Electronic component parameter management method and system based on artificial intelligence, and terminal

Abstract

The invention provides an electronic component parameter management method and system based on artificial intelligence, and a terminal. The method comprises the following steps: acquiring an input file to be identified; performing positioning extraction on key original parameter contents in the to-be-identified file based on a preset extraction strategy to obtain original electronic component parameter data; performing structured processing on the original electronic component parameter data to obtain intermediate electronic component parameter data with a uniform format; performing parameter identification and parameter conversion on all the intermediate electronic component parameter data based on an artificial intelligence model to obtain standardized target electronic component parameter data; and carrying out validity verification on the target electronic component parameter data, and storing the target electronic component parameter data which is verified to be valid in a corresponding position in an electronic component database. According to the invention, the electronic component parameters can be identified and classified efficiently and accurately.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic component parameter management, and in particular to an electronic component parameter management method, system and terminal based on artificial intelligence. Background Art

[0002] With the rapid development of the electronics industry, the types and quantity of electronic components have increased dramatically, and their technical parameters have become increasingly complex. In the process of electronic component management and archiving, how to efficiently and accurately identify and classify component parameters has become an important issue in the industry. At present, the identification and classification of electronic component parameters mainly rely on manual operation and traditional computer programs, but there are many problems. First, the file format is diversified, such as text-based format and image format. For text-based specifications, although parameters can be extracted through traditional text parsing programs, the parsing effect is often poor in the face of complex formats and structures. For image-based specifications, although traditional direct OCR (optical character recognition) processing can recognize some text information, the recognition accuracy is low, especially in the case of poor image clarity, the recognition effect is greatly reduced. Second, the parameter description is not standardized. Specifications from different manufacturers and languages ​​have large differences in the description of the same parameter, and even use synonymous expressions, which brings difficulties to automatic identification and classification. Traditional computer programs are difficult to cope with this diversity and complexity, and often require manual intervention for error correction and matching. Third, the parameter units are not unified. There are also multiple unit systems in the same industry, which makes it difficult to achieve standardization. Fourth, manual operation is inefficient. Existing software tools require a lot of upfront configuration and lack support for non-standard terminology, which leads to frequent manual intervention and prone to errors, affecting the accuracy and completeness of component database information. Summary of the invention

[0003] The main purpose of the present invention is to provide an electronic component parameter management method, system, terminal and computer-readable storage medium based on artificial intelligence, aiming to solve the technical problem of how to efficiently and accurately identify and classify electronic component parameters.

[0004] In a first aspect, the present invention provides an electronic component parameter management method based on artificial intelligence, comprising:

[0005] Get the input file to be recognized;

[0006] Based on a preset extraction strategy, key original parameter content in the file to be identified is located and extracted to obtain original electronic component parameter data;

[0007] Performing structured processing on the original electronic component parameter data to obtain intermediate electronic component parameter data in a unified format;

[0008] Perform parameter identification and parameter conversion on all the intermediate electronic component parameter data based on the artificial intelligence model to obtain standardized target electronic component parameter data;

[0009] The validity of the target electronic component parameter data is verified, and the verified valid target electronic component parameter data is stored in a corresponding position in the electronic component database.

[0010] Furthermore, before locating and extracting the key original parameter content in the to-be-identified file based on the preset extraction strategy to obtain the original electronic component parameter data, the method further includes:

[0011] Obtaining an input file to be identified, and automatically determining whether a current format of the file to be identified is a target format according to a suffix of the file to be identified;

[0012] If not, the current format of the file to be identified is converted into the target format.

[0013] Furthermore, the method of locating and extracting the key original parameter content in the to-be-identified file based on a preset extraction strategy to obtain the original electronic component parameter data includes:

[0014] Determine whether the content of the file to be identified contains text content and non-text content;

[0015] If so, locate the key original parameter content in the text content based on the text parsing algorithm and extract the parameters to obtain the original electronic component parameter data;

[0016] If not, the key original parameter content in the non-text content is located based on a graphic feature recognition algorithm and parameter extraction is performed to obtain original electronic component parameter data.

[0017] Furthermore, the method of locating key original parameter content in the non-text content based on the graphic feature recognition algorithm and extracting parameters to obtain original electronic component parameter data includes:

[0018] Locating key original parameter content in the non-text content based on a graphic feature recognition algorithm;

[0019] Based on an artificial intelligence recognition algorithm or an OCR recognition algorithm, parameter extraction is performed on the key original parameter content to obtain original electronic component parameter data.

[0020] Furthermore, before performing parameter identification and parameter conversion on all the intermediate electronic component parameter data based on the artificial intelligence model to obtain standardized target electronic component parameter data, the method further includes:

[0021] Based on the artificial intelligence model, erroneous characters in the original electronic component parameter data are detected and automatically corrected.

[0022] Furthermore, the artificial intelligence model is used to perform parameter identification and parameter conversion on all the intermediate electronic component parameter data to obtain standardized target electronic component parameter data, including:

[0023] Perform parameter recognition on all the intermediate electronic component parameter data based on the artificial intelligence model to find standardized parameter names corresponding to the intermediate electronic component parameter data;

[0024] Parameter conversion is performed on the intermediate electronic component parameter data to obtain standardized target electronic component parameter data; wherein the target electronic component parameter data includes standardized parameter names, parameter values ​​and numerical units.

[0025] In a second aspect, the present invention provides an electronic component parameter management system based on artificial intelligence, including an original parameter data extraction subsystem and a parameter identification and classification subsystem;

[0026] The original parameter data extraction subsystem is used to:

[0027] Get the input file to be recognized;

[0028] Based on a preset extraction strategy, key original parameter content in the file to be identified is located and extracted to obtain original electronic component parameter data;

[0029] Performing structured processing on the original electronic component parameter data to obtain intermediate electronic component parameter data in a unified format;

[0030] The parameter identification and classification subsystem is used to:

[0031] Perform parameter identification and parameter conversion on all the intermediate electronic component parameter data based on the artificial intelligence model to obtain standardized target electronic component parameter data;

[0032] The validity of the target electronic component parameter data is verified, and the verified valid target electronic component parameter data is stored in a corresponding position in the electronic component database.

[0033] Further, the parameter identification and classification subsystem includes an electronic component database and the artificial intelligence model;

[0034] Wherein, the electronic component database is established in the following manner:

[0035] Based on various standard information, establish an electronic component database containing standardized parameter names and set standardized numerical units;

[0036] The artificial intelligence model is prepared in the following manner:

[0037] During the pre-training phase of the artificial intelligence model, input various types of standard data information;

[0038] Customizing the artificial intelligence model so that the content output by the artificial intelligence model is limited to the existing fields in the electronic component database and can perform the functions of error character detection and correction, parameter identification and parameter conversion;

[0039] The output results of the artificial intelligence model are verified for validity to obtain a trained artificial intelligence model.

[0040] In a third aspect, the present invention provides a terminal comprising: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements an electronic component parameter management method based on artificial intelligence as described in the first aspect.

[0041] In a fourth aspect, the present invention provides a computer-readable storage medium, which stores a computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute an artificial intelligence-based electronic component parameter management method as described in the first aspect.

[0042] Compared with the prior art, the beneficial effects of the present invention are:

[0043] (1) The present invention locates and extracts the key original parameter content in the identification file based on a preset extraction strategy, and can quickly, accurately, and batch extract key parameter information from specifications in different formats, greatly improving the efficiency of data processing. Compared with the traditional manual extraction, analysis, and entry method, the present invention greatly shortens the required time.

[0044] (2) When the input parameters are inconsistent with the standards in the electronic component database, the present invention performs parameter identification and parameter conversion on all intermediate electronic component parameter data based on the artificial intelligence model to obtain standardized target electronic component parameter data, that is, automatic parameter concept conversion and unit conversion are performed through the artificial intelligence model to ensure that all parameters can be stored in a standard form. This intelligent processing reduces the need for manual intervention and improves the flexibility and reliability of data processing.

[0045] (3) The present invention has a flexible and diverse error handling mechanism, which can verify the validity of the target electronic component parameter data output by the artificial intelligence model and require the artificial intelligence model to re-analyze and reason when necessary. This mechanism improves the accuracy and completeness of the result data and reduces data quality problems caused by inherent shortcomings such as artificial hallucinations or technical errors in other links. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying creative work.

[0047] Figure 1 It is a flowchart of an electronic component parameter management method based on artificial intelligence provided by an embodiment of the present invention;

[0048] Figure 2 It is a structural diagram of an electronic component parameter management system based on artificial intelligence provided by an embodiment of the present invention;

[0049] Figure 3 It is a schematic diagram of the process of obtaining a parameter identification and classification subsystem provided by an embodiment of the present invention;

[0050] Figure 4 It is a schematic diagram of a flow chart of an original parameter data extraction subsystem performing an original parameter data extraction step provided by an embodiment of the present invention;

[0051] Figure 5 It is a flow chart of the parameter identification and classification subsystem according to one embodiment of the present invention performing the parameter identification and classification steps;

[0052] Figure 6 It is a schematic diagram of the structure of a terminal provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0053] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0054] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first" and "second" are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0055] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection, a direct connection, or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0056] In the present invention, unless otherwise clearly specified and limited, a first feature being "above" or "below" a second feature may include that the first and second features are in direct contact, or may include that the first and second features are not in direct contact but are in contact through another feature between them. Moreover, a first feature being "above", "above" and "above" a second feature includes that the first feature is directly above and obliquely above the second feature, or simply indicates that the first feature is higher in level than the second feature. A first feature being "below", "below" and "below" a second feature includes that the first feature is directly below and obliquely below the second feature, or simply indicates that the first feature is lower in level than the second feature.

[0057] See also Figure 1 , Figure 1 It is a flowchart of an electronic component parameter management method based on artificial intelligence provided by an embodiment of the present invention.

[0058] An electronic component parameter management method based on artificial intelligence according to an embodiment of the present invention comprises the following steps:

[0059] S100, obtaining an input file to be identified;

[0060] S200, locating and extracting key original parameter content in the to-be-identified file based on a preset extraction strategy to obtain original electronic component parameter data;

[0061] S300, performing structured processing on the original electronic component parameter data to obtain intermediate electronic component parameter data in a unified format;

[0062] S400, performing parameter identification and parameter conversion on all the intermediate electronic component parameter data based on an artificial intelligence model to obtain standardized target electronic component parameter data;

[0063] S500: Verify the validity of the target electronic component parameter data, and store the verified valid target electronic component parameter data in a corresponding position in an electronic component database.

[0064] In this embodiment, the processing of the electronic component parameters in the file to be identified is mainly divided into two parts, one is the original parameter data extraction step, and the other is the parameter identification and classification step.

[0065] An electronic component parameter management method based on artificial intelligence according to an embodiment of the present invention can be applied to an electronic component parameter management system based on artificial intelligence provided by an embodiment of the present invention. Figure 2 , Figure 2 It is a structural diagram of an electronic component parameter management system based on artificial intelligence provided by one embodiment of the present invention.

[0066] An electronic component parameter management system based on artificial intelligence according to an embodiment of the present invention comprises an original parameter data extraction subsystem and a parameter identification and classification subsystem;

[0067] The original parameter data extraction subsystem is used to:

[0068] Get the input file to be recognized;

[0069] Based on a preset extraction strategy, key original parameter content in the file to be identified is located and extracted to obtain original electronic component parameter data;

[0070] Performing structured processing on the original electronic component parameter data to obtain intermediate electronic component parameter data in a unified format;

[0071] The parameter identification and classification subsystem is used to:

[0072] Perform parameter identification and parameter conversion on all the intermediate electronic component parameter data based on the artificial intelligence model to obtain standardized target electronic component parameter data;

[0073] The validity of the target electronic component parameter data is verified, and the verified valid target electronic component parameter data is stored in a corresponding position in the electronic component database.

[0074] In this example, see Figure 2 The electronic component parameter management system based on artificial intelligence consists of two major subsystems: an original parameter data extraction subsystem and a parameter identification and classification subsystem. The original parameter data extraction subsystem can execute steps S100 to S300, i.e., execute the original parameter data extraction step, and the parameter identification and classification subsystem can execute steps S400 to S500, i.e., execute the parameter identification and classification step. The intermediate electronic component parameter data output by the original parameter data extraction subsystem can be used as the input of the parameter identification and classification subsystem.

[0075] In a specific embodiment, the original parameter data extraction subsystem is directly programmed by professionals so that the original parameter data extraction subsystem can execute steps S100 to S300.

[0076] See also Figure 3 , in a specific embodiment, the parameter identification and classification subsystem includes an electronic component database and the artificial intelligence model;

[0077] Wherein, the electronic component database is established in the following manner:

[0078] Based on various standard information, establish an electronic component database containing standardized parameter names and set standardized numerical units;

[0079] The artificial intelligence model is prepared in the following manner:

[0080] During the pre-training phase of the artificial intelligence model, input various types of standard data information;

[0081] Customizing the artificial intelligence model so that the content output by the artificial intelligence model is limited to the existing fields in the electronic component database and can perform the functions of error character detection and correction, parameter identification and parameter conversion;

[0082] The output results of the artificial intelligence model are verified for validity to obtain a trained artificial intelligence model.

[0083] In this example, see Figure 3When establishing an electronic component database, the standardized parameter names contained in the national standards such as "Electrical Engineering Terminology" are used as attribute field names, table names or index information to establish the electronic component database, and one or more numerical units are selected as standardized units for storage in the electronic component database according to the relevant standard requirements. The above-mentioned bibliographic and standard documents are not limited, and the above-mentioned book names are only examples. The electronic component database can also be established based on other standard information.

[0084] See also Figure 3 , in the artificial intelligence model (such as Figure 3 , Figure 4 and Figure 5 When training the AI ​​tools shown in the figure, such as the large language model, the AI ​​model is pre-trained first. At this stage, relevant information such as national standards and industry specifications such as "Electrical Engineering Terminology" are entered. At the same time, relevant standard description documents from other countries, regions, and languages ​​are also entered as training materials to train the basic AI model and ensure that the AI ​​model has a basic professional knowledge background.

[0085] Then, the AI ​​model is customized by fine-tuning it, adding prompt words and user examples, etc., stipulating that its output content must be limited to the existing fields in the electronic component database, and guiding the AI ​​model to give the closest synonymous field when there are some errors in the input characters or there are no completely identical fields. When there are indeed no similar fields, the AI ​​model is instructed to try to convert parameters or output a judgment conclusion that there is no matching field.

[0086] In addition, professionals write supporting auxiliary programs to control data flow and verify the validity of the output results of the artificial intelligence model. For example, check whether the field to which the parameter belongs exists in the electronic component database and whether the converted parameter value is within a reasonable range. If the output is judged to be invalid, the auxiliary program will require the artificial intelligence model to re-analyze the reasoning. If the output is confirmed to be valid, the parameter value is automatically stored in the corresponding row and field of the electronic component database, or the parameter value that does not need to be stored in the electronic component database is discarded according to the judgment of the artificial intelligence model. The trained artificial intelligence model is integrated in the above way.

[0087] In this way, by using the standardized electronic component database established by national or industry standard terms such as "Electrical Engineering Terminology", the parameter identification and classification subsystem can achieve parameter unification and standardization, ensuring that all extracted parameters are stored according to unified standards. This not only facilitates subsequent data retrieval and analysis, but also ensures the consistency and comparability of data from different sources.

[0088] In addition, the parameter identification and classification subsystem also has a flexible and diverse error handling mechanism, and the supporting auxiliary program has a self-checking function, which can detect the validity of the output results of the artificial intelligence model and require the artificial intelligence model to re-analyze and reason when necessary. This mechanism improves the accuracy and completeness of the result data and reduces data quality problems caused by inherent shortcomings such as artificial hallucinations or technical errors in other links.

[0089] In a specific embodiment, before the key original parameter content of the to-be-identified file is located and extracted based on a preset extraction strategy in step S200 to obtain the original electronic component parameter data, the method further includes the following steps:

[0090] S600, obtaining an input file to be identified, and automatically determining whether a current format of the file to be identified is a target format according to a suffix of the file to be identified;

[0091] S700: If not, convert the current format of the file to be identified into the target format.

[0092] See also Figure 4 In this embodiment, the file type judgment program of the original parameter data extraction subsystem automatically judges whether the format of the file to be identified is the target format according to the suffix name of the input file, wherein the target format is a common file format, such as XML, PDF format, etc. It can be understood that the target format can also be other formats, which are not specifically limited here. If the above recognition is unsuccessful, try to read the file header of the file to be identified in binary file mode, and re-judge the file type according to the file header features. For some file formats that are not convenient to be directly processed by the program, the file will be converted into other common file formats by the format conversion program before processing. Then determine the subsequent processing strategy according to the file format type.

[0093] In a specific embodiment, the step S200 locates and extracts the key original parameter content in the file to be identified based on a preset extraction strategy to obtain the original electronic component parameter data, including the following steps:

[0094] S210, determining whether the content of the to-be-identified file contains text content and non-text content;

[0095] S220: If yes, locate the key original parameter content in the text content based on the text parsing algorithm and extract the parameters to obtain the original electronic component parameter data;

[0096] S230: If not, locate the key original parameter content in the non-text content based on the graphic feature recognition algorithm and perform parameter extraction to obtain original electronic component parameter data.

[0097] See also Figure 4 In this embodiment, the original parameter data extraction subsystem adopts a content extraction strategy judgment algorithm (such as Figure 4 The content extraction strategy judgment program shown in the figure) judges whether the content of the file to be identified contains text content and non-text content, wherein the non-text content includes content in image format.

[0098] For specifications that can be directly read in text, the original parameter data extraction subsystem uses a text parsing algorithm (such as Figure 4 The text parsing and judgment program shown in the figure) locates the key original parameter description paragraph or table area through regular expressions, keyword search and other technologies, and directly copies the relevant text content.

[0099] If the specification is in image format or has a complex layout, the original parameter data extraction subsystem uses graphic feature recognition algorithms, such as edge detection and contour recognition, to locate the table or area containing the technical parameters, and then performs key original parameter content recognition and extraction processing.

[0100] The purpose of this embodiment is to minimize the scope of the focus area and reduce the amount of subsequent data processing. However, if the relevant detection and judgment encounter obstacles, a larger focus area can be selected to ensure that key information such as parameter name, parameter value, and numerical unit is included.

[0101] In a specific embodiment, the step S230 locates the key original parameter content in the non-text content based on the graphic feature recognition algorithm and extracts the parameters to obtain the original electronic component parameter data, including the following steps:

[0102] S231, locating key original parameter content in the non-text content based on a graphic feature recognition algorithm;

[0103] S232: Extract parameters of the key original parameter content based on an artificial intelligence recognition algorithm or an OCR recognition algorithm to obtain original electronic component parameter data.

[0104] See also Figure 4 In this embodiment, the key original parameter content in the non-text content is first located by a graphic feature recognition algorithm, and then OCR recognition or artificial intelligence recognition processing is performed to obtain the original electronic component parameter data.

[0105] In another embodiment, some or all functions in the original parameter data extraction subsystem may be replaced manually, such as:

[0106] a) Manually locate the parameter area, then use the program to perform text parsing or OCR recognition, and then input it into the parameter recognition and classification subsystem;

[0107] b) Locate the parameter area through the program, then manually extract the original content of the parameter, and then input it into the parameter identification and classification subsystem;

[0108] c) Manually read the specification sheet and then input it directly into the parameter identification and classification subsystem.

[0109] In another embodiment, some or all functions in the original parameter data extraction subsystem may be directly skipped, such as:

[0110] a) No format judgment or format conversion is performed, requiring only files of a specific format or plain text to be input;

[0111] b) Does not perform content extraction strategy judgment, and only adapts to text or image specifications;

[0112] c) Without narrowing the area of ​​interest, the full text content is directly input into the parameter identification and classification subsystem.

[0113] Please continue reading Figure 4 In the step S300, the original electronic component parameter data is structured to obtain intermediate electronic component parameter data in a unified format. The original electronic component parameter data extracted by the original parameter data extraction subsystem will be structured to form a unified parameter data format, such as a key-value pair or a table format, and will carry relevant information such as original file information and processing strategies to obtain intermediate electronic component parameter data to assist the subsequent parameter identification and classification subsystem in analysis and judgment processing.

[0114] In a specific embodiment, before performing parameter identification and parameter conversion on all the intermediate electronic component parameter data based on the artificial intelligence model to obtain standardized target electronic component parameter data in step S400, the method further includes the following steps:

[0115] S800: Detect and automatically correct erroneous characters on the original electronic component parameter data based on the artificial intelligence model.

[0116] In this embodiment, through the control of the auxiliary program, the execution result data of the original parameter data extraction subsystem (such as the intermediate electronic component parameter data) flows to the parameter identification and classification subsystem, which regards this data as input and starts to execute steps S800, S400~S500.

[0117] See also Figure 5 ,The artificial intelligence model (AI tool) in the parameter recognition and classification subsystem analyzes and automatically corrects the erroneous characters of the input original electronic component parameter data (such as parameter names and numerical units) to improve the accuracy of subsequent recognition.

[0118] In a specific embodiment, step S400 performs parameter identification and parameter conversion on all the intermediate electronic component parameter data based on an artificial intelligence model to obtain standardized target electronic component parameter data, including the following steps:

[0119] S410. Perform parameter identification on all the intermediate electronic component parameter data based on the artificial intelligence model to find the standardized parameter names corresponding to the intermediate electronic component parameter data;

[0120] S420. Perform parameter conversion on the intermediate electronic component parameter data to obtain the standardized target electronic component parameter data; wherein, the target electronic component parameter data includes the standardized parameter names, parameter values, and numerical units.

[0121] Please refer to Figure 5 , in this embodiment, the artificial intelligence model (AI tool) in the parameter identification and classification subsystem first finds the same or the closest standardized parameter names according to all the input intermediate electronic component parameter data (such as parameter names and numerical units). Then it determines whether the intermediate electronic component parameter data needs to be parameter-converted. If necessary, parameter concept conversion or unit conversion is performed, and finally, the target electronic component parameter data corresponding to all the parameters involved in the input content is generated, such as the standardized parameter names, parameter values, and numerical units. In addition, for the intermediate electronic component parameter data that cannot be recognized and the retry exceeds the preset number of times, it will be recorded in the error log, and subsequent manual intervention can be prompted.

[0122] Please refer to Figure 5 , in step S500, the validity of the target electronic component parameter data is verified, and the verified valid target electronic component parameter data is stored in the corresponding position in the electronic component database. The auxiliary program in the parameter identification and classification subsystem will automatically verify whether the output result of the artificial intelligence model (AI tool) is valid, including checking whether there is a corresponding field for the parameter name in the electronic component database, and whether the converted parameter value is within a reasonable range.

[0123] If an invalid output is found, the artificial intelligence model is required to re-analyze and reason.

[0124] After confirming that the output is valid, the auxiliary program automatically stores the parameter value under the corresponding row and field in the database. For the parameter values determined by the artificial intelligence model not to be stored in the database, the relevant results are discarded.

[0125] If there are multiple target electronic component parameter data to be processed, the above steps are repeated until all the target electronic component parameter data are correctly processed.

[0126] In summary, compared with the prior art, the electronic component parameter management method and system based on artificial intelligence provided by the embodiment of the present invention has the following beneficial effects:

[0127] (1) The present invention locates and extracts the key original parameter content in the identification file based on a preset extraction strategy, and can quickly, accurately, and batch extract key parameter information from specifications in different formats, greatly improving the efficiency of data processing. Compared with the traditional manual extraction, analysis, and entry method, the present invention greatly shortens the required time.

[0128] (2) When the input parameters are inconsistent with the standards in the electronic component database, the present invention performs parameter identification and parameter conversion on all intermediate electronic component parameter data based on the artificial intelligence model to obtain standardized target electronic component parameter data, that is, automatic parameter concept conversion and unit conversion are performed through the artificial intelligence model to ensure that all parameters can be stored in a standard form. This intelligent processing reduces the need for manual intervention and improves the flexibility and reliability of data processing.

[0129] (3) The present invention has a flexible and diverse error handling mechanism, which can verify the validity of the target electronic component parameter data output by the artificial intelligence model and require the artificial intelligence model to re-analyze and reason when necessary. This mechanism improves the accuracy and completeness of the result data and reduces data quality problems caused by inherent shortcomings such as artificial hallucinations or technical errors in other links.

[0130] (4) The present invention performs erroneous character detection and automatic correction on the original electronic component parameter data based on an artificial intelligence model, and utilizes advanced artificial intelligence algorithms to jointly analyze the connection and context between the input parameter name, parameter value and numerical unit. The present invention can automatically correct character errors that may be introduced by OCR during the recognition process, thereby improving the accuracy of parameter recognition.

[0131] (5) The present invention uses the standardized electronic component database established by national or industry standard terms such as "Electrical Engineering Terminology" to ensure that all extracted parameters are stored according to unified standards. This not only facilitates subsequent data retrieval and analysis, but also ensures the consistency and comparability of data from different sources.

[0132] (6) The present invention supports multi-language parameter recognition and can process specification documents in different language environments, automatically recognize and convert them into standard parameter names and units. This multi-language compatibility facilitates global supply chain management, helps break down language barriers, and promotes international technical exchanges and cooperation.

[0133] See also Figure 6 , Figure 6It is a schematic structural diagram of a terminal provided by an embodiment of the present invention. The terminal includes:

[0134] A processor 100, a memory 200, and a computer program stored in the memory 200 and configured to be executed by the processor 100. When the processor 100 executes the computer program, it implements a method for managing electronic component parameters based on artificial intelligence as described in each of the above embodiments.

[0135] The processor 100 can be implemented in ways such as a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided by the embodiments of the present invention;

[0136] The memory 200 can be implemented in forms such as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 200 can store an operating system and other application programs. When implementing the technical solutions provided by the embodiments of this specification through software or firmware, the relevant program codes are stored in the memory 200 and are called by the processor 100 to execute a method for managing electronic component parameters based on artificial intelligence in the embodiments of the present invention;

[0137] An input / output interface 300 for implementing information input and output;

[0138] A communication interface 400 for implementing communication interaction between this device and other devices, which can be implemented through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.);

[0139] A bus 500 for transmitting information between various components of the device (such as the processor 100, the memory 200, the input / output interface 300, and the communication interface 400);

[0140] Among them, the processor 100, the memory 200, the input / output interface 300, and the communication interface 400 are communicatively connected to each other inside the device through the bus 500.

[0141] An embodiment of the present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is running, the device where the computer-readable storage medium is located is controlled to execute an electronic component parameter management method based on artificial intelligence in the above-mentioned embodiments.

[0142] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0143] The embodiments described in the embodiments of the present invention are intended to more clearly illustrate the technical solutions of the embodiments of the present invention, and do not constitute a limitation on the technical solutions provided by the embodiments of the present invention. Those skilled in the art can appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of the present invention are also applicable to similar technical problems.

[0144] Those skilled in the art will appreciate that the technical solutions shown in the figures do not limit the embodiments of the present invention and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0145] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0146] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0147] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0148] It should be understood that in the present invention, "at least one (item)" refers to one or more, and "plurality" refers to two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can represent: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0149] In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0150] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0151] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0152] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including multiple instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store programs.

[0153] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the embodiments of the present invention is not limited thereby. Any modification, equivalent substitution and improvement made by those skilled in the art without departing from the scope and essence of the embodiments of the present invention shall be within the scope of the rights of the embodiments of the present invention.

Claims

1. An electronic component parameter management method based on artificial intelligence, characterized in that: include: Get the input file to be recognized; Based on a preset extraction strategy, key original parameter content in the file to be identified is located and extracted to obtain original electronic component parameter data; Performing structured processing on the original electronic component parameter data to obtain intermediate electronic component parameter data in a unified format; Perform parameter identification and parameter conversion on all the intermediate electronic component parameter data based on the artificial intelligence model to obtain standardized target electronic component parameter data; The validity of the target electronic component parameter data is verified, and the verified valid target electronic component parameter data is stored in a corresponding position in the electronic component database.

2. The method for managing electronic component parameters based on artificial intelligence according to claim 1, characterized in that: Before locating and extracting the key original parameter content in the to-be-identified file based on the preset extraction strategy to obtain the original electronic component parameter data, the method further includes: Obtaining an input file to be identified, and automatically determining whether a current format of the file to be identified is a target format according to a suffix of the file to be identified; If not, the current format of the file to be identified is converted into the target format.

3. The method for managing electronic component parameters based on artificial intelligence according to claim 1, characterized in that: The method of locating and extracting the key original parameter content in the to-be-identified file based on a preset extraction strategy to obtain the original electronic component parameter data includes: Determine whether the content of the file to be identified contains text content and non-text content; If so, locate the key original parameter content in the text content based on the text parsing algorithm and extract the parameters to obtain the original electronic component parameter data; If not, the key original parameter content in the non-text content is located based on a graphic feature recognition algorithm and parameter extraction is performed to obtain original electronic component parameter data.

4. The method for managing electronic component parameters based on artificial intelligence according to claim 3, characterized in that: The method of locating the key original parameter content in the non-text content based on the graphic feature recognition algorithm and extracting the parameters to obtain the original electronic component parameter data includes: Locating key original parameter content in the non-text content based on a graphic feature recognition algorithm; Based on an artificial intelligence recognition algorithm or an OCR recognition algorithm, parameter extraction is performed on the key original parameter content to obtain original electronic component parameter data.

5. The method for managing electronic component parameters based on artificial intelligence according to claim 1, characterized in that: Before performing parameter identification and parameter conversion on all the intermediate electronic component parameter data based on the artificial intelligence model to obtain standardized target electronic component parameter data, the method further includes: Based on the artificial intelligence model, erroneous characters in the original electronic component parameter data are detected and automatically corrected.

6. The method for managing electronic component parameters based on artificial intelligence according to claim 5, characterized in that: The method of performing parameter identification and parameter conversion on all the intermediate electronic component parameter data based on the artificial intelligence model to obtain standardized target electronic component parameter data includes: Perform parameter recognition on all the intermediate electronic component parameter data based on the artificial intelligence model to find standardized parameter names corresponding to the intermediate electronic component parameter data; Parameter conversion is performed on the intermediate electronic component parameter data to obtain standardized target electronic component parameter data; wherein the target electronic component parameter data includes standardized parameter names, parameter values ​​and numerical units.

7. An electronic component parameter management system based on artificial intelligence, characterized in that: It includes original parameter data extraction subsystem and parameter identification and classification subsystem; The original parameter data extraction subsystem is used to: Get the input file to be recognized; Based on a preset extraction strategy, key original parameter content in the file to be identified is located and extracted to obtain original electronic component parameter data; Performing structured processing on the original electronic component parameter data to obtain intermediate electronic component parameter data in a unified format; The parameter identification and classification subsystem is used to: Perform parameter identification and parameter conversion on all the intermediate electronic component parameter data based on the artificial intelligence model to obtain standardized target electronic component parameter data; The validity of the target electronic component parameter data is verified, and the verified valid target electronic component parameter data is stored in a corresponding position in the electronic component database.

8. The electronic component parameter management system based on artificial intelligence according to claim 7, characterized in that: The parameter identification and classification subsystem includes an electronic component database and the artificial intelligence model; Wherein, the electronic component database is established in the following manner: Based on various standard information, establish an electronic component database containing standardized parameter names and set standardized numerical units; The artificial intelligence model is prepared in the following manner: During the pre-training phase of the artificial intelligence model, input various types of standard data information; Customizing the artificial intelligence model so that the content output by the artificial intelligence model is limited to the existing fields in the electronic component database and can perform the functions of error character detection and correction, parameter identification and parameter conversion; The output results of the artificial intelligence model are verified for validity to obtain a trained artificial intelligence model.

9. A terminal, characterized in that: include: A processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, an artificial intelligence-based electronic component parameter management method as described in any one of claims 1 to 6 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute an electronic component parameter management method based on artificial intelligence as described in any one of claims 1 to 6.

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