Circuit board acceptance scoring method and system based on multi-modal data fusion

Abstract

The invention relates to a circuit board acceptance scoring method and system based on multi-modal data fusion, and the method comprises the steps: collecting an image of a to-be-scored circuit board, and obtaining the electrical parameter data of the circuit board through a probe and an analog-to-digital converter; the image and the electrical parameter data are preprocessed; performing feature extraction on the preprocessed image and the electrical parameter data; according to the image features and the electrical parameter data features, automatically performing score evaluation on the to-be-graded circuit board by using a grading algorithm and a weighting algorithm to obtain a circuit board score; and generating feedback suggestions according to the circuit board scores, and outputting the circuit board scores and the feedback suggestions through images, texts and sounds. Compared with the prior art, an automatic scoring mechanism is introduced, rapid and accurate scoring of the electronic process practical works can be achieved, and the scoring efficiency is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of circuit boards, and in particular to a circuit board acceptance scoring method and system based on multimodal data fusion. Background Art

[0002] In the electronic process internship education, it is crucial to cultivate students' practical operation skills and electronic product design and debugging capabilities. However, traditional manual scoring methods are often limited by teachers' subjective judgment and are inefficient, making it difficult to meet the needs of objective and fair evaluation of students' internship results.

[0003] Traditional scoring methods are not only highly subjective, but also difficult to quantify students' skill levels and innovation capabilities during internships. In addition, as the number of students increases, manual scoring methods have become increasingly difficult to meet the needs of efficient management.

[0004] The main technical problems faced in the current electronic process internship scoring process include the following:

[0005] 1. Inconsistency of subjective scoring: Traditional manual scoring methods rely on teachers’ subjective judgment, which may lead to large differences in scoring results between different teachers and lack of unified standards;

[0006] 2. Low grading efficiency: With the increase in the number of students, manual grading is not only time-consuming and laborious, but also difficult to complete the grading of a large number of internship works in a short period of time;

[0007] 3. Insufficient scoring accuracy: Traditional scoring methods may not fully and accurately reflect students’ actual performance and skill level in electronic process internships. Summary of the invention

[0008] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and to provide a circuit board acceptance scoring method and system based on multimodal data fusion, so as to improve the scoring efficiency of circuit board acceptance in electronic process internship, reduce subjectivity, and standardize scoring.

[0009] The purpose of the present invention can be achieved by the following technical solutions:

[0010] A circuit board acceptance scoring method based on multimodal data fusion, the method comprising:

[0011] Collect images of the circuit board to be evaluated, and obtain the electrical parameter data of the circuit board through the probe and analog-to-digital converter;

[0012] Preprocessing the image and the electrical parameter data;

[0013] Performing feature extraction on the preprocessed image and electrical parameter data, the extracted image features include welding quality features and component layout features, and the extracted electrical parameter data features include circuit connection features;

[0014] According to the image features and the electrical parameter data features, a scoring algorithm and a weighting algorithm are used to automatically score the circuit board to be scored, so as to obtain a circuit board score;

[0015] Feedback is generated based on the circuit board score, and the circuit board score and the feedback are output through graphics, text and sound.

[0016] Furthermore, the process of acquiring the electrical parameter data includes:

[0017] Touch the probes to the critical test points on the circuit board;

[0018] Through the probe, a signal of set frequency, amplitude and waveform is injected into the circuit board, and then the output electrical parameter data of each node of the circuit board under signal excitation is collected in combination with an analog-to-digital converter, wherein the electrical parameter data includes voltage, current, frequency and phase.

[0019] Furthermore, the preprocessing of the image includes: performing image enhancement processing on the collected circuit board image to be rated, including grayscale correction, noise removal and contrast enhancement; using an image segmentation algorithm to separate solder joints and components from the image for subsequent feature extraction;

[0020] The preprocessing of the electrical parameter data includes: performing data filtering on the collected electrical parameter data to remove high-frequency noise and interference signals, and performing data rationality checking on the electrical parameter data to eliminate abnormal values.

[0021] Furthermore, after the image and electrical parameter data are preprocessed, the processed data are preliminarily classified and sorted, and normalized so that the data have a unified dimension and value range.

[0022] Furthermore, the feature extraction process performed on the preprocessed image includes:

[0023] In the preprocessed image, the geometric shape of the solder joint is calculated by using a morphological analysis algorithm, and the geometric shape includes the solder joint area, perimeter and circularity. Meanwhile, the glossiness and color of the solder joint are obtained by photometric analysis. The solder joint area, perimeter, circularity, glossiness and color constitute the welding quality characteristics.

[0024] The precise position, direction and spacing of the components on the circuit board are obtained by image recognition and coordinate positioning algorithm, and the precise position, direction and spacing of the components on the circuit board are analyzed to obtain the arrangement of the components. The precise position, direction and spacing of the components on the circuit board and the arrangement of the components constitute the component layout features;

[0025] The process of extracting the features of the preprocessed electrical parameter data includes:

[0026] The resistance, capacitance and inductance of the circuit are calculated based on the preprocessed electrical parameter data, the signal transmission characteristics are analyzed, and the circuit connection breaks, short circuits and cold solder joints are extracted. The signal transmission characteristics and the circuit connection breaks, short circuits and cold solder joints constitute the circuit connection features.

[0027] Furthermore, the circuit board scoring includes the scoring of image features, the scoring of electrical parameter data features, the scoring of the circuit board to be scored, and the grade evaluation of the circuit board to be scored;

[0028] The scoring algorithm includes automatically determining and calculating scores for the image features and the electrical parameter data features based on a preset scoring standard;

[0029] The weighted algorithm includes obtaining a score of the circuit board to be scored by combining the scores of the image features and the electrical parameter data features based on a preset score weight;

[0030] The score assessment process includes: using the scoring algorithm to automatically determine and calculate the scores of the image features and the electrical parameter data features; using the weighted algorithm to calculate the score of the circuit board to be scored; and giving the circuit board to be scored a corresponding grade evaluation based on the preset scoring range of different grades.

[0031] Furthermore, the feedback generation process includes:

[0032] Obtain the scores of image features, the scores of electrical parameter data features, the scores of circuit boards to be scored, and the grade evaluation of circuit boards to be scored; retrieve corresponding feedback statements in the circuit board database according to the scores and grade evaluations; combine the feedback statements in grammatical order to generate feedback opinions.

[0033] Furthermore, the storage content in the circuit board database includes historical circuit board images, historical circuit board electrical parameter data, historical image features, historical electrical parameter data features, historical circuit board scores and historical feedback. The circuit board database classifies and manages the storage content according to the student number, circuit board type and time of the student who made the circuit board; after the circuit board score and feedback are presented to the students, the current circuit board image, electrical parameter data, image features, electrical parameter data features, circuit board score and feedback are stored in the circuit board database; the circuit board database will update the storage content according to the teaching content.

[0034] Furthermore, before the score assessment, the circuit board to be scored is searched for repeated scores in the circuit board database based on the student number and circuit board type of the student who made the circuit board. If the student number and circuit board type that are the same as those of the circuit board to be scored are retrieved from the circuit board database, it is determined that the circuit board to be scored is undergoing a repeated scoring operation, and the image, electrical parameter data, image features and electrical parameter data features of the circuit board to be scored are compared with the historical data in the circuit board database. If they are consistent, the corresponding circuit board score and feedback in the circuit board database are output; if they are inconsistent, the differences in the features of the circuit board to be scored are compared, and the score is assessed in combination with the modifications to the circuit board to be scored, and additional feedback is generated for the modifications when feedback is generated.

[0035] A circuit board acceptance scoring system based on multimodal data fusion, the system comprising:

[0036] An image acquisition module, which acquires images of the circuit board to be rated;

[0037] An electrical parameter data acquisition module generates a signal and collects electrical parameter data of the circuit board to be evaluated;

[0038] A data processing and control module receives data from the image acquisition module and the electrical parameter data acquisition module, performs real-time preprocessing, feature extraction and score evaluation on the image and the electrical parameter data to obtain a circuit board score, and generates feedback based on the circuit board score;

[0039] The display and feedback module outputs the rating and the feedback through graphics, text and sound.

[0040] Compared with the prior art, the beneficial effects of the present invention include:

[0041] 1. The present invention introduces an automated scoring mechanism, which enables fast and accurate scoring of electronic process practice works, significantly improves scoring efficiency, and also reduces subjective interference in the scoring process, ensuring the fairness and consistency of the scoring results; the present invention scores the circuit board, in addition to using image recognition to evaluate the appearance of the circuit board, and also uses probes to collect the output signals of each node of the circuit board under signal excitation, deeply detects the circuit connection status and performance of the circuit board, and comprehensively evaluates the circuit board, making the scoring more comprehensive; through the preset scoring standards and algorithms, standardized scoring of practical works is achieved; feedback is generated based on the scoring of the circuit board, and the scoring results and abnormal situations are immediately fed back to students, which helps students correct errors in time and improves the efficiency of circuit board welding learning;

[0042] 2. The present invention collects various data of the circuit board and stores them in the circuit board database, which can realize comprehensive data management of the teaching process. This data management method helps teachers to have a deeper understanding of students' learning situation and provide strong support for subsequent teaching decisions;

[0043] 3. The present invention also establishes a repeated scoring retrieval to address the situation where students may repeatedly submit the same circuit board for scoring or submit the circuit board for scoring again after modification. For circuit boards with the same characteristics, the same score and feedback are output. For circuit boards submitted again after modification, comprehensive evaluation and feedback are given based on the improvements. This improves the scoring efficiency while also increasing the system's adaptability. Scoring and feedback are given in a more comprehensive, objective and flexible manner, thereby improving the learning quality of electronic process internships. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 is a flow chart of the method of the present invention;

[0045] Figure 2 This is a system structure diagram of the present invention. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0047] Example 1

[0048] This embodiment has disclosed a circuit board acceptance scoring method based on multimodal data fusion. This method is mainly aimed at the acceptance scoring of students soldering circuit boards in electronic process practice, such as Figure 1 As shown, the specific steps of the method include:

[0049] Step S1, collecting an image of a circuit board to be evaluated, and acquiring electrical parameter data of the circuit board through a probe and an analog-to-digital converter.

[0050] The process of obtaining electrical parameter data includes:

[0051] Touch the probes to the critical test points on the circuit board;

[0052] Through the probe, a signal with set frequency, amplitude and waveform is injected into the circuit board, and then the output electrical parameter data of each node of the circuit board under signal excitation is collected in combination with the analog-to-digital converter. The electrical parameter data includes voltage, current, frequency and phase.

[0053] Step S2, preprocessing the image and electrical parameter data.

[0054] The image preprocessing includes: image enhancement processing of the collected circuit board images to be rated, including grayscale correction, noise removal and contrast enhancement; using image segmentation algorithm to separate solder joints and components from the image for subsequent feature extraction;

[0055] The preprocessing of the electrical parameter data includes: performing data filtering on the collected electrical parameter data to remove high-frequency noise and interference signals, and performing data rationality checks on the electrical parameter data to eliminate abnormal values.

[0056] After preprocessing the image and electrical parameter data, the processed data are preliminarily classified and normalized to give the data a uniform dimension and numerical range.

[0057] Step S3, extracting features from the preprocessed image and electrical parameter data, wherein the extracted image features include welding quality features and component layout features, and the extracted electrical parameter data features include circuit connection features.

[0058] The process of feature extraction of the preprocessed image includes:

[0059] In the preprocessed image, the geometric shape of the solder joint is calculated using the morphological analysis algorithm. The geometric shape includes the solder joint area, perimeter and circularity. At the same time, the glossiness and color of the solder joint are obtained through photometric analysis. The solder joint area, perimeter, circularity, glossiness and color constitute the welding quality characteristics.

[0060] The precise position, direction and spacing of components on the circuit board are obtained through image recognition and coordinate positioning algorithms. The precise position, direction and spacing of components on the circuit board are analyzed to obtain the arrangement of components. The precise position, direction and spacing of components on the circuit board and the arrangement of components constitute the layout characteristics of components.

[0061] The process of extracting the features of the preprocessed electrical parameter data includes:

[0062] The resistance, capacitance and inductance of the circuit are calculated based on the preprocessed electrical parameter data, the signal transmission characteristics are analyzed, and the circuit connection breaks, short circuits and cold solder joints are extracted. The signal transmission characteristics and the circuit connection breaks, short circuits and cold solder joints constitute the circuit connection features.

[0063] Step S4, based on the image features and electrical parameter data features, a scoring algorithm and a weighting algorithm are used to automatically score the circuit board to obtain a circuit board score.

[0064] The circuit board scoring includes the scoring of image features, the scoring of electrical parameter data features, the scoring of the circuit board to be scored, and the grade evaluation of the circuit board to be scored.

[0065] The scoring algorithm includes automatic score determination and calculation of image features and electrical parameter data features based on preset scoring criteria;

[0066] The weighted algorithm includes scoring the image features and electrical parameter data features based on a preset score weight to obtain a score for the circuit board to be scored;

[0067] The scoring process includes: using a scoring algorithm to automatically determine and calculate the scores of image features and electrical parameter data features; using a weighted algorithm to calculate the score of the circuit board to be scored; and giving the circuit board to be scored a corresponding grade evaluation based on the preset scoring range of different grades.

[0068] Step S5, generating feedback based on the circuit board score, and outputting the circuit board score and feedback through graphics, text and sound.

[0069] The feedback generation process includes:

[0070] Obtain the scores of image features, the scores of electrical parameter data features, the scores of circuit boards to be scored, and the grade evaluation of circuit boards to be scored; retrieve corresponding feedback statements in the circuit board database according to the scores and grade evaluations; combine the feedback statements in grammatical order to generate feedback opinions.

[0071] The storage content in the circuit board database includes historical circuit board images, historical circuit board electrical parameter data, historical image features, historical electrical parameter data features, historical circuit board scores and historical feedback. The circuit board database classifies and manages the storage content according to the student number, circuit board type and time of the student who made the circuit board; after the circuit board score and feedback are presented to the students, the current circuit board image, electrical parameter data, image features, electrical parameter data features, circuit board score and feedback are stored in the circuit board database; the circuit board database will update the storage content according to the teaching content.

[0072] Before scoring, the circuit board to be scored is searched for repeated scores in the circuit board database based on the student number and circuit board type of the student who made the circuit board. If the same student number and circuit board type as the circuit board to be scored are retrieved in the circuit board database, it is determined that the circuit board to be scored is undergoing a repeated scoring operation, and the image, electrical parameter data, image features and electrical parameter data features of the circuit board to be scored are compared with the historical data in the circuit board database. If they are consistent, the corresponding circuit board score and feedback in the circuit board database are output. If they are inconsistent, the differences in the features of the circuit board to be scored are compared, and the score is assessed in combination with the modifications to the circuit board to be scored, and additional feedback is generated for the modifications when feedback is generated.

[0073] Below, based on the above method, an example is given with a practical application scenario:

[0074] When the circuit board work is connected to the device, image acquisition is automatically started. The camera shoots the work from different angles in preset shooting modes, such as timed shooting and triggered shooting, to obtain high-resolution welding quality and component layout images. The camera has autofocus and image enhancement functions to ensure that the captured images are clear and accurate, providing a high-quality data source for subsequent analysis.

[0075] The collected raw image data is first subjected to image enhancement processing, such as grayscale correction, noise removal, contrast enhancement, etc., to improve image quality. Then, the image segmentation algorithm is used to accurately separate the solder joints and components from the background to facilitate subsequent feature extraction. For electrical parameter data, data filtering is performed to remove high-frequency noise and interference signals, and data rationality checks are performed to remove outliers. The processed data is classified and sorted according to feature categories such as welding quality, component layout, circuit connection, etc., and normalized so that different types of data have a unified dimension and numerical range, which is convenient for subsequent algorithm analysis.

[0076] Next, feature extraction is performed to extract welding quality features: from the preprocessed solder joint image, the geometric features of the solder joint, such as solder joint area, perimeter, circularity, etc., are calculated using the morphological analysis algorithm. At the same time, the glossiness and color characteristics of the solder joint are obtained through photometric analysis to comprehensively judge the solder joint quality. For example, a solder joint circularity close to 1 and a high glossiness usually indicates a good weld.

[0077] Component layout feature extraction: Determine the precise position, direction and spacing of components on the circuit board through image recognition and coordinate positioning algorithms, and analyze whether the component arrangement conforms to the layout rules, such as whether it is arranged by functional area and whether the component spacing is uniform. At the same time, combined with the circuit schematic information, determine whether the component connection relationship is correct and extract the rationality characteristics of the component layout.

[0078] Circuit connection feature extraction: Based on the electrical parameter data collected by the probe system, calculate the equivalent parameters of the circuit such as resistance, capacitance, inductance, etc., analyze the signal transmission characteristics such as signal attenuation, delay and distortion, and determine whether the circuit connection has problems such as open circuit, short circuit, cold solder joint, etc. For example, if the resistance value between two points is abnormally large, there may be a circuit break.

[0079] Subsequently, the scoring algorithm conducts a comprehensive analysis of the extracted features based on the preset scoring criteria. For welding quality, the score is based on the degree of deviation between the geometric and appearance features of the solder joints and the standard values. For example, if the solder joint area deviation is within ±10%, full marks will be awarded, and a certain score will be deducted for each deviation exceeding ±1%. For component layout, the score is based on the rationality of the component position, spacing and connection relationship. If the component spacing is less than the minimum safe spacing, points will be deducted. For circuit connection, the correctness of the connection is judged based on the electrical parameter calculation results and signal transmission characteristics. If there is a break or short circuit, it is directly judged as unqualified. For product function realization, the score is based on the frequency signal accuracy. If the frequency range deviation is within ±5kHZ, the corresponding score can be obtained. The final score of the practical work is calculated by weighted summation based on the scoring results of various indicators. The weights are set according to the practical focus and teaching requirements, such as the welding quality weight is 0.1, the component layout weight is 0.2, the frequency accuracy weight is 0.5, and the appearance weight of the practical work is 0.1. The corresponding grade evaluation is given according to the score range, such as 90 to 100 points for A, 80 to 89 points for B, and so on.

[0080] The scoring results and detailed feedback are presented to students on the display screen. The feedback not only includes the score and grade, but also specifically points out the existing problems and improvement directions for each scoring item, such as "the size of solder joint X is too large, which may cause a short circuit risk, please adjust the welding parameters." At the same time, based on the students' scores and common problems, the system automatically generates personalized learning suggestions and guidance plans, such as recommending relevant welding skills video tutorials or circuit design optimization materials to help students improve their practical skills.

[0081] The system automatically stores historical data such as students' practical work data, scoring results, and feedback, and manages them by categories according to student ID, practical project, time, and other dimensions. Teachers can quickly retrieve data for specific students or projects through the query function, and use statistical analysis tools to generate various reports, such as class average score reports, score distribution reports for different projects, etc., to deeply analyze the practical situation and student performance. Through data mining technology, common problems and individual differences of students in the practice process are discovered, providing data support for teaching improvements, such as adjusting teaching content and improving practical project design.

[0082] Example 2

[0083] This embodiment is based on the above-mentioned embodiment 1 and is intended to disclose a circuit board acceptance scoring system based on multimodal data fusion. The system modules are as follows: Figure 2 As shown, including:

[0084] Image acquisition module 1, including multiple high-resolution cameras, takes pictures of PCB boards from multiple angles to comprehensively collect images of the circuit boards to be rated. The cameras have autofocus and image enhancement functions to ensure that the images taken are clear and accurate, providing a high-quality data source for subsequent analysis;

[0085] The electrical parameter data acquisition module 2 is based on advanced signal generation technology and can accurately generate various types of signals, such as sine waves, square waves and pulse waves, according to the requirements of electronic product testing. The signal frequency can be adjusted within a wide range to generate signals. The generated signals of specific frequency, amplitude and waveform are accurately injected into the circuit board through the probe, and then the electrical parameter data of the circuit board to be scored is collected by the probe and the analog-to-digital converter. The analog-to-digital converter has a sampling rate of 100MS / s and a resolution of more than 16 bits. It can accurately collect various weak signal changes of the PCB board during the test process. It also has signal conditioning functions to amplify, filter and isolate the collected signals to ensure that the signal quality meets the requirements of subsequent analysis.

[0086] The data processing and control module 3 receives the data from the image acquisition module and the electrical parameter data acquisition module, performs real-time preprocessing, feature extraction and score evaluation on the image and electrical parameter data, obtains the circuit board score, and generates feedback based on the circuit board score. As the core computing unit of the system, this module is equipped with a high-performance processor and a large-capacity memory. Its built-in storage unit uses a high-speed solid-state hard disk to quickly store and read key information such as scoring standards, algorithm models and a large amount of historical data to ensure smooth and efficient operation of the system. At the same time, this module also provides various types of interfaces, including standard interfaces such as USB, HDMI, RS-232, and special interfaces for specific practical equipment to ensure stable and high-speed connection with various circuit boards and accurate data transmission. At the same time, it supports communication with external computers, servers and other devices to facilitate remote upload, download and sharing of data.

[0087] The display and feedback module 4 presents the scores and feedback to the students through pictures, texts and sound prompts. It adopts a high-definition LCD screen, which can intuitively display the scoring results, detailed feedback and a simple and easy-to-use operation interface. The display interface supports the display of pictures and texts. For example, when displaying welding quality problems, the image and text description of the problem solder joint can be presented at the same time. In addition, different color indicator lights, such as green for pass, red for fail, and yellow for some problems, and diversified sound prompts, such as different tones for different scoring levels or problem types, can be used to instantly feedback the scoring results and abnormal situations to students. Even if students do not check the display screen, they can quickly obtain basic information.

[0088] The remaining specific details of the above modules can be understood by referring to the relevant descriptions and effects in Example 1.

[0089] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A circuit board acceptance scoring method based on multimodal data fusion, characterized in that: The method comprises: Collect images of the circuit board to be evaluated, and obtain the electrical parameter data of the circuit board through the probe and analog-to-digital converter; Preprocessing the image and the electrical parameter data; Performing feature extraction on the preprocessed image and electrical parameter data, the extracted image features include welding quality features and component layout features, and the extracted electrical parameter data features include circuit connection features; According to the image features and the electrical parameter data features, a scoring algorithm and a weighting algorithm are used to automatically score the circuit board to be scored, so as to obtain a circuit board score; Feedback is generated based on the circuit board score, and the circuit board score and the feedback are output through graphics, text and sound.

2. A circuit board acceptance scoring method based on multimodal data fusion according to claim 1, characterized in that: The process of acquiring the electrical parameter data includes: Touch the probes to the critical test points on the circuit board; Through the probe, a signal of set frequency, amplitude and waveform is injected into the circuit board, and then the output electrical parameter data of each node of the circuit board under signal excitation is collected in combination with an analog-to-digital converter, wherein the electrical parameter data includes voltage, current, frequency and phase.

3. A circuit board acceptance scoring method based on multimodal data fusion according to claim 1, characterized in that: The preprocessing of the image includes: performing image enhancement processing on the collected circuit board image to be rated, including grayscale correction, noise removal and contrast enhancement; using an image segmentation algorithm to separate solder joints and components from the image for subsequent feature extraction; The preprocessing of the electrical parameter data includes: performing data filtering on the collected electrical parameter data to remove high-frequency noise and interference signals, and performing data rationality checking on the electrical parameter data to eliminate abnormal values.

4. The circuit board acceptance scoring method based on multimodal data fusion according to claim 1 is characterized in that: After the image and electrical parameter data are preprocessed, the processed data are preliminarily classified and sorted, and normalized so that the data have a unified dimension and value range.

5. The circuit board acceptance scoring method based on multimodal data fusion according to claim 1 is characterized in that: The process of extracting the features of the preprocessed image includes: In the preprocessed image, the geometric shape of the solder joint is calculated by using a morphological analysis algorithm, and the geometric shape includes the solder joint area, perimeter and circularity. Meanwhile, the glossiness and color of the solder joint are obtained by photometric analysis. The solder joint area, perimeter, circularity, glossiness and color constitute the welding quality characteristics. The precise position, direction and spacing of the components on the circuit board are obtained by image recognition and coordinate positioning algorithm, and the precise position, direction and spacing of the components on the circuit board are analyzed to obtain the arrangement of the components. The precise position, direction and spacing of the components on the circuit board and the arrangement of the components constitute the component layout features; The process of extracting the features of the preprocessed electrical parameter data includes: The resistance, capacitance and inductance of the circuit are calculated based on the preprocessed electrical parameter data, the signal transmission characteristics are analyzed, and the circuit connection breaks, short circuits and cold solder joints are extracted. The signal transmission characteristics and the circuit connection breaks, short circuits and cold solder joints constitute the circuit connection features.

6. The circuit board acceptance scoring method based on multimodal data fusion according to claim 1 is characterized in that: The circuit board scoring includes the scoring of image features, the scoring of electrical parameter data features, the scoring of the circuit board to be scored, and the grade evaluation of the circuit board to be scored; The scoring algorithm includes automatically determining and calculating scores for the image features and the electrical parameter data features based on a preset scoring standard; The weighted algorithm includes obtaining a score of the circuit board to be scored by combining the scores of the image features and the electrical parameter data features based on a preset score weight; The score assessment process includes: using the scoring algorithm to automatically determine and calculate the scores of the image features and the electrical parameter data features; using the weighted algorithm to calculate the score of the circuit board to be scored; and giving the circuit board to be scored a corresponding grade evaluation based on the preset scoring range of different grades.

7. A circuit board acceptance scoring method based on multimodal data fusion according to claim 6, characterized in that: The feedback generation process includes: Obtain the scores of image features, the scores of electrical parameter data features, the scores of circuit boards to be scored, and the grade evaluation of circuit boards to be scored; retrieve corresponding feedback statements in the circuit board database according to the scores and grade evaluations; combine the feedback statements in grammatical order to generate feedback opinions.

8. A circuit board acceptance scoring method based on multimodal data fusion according to claim 7, characterized in that: The storage content in the circuit board database includes historical circuit board images, historical circuit board electrical parameter data, historical image features, historical electrical parameter data features, historical circuit board scores and historical feedback. The circuit board database classifies and manages the storage content according to the student number, circuit board type and time of the student who made the circuit board; after the circuit board score and feedback are presented to the students, the current circuit board image, electrical parameter data, image features, electrical parameter data features, circuit board score and feedback are stored in the circuit board database; the circuit board database will update the storage content according to the teaching content.

9. A circuit board acceptance scoring method based on multimodal data fusion according to claim 8, characterized in that: Before the score assessment, the circuit board to be scored is searched for repeated scores in the circuit board database based on the student number and circuit board type of the student who made the circuit board. If the student number and circuit board type that are the same as those of the circuit board to be scored are retrieved in the circuit board database, it is determined that the circuit board to be scored is undergoing a repeated scoring operation, and the image, electrical parameter data, image features and electrical parameter data features of the circuit board to be scored are compared with the historical data in the circuit board database. If they are consistent, the corresponding circuit board score and feedback in the circuit board database are output; if they are inconsistent, the differences in the features of the circuit board to be scored are compared, and the score is assessed in combination with the modifications to the circuit board to be scored, and additional feedback is generated for the modifications when feedback is generated.

10. A circuit board acceptance scoring system based on multimodal data fusion, characterized in that: The system comprises: An image acquisition module, which acquires images of circuit boards to be rated; An electrical parameter data acquisition module generates a signal and collects electrical parameter data of the circuit board to be evaluated; A data processing and control module receives data from the image acquisition module and the electrical parameter data acquisition module, performs real-time preprocessing, feature extraction and score evaluation on the image and the electrical parameter data to obtain a circuit board score, and generates feedback based on the circuit board score; The display and feedback module outputs the rating and the feedback through graphics, text and sound.

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