component text and calculating the sum of scores corresponding to the capacitor type, this method can accurately determine the capacitor type corresponding to the component text, thereby improving the accuracy of recognition.

[0083] Fig. 9 FIG. 1 is a schematic diagram of a capacitance classification and identification device provided by another exemplary embodiment of the present application. Fig. 9 As shown, the type acquisition module 202 may include:

[0084] The feature word acquisition unit 2021 is used to perform feature recognition on the component text to obtain multiple feature words; the attribute unit 2022 is used to perform attribute annotation on each feature word to obtain multiple feature words containing attributes; the capacitor type acquisition unit 2023 is used to obtain the capacitor type based on the multiple feature words containing attributes.

[0085] In one embodiment, if Fig. 9 As shown, the capacitance classification and identification device 20 can be specifically configured to: select necessary features and minor features from a plurality of feature words containing attributes to obtain a plurality of necessary feature words and a plurality of minor feature words.

[0086] In one embodiment, if Fig. 9 As shown, the calculation module 203 can be specifically configured to: determine the first correlation between each necessary feature word and the capacitor type; determine the second correlation between each secondary feature word and the capacitor type; and calculate the sum of the scores corresponding to the capacitor type based on multiple first correlations and multiple second correlations.

[0087] In one embodiment, the attribute acquisition unit 2021 may be specifically configured to: perform semantic analysis on each feature word to obtain a plurality of feature words containing attributes.

[0088] In one embodiment, if Fig. 9 As shown, the number of capacitor types includes multiple types, and the determination module 204 may include: a maximum score unit 2041, which is used to determine that the capacitor type with the largest total score value is the capacitor type corresponding to the component text.

[0089] In one embodiment, the attribute acquisition unit 2021 can be specifically configured to: perform parameter identification on the component text to obtain multiple parameter attributes, wherein parameter identification includes identifying symbols or symbol combinations of the component text, and / or perform keyword identification on the component text to obtain multiple keyword attributes, wherein keyword identification includes word or character recognition of the text in the bill of materials.

[0090] In one embodiment, if Fig. 9 As shown, the acquisition module 201 may include:

[0091] The extraction unit 2011 is used to extract a text containing component parameters from the bill of materials; the processing unit 2012 is used to pre-process the text to obtain the component text.

[0092] In one embodiment, the processing unit 2012 may be specifically configured to: perform word segmentation processing on the text to obtain a word segmentation text, wherein the word segmentation text is structured data, and modify the word segmentation text according to a preset word library to obtain a component text.

[0093] In one embodiment, the processing unit 2012 can be specifically configured to: perform encoding conversion on the text to obtain encoded text, convert the full-width characters in the encoded text into half-width characters to obtain character text, and delete semantically meaningless words in the character text to obtain component text.

[0094] Below, reference Fig.10 The electronic device according to the embodiment of the present application is described. The electronic device may be any one or both of the first device and the second device, or a stand-alone device independent of them, and the stand-alone device may communicate with the first device and the second device to receive the collected input signal from them.

[0095] Fig.10 A block diagram of an electronic device according to an embodiment of the present application is illustrated.

[0096] like Fig.10 As shown, the electronic device 10 includes one or more processors 11 and a memory 12 .

[0097] The processor 11 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 10 to perform desired functions.

[0098] The memory 12 may include one or more computer program products, and the computer program product may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 11 may run the program instructions to implement the capacitance classification identification method of each embodiment of the present application described above and / or other desired functions. Various contents such as input signals, signal components, noise components, etc. may also be stored in the computer-readable storage medium.

[0099] In one example, the electronic device 10 may further include: an input device 13 and an output device 14, and these components are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0100] When the electronic device is a stand-alone device, the input device 13 may be a communication network connector, which is used to receive the collected input signals from the first device and the second device.

[0101] In addition, the input device 13 may also include, for example, a keyboard, a mouse, and the like.

[0102] The output device 14 can output various information to the outside, including the determined distance information, direction information, etc. The output device 14 can include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto, and the like.

[0103] Of course, to simplify, Fig.10 Only some of the components related to the present application in the electronic device 10 are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, according to specific application situations, the electronic device 10 may also include any other appropriate components.

[0104] The computer program product may be written in any combination of one or more programming languages ​​to write program codes for performing the operations of the embodiments of the present application, including object-oriented programming languages, such as Java, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as an independent software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0105] The computer readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0106] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present application to the forms disclosed herein. Although multiple example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. A capacitance classification and identification method, characterized in that: include: Obtaining component text; wherein the component text includes the capacitance parameters recorded in the bill of materials; According to the component text, obtaining a capacitor type related to the component text; Calculating a sum of scores corresponding to the capacitor type; wherein the sum of scores represents the relevance between the capacitor type and the component text; and Determining the capacitor type corresponding to the component text according to the numerical value of the sum of the scores; The acquiring, according to the component text, a capacitor type related to the component text comprises: Performing feature recognition on the component text to obtain a plurality of feature words; Performing attribute labeling on each of the feature words to obtain a plurality of feature words with attributes; and Acquire the capacitor type according to the plurality of feature words containing attributes; After acquiring the capacitor type according to the plurality of feature words containing attributes, the method further includes: Selecting necessary features and minor features from the plurality of feature words containing attributes to obtain a plurality of necessary feature words and a plurality of minor feature words; Calculating the sum of scores corresponding to the capacitor type includes: Determining a first correlation between each of the necessary feature words and the capacitor type; Determining a second correlation between each of the secondary feature words and the capacitor type; and Calculating a sum of scores corresponding to the capacitor type according to the plurality of the first correlations and the plurality of the second correlations; The number of the capacitor types includes multiple types; wherein determining the capacitor type corresponding to the component text according to the value of the sum of the scores includes: It is determined that the capacitor type with the largest sum of the scores is the capacitor type corresponding to the component text.

2. The capacitance classification and identification method according to claim 1, characterized in that: The step of labeling each of the feature words with attributes to obtain a plurality of feature words with attributes includes: Semantic analysis is performed on each of the feature words to obtain a plurality of feature words containing attributes.

3. The capacitance classification and identification method according to claim 1, characterized in that: The performing feature recognition on the component text to obtain a plurality of feature words includes: Performing parameter recognition on the component text to obtain a plurality of parameter feature words; wherein the parameter recognition includes recognizing symbols or symbol combinations of the component text; and / or Keyword recognition is performed on the component text to obtain a plurality of keywords; wherein the keyword recognition includes word or character recognition of the text in the bill of materials.

4. The capacitance classification and identification method according to claim 1, characterized in that: The obtaining of component text includes: extracting a text containing the capacitor parameters from the bill of materials; and The text is preprocessed to obtain the component text.

5. The capacitance classification and identification method according to claim 4, characterized in that: The preprocessing of the text to obtain the component text includes: Performing word segmentation processing on the text to obtain a word segmentation text; wherein the word segmentation text is structured data; and The segmented text is modified according to a preset word library to obtain the component text.

6. The capacitance classification and identification method according to claim 4, characterized in that: The preprocessing of the text to obtain the component text includes: Performing encoding conversion on the text to obtain an encoded text; Converting the full-width characters in the coded text into half-width characters to obtain character text; and The semantically meaningless words in the character text are deleted to obtain the component text.

7. A capacitance classification and identification device, characterized in that: include: An acquisition module, used for acquiring component text; wherein the component text includes the capacitance parameters recorded in the bill of materials; A type acquisition module, used for acquiring a capacitor type related to the component text according to the component text; A calculation module, used to calculate the sum of scores corresponding to the capacitor type; wherein the sum of scores represents the correlation between the capacitor type and the component text; and A determination module, used to determine the capacitor type corresponding to the component text according to the numerical value of the sum of the scores; The acquiring, according to the component text, a capacitor type related to the component text comprises: Performing feature recognition on the component text to obtain a plurality of feature words; Performing attribute labeling on each of the feature words to obtain a plurality of feature words with attributes; and Acquire the capacitor type according to the plurality of feature words containing attributes; After acquiring the capacitor type according to the plurality of feature words containing attributes, the method further includes: Selecting necessary features and minor features from the plurality of feature words containing attributes to obtain a plurality of necessary feature words and a plurality of minor feature words; Calculating the sum of scores corresponding to the capacitor type includes: Determining a first correlation between each of the necessary feature words and the capacitor type; Determining a second correlation between each of the secondary feature words and the capacitor type; and Calculating a sum of scores corresponding to the capacitor type according to the plurality of the first correlations and the plurality of the second correlations; The number of the capacitor types includes multiple types; wherein determining the capacitor type corresponding to the component text according to the value of the sum of the scores includes: It is determined that the capacitor type with the largest sum of the scores is the capacitor type corresponding to the component text.

8. A computer-readable storage medium storing a computer program, wherein the computer program is used to execute the capacitance classification and identification method according to any one of claims 1 to 6.

9. An electronic device, comprising: processor; a memory for storing instructions executable by the processor; The processor is used to execute the capacitance classification and identification method described in any one of claims 1 to 6.

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