Image data and structured data fusion analysis method based on artificial intelligence algorithm

Abstract

The invention discloses an image data and structured data fusion analysis method based on an artificial intelligence algorithm, and relates to the technical field of data fusion, and the method comprises the steps: calculating a first image feature weight and a second image feature weight of each image feature vector, calculating a comprehensive weight of image features, and obtaining a first image feature weight and a second image feature weight of each image feature vector; and obtaining the weight of the first structural feature and the weight of the second structural feature of each structural data feature vector, calculating the comprehensive weight of the structural features, after alignment processing, deleting the redundant image features or structural features ranked at the back, and starting from the image feature with the maximum comprehensive weight of the image features. The image features are matched with the structural features corresponding to the maximum cosine similarity in the cosine similarities of all the structural data features, the steps are repeated until all the image features are matched, cross-domain fusion between the image data and the structural data can be promoted, and support is provided for comprehensive data analysis and decision making.

Description

Technical Field

[0001] The present invention relates to the technical field of data fusion, and in particular to an image data and structured data fusion analysis method based on an artificial intelligence algorithm. Background Art

[0002] With the rapid development of information technology and artificial intelligence technology, data processing and analysis capabilities have been significantly improved, especially in the field of multimedia data processing. Image data and structured data, as two important data types, play a vital role in various fields. Image data, with its rich visual information and intuitive form of expression, is widely used in medical imaging, autonomous driving, intelligent security and other fields; while structured data, with its precise and standardized characteristics, shows great potential in data analysis, decision support and other aspects. However, a single type of data is often difficult to meet the needs of complex application scenarios. Therefore, how to achieve the effective fusion and analysis of image data and structured data has become an important topic of current research.

[0003] In the Chinese invention application with application publication number CN117437513A, a method for image data fusion analysis based on big data is disclosed. In order to overcome the shortcomings of the prior art, such as high requirements for multi-image processing performance, limited single image information, high complexity and low efficiency of multi-image processing tasks, slow data extraction, and inaccurate data, a method for image data fusion analysis based on big data is provided. Image analysis is carried out in a big data manner, multiple similar images are marked and stored in the same storage space, and the contents of multiple similar images are merged through image fusion. The efficiency of data extraction and the efficiency of the system in image analysis are improved, making the image analysis results more universal.

[0004] In the above invention application, image analysis is carried out through big data, multiple similar images are marked and stored in the same storage space, and the contents of multiple similar pictures are merged through image fusion, but the basis is the fusion between data of the same type. However, a single type of data is often difficult to meet the needs of complex application scenarios, and often cannot fully and deeply reveal the laws and patterns behind the data.

[0005] To this end, the present invention provides an image data and structured data fusion analysis method based on artificial intelligence algorithm. Summary of the invention

[0006] 1. Technical issues to be resolved In view of the shortcomings of the prior art, the present invention provides an image data and structured data fusion analysis method based on artificial intelligence algorithm. The present invention calculates the first image feature weight of each image feature vector and the second image feature weight , calculate the comprehensive weight of image features , and obtain the first structural feature of each structured data feature vector and the second structural feature weight , calculate the comprehensive weight of structural features After alignment, the redundant image features or structural features that are ranked later are deleted, and the comprehensive weight of the image features is Starting from the largest image feature, the image feature is matched with the structural feature corresponding to the largest cosine similarity among the cosine similarities of all structured data features, and the above steps are repeated until all image features are matched. Not only the similarity between features of the same type but also the similarity between features of different types are considered, which can promote the cross-domain integration between image data and structured data, provide support for comprehensive data analysis and decision-making, and thus solve the technical problems recorded in the background technology.

[0007] (II) Technical solution To achieve the above objectives, the present invention is implemented through the following technical solutions: a method for fusion analysis of image data and structured data based on artificial intelligence algorithm, comprising the following steps: Use various feature extraction algorithms to extract image features of image data, then use dimensionality reduction technology to reduce the high-dimensional feature vector to a low-dimensional space, and calculate the cosine similarity between each image feature vector in the low-dimensional space. , and calculate the first image feature weight of each image feature vector ; Use the NLP library to analyze structured data, extract structured text features, and calculate the cosine similarity between each structured text feature vector in a low-dimensional space. , and calculate the first structural feature weight of the structured text feature vector ; Get image features and structured data features, and analyze the cosine similarity between each structured data feature and all image features , calculate the second structural feature weight of each structured data feature , and analyze the cosine similarity between each image feature and all structured data features , calculate the second image feature weight for each image feature ; Get the first image feature weight of each image feature vector and the second image feature weight , calculate the comprehensive weight of image features , and obtain the first structural feature of each structured data feature vector and the second structural feature weight , calculate the comprehensive weight of structural features After alignment, the redundant image features or structural features that are ranked later are deleted, and the comprehensive weight of the image features is Starting from the largest image feature, the image feature is matched with the structural feature corresponding to the largest cosine similarity among the cosine similarities of all structured data features, and the above steps are repeated until all image features are matched.

[0008] Furthermore, various feature extraction algorithms (such as SIFT, SURF, ORB and other local feature descriptors) and convolutional layers in deep learning methods are used to extract color features, texture features, shape features, spatial relationship features, etc. of image data to obtain high-level image feature text representations. The high-dimensional feature vectors are then reduced to low-dimensional space through the bag-of-words model, and the similarities between the feature vectors of each image are calculated in the low-dimensional space to obtain the cosine similarity between the feature vectors of each image. .

[0009] The bag-of-words model is the simplest text vectorization method. It treats text as an unordered collection of words, ignoring the order and grammatical relationship between words. The specific steps are as follows: Build a vocabulary: Traverse all texts, count all the words that appear, and build a vocabulary containing all the words. Text vectorization: For each text, create a vector based on the vocabulary, and each dimension of the vector corresponds to a word in the vocabulary. If the text contains the word, the value of the corresponding dimension is the number of times the word appears in the text (or other weight), otherwise it is 0.

[0010] Furthermore, the cosine similarity between the feature vectors of each image is obtained , calculate the first image feature weight of each image feature vector :

[0011] in, a and b Both represent the sequential numbers of image feature vectors, and a =1, 2, …, n, b =1, 2,…, n.

[0012] Furthermore, we use NLP libraries (such as NLTK, spaCy, Stanford NLP, etc. in Python) to perform more complex text analysis tasks on structured data, extract structured text features, and then use the bag-of-words model to reduce the high-dimensional feature vectors to a low-dimensional space. In the low-dimensional space, we calculate the similarity between each structured text feature vector and obtain the cosine similarity between each structured text feature vector. .

[0013] Structured data has clear fields and attributes, and each field has a specific data type and value range. This type of data is easy to organize, store, and analyze, and can be effectively processed and mined through various algorithms and tools. Structured data is usually presented in tables, tree structures, graphics, or other forms, such as tables in relational databases, CSV files, XML documents, JSON data, etc.

[0014] Furthermore, the cosine similarity between each structured text feature vector is obtained , calculate the first structural feature of the structured text feature vector :

[0015] in, c and d Both represent the sequential numbers of image feature vectors, and c =1, 2, …, m, d =1, 2, …, m.

[0016] Furthermore, the image features and structured data features are obtained, and the cosine similarity between each structured data feature and all image features is analyzed. , calculate the second structural feature weight of each structured data feature :

[0017] Cosine similarity is a mathematical method to measure the similarity between two vectors in multidimensional space. The core idea is to regard the vectors as rays starting from the origin and evaluate their similarity by calculating the cosine value of the angle between the two rays.

[0018] Furthermore, the image features and structured data features are obtained, and the cosine similarity between each image feature and all structured data features is analyzed. , calculate the second image feature weight for each image feature :

[0019] The second structural feature weight of each corresponding image feature The calculation formula is as above.

[0020] Further, the first image feature weight of each image feature vector is obtained and the second image feature weight , calculate the comprehensive weight of image features :

[0021] The comprehensive weight of the corresponding image features The calculation formula is as above.

[0022] Further, the first structural feature of each structured data feature vector is obtained and the second structural feature weight , calculate the comprehensive weight of structural features :

[0023] The comprehensive weight of the corresponding structural features The calculation formula is as above.

[0024] Further, the comprehensive weight of image features is obtained and the comprehensive weight of structural features , after sorting them from large to small, the numbers are aligned and the redundant image features or structural features sorted later are deleted.

[0025] Furthermore, after the number of image features and structural features is consistent, the comprehensive weight of image features Starting from the largest image feature, the image feature is matched with the structural feature corresponding to the largest cosine similarity among the cosine similarities of all structured data features, and the above steps are repeated until all image features are matched.

[0026] (III) Beneficial effects The present invention provides an image data and structured data fusion analysis method based on artificial intelligence algorithm, which has the following beneficial effects: 1. Use various feature extraction algorithms to extract image features of image data, and then use dimensionality reduction technology to reduce the high-dimensional feature vector to a low-dimensional space, and calculate the cosine similarity between each image feature vector in the low-dimensional space. , and calculate the first image feature weight of each image feature vector , we can evaluate the similarity between different image feature vectors, further understand which features play a key role in image classification, retrieval, fusion and other tasks, and provide a basis for subsequent classification, retrieval, fusion and other tasks.

[0027] 2. Use the NLP library to analyze structured data, extract structured text features, and calculate the cosine similarity between each structured text feature vector in a low-dimensional space. , and calculate the first structural feature of the structured text feature vector , the similarity between different structured text feature vectors can be evaluated, which helps to gain a deeper understanding of the meaning and context of the data and provide strong support for subsequent analysis and processing.

[0028] 3. Obtain image features and structured data features, and analyze the cosine similarity between each structured data feature and all image features , calculate the second structural feature weight of each structured data feature , and analyze the cosine similarity between each image feature and all structured data features , calculate the second image feature weight for each image feature , the impact on the overall feature set of different data types can be analyzed, thereby improving the accuracy and effectiveness of feature representation, helping subsequent analysis and processing processes to focus more on key information and improve overall efficiency.

[0029] 4. Obtain the first image feature weight of each image feature vector and the second image feature weight , calculate the comprehensive weight of image features , and obtain the first structural feature of each structured data feature vector and the second structural feature weight , calculate the comprehensive weight of structural features After alignment, the redundant image features or structural features that are ranked later are deleted, and the comprehensive weight of the image features is Starting from the largest image feature, the image feature is matched with the structural feature corresponding to the largest cosine similarity among the cosine similarities of all structured data features, and the above steps are repeated until all image features are matched. This not only considers the similarity between features of the same type, but also the similarity between features of different types, which can promote the cross-domain integration between image data and structured data, and provide support for comprehensive data analysis and decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 The figure is a flow chart of the image data and structured data fusion analysis method based on artificial intelligence algorithm of the present invention. DETAILED DESCRIPTION

[0031] 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 only 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 are within the scope of protection of the present invention.

[0032] See also Figure 1 The present invention provides an image data and structured data fusion analysis method based on an artificial intelligence algorithm, comprising the following steps: Step 1: Use various feature extraction algorithms to extract image features of image data, and then use dimensionality reduction technology to reduce the high-dimensional feature vector to a low-dimensional space, and calculate the cosine similarity between each image feature vector in the low-dimensional space. , and calculate the first image feature weight of each image feature vector .

[0033] The step 1 includes the following contents: Step 101: Use various feature extraction algorithms (such as SIFT, SURF, ORB and other local feature descriptors) and the convolution layer in the deep learning method to extract the color features, texture features, shape features, spatial relationship features, etc. of the image data to obtain a high-level text representation of image features, and then use the bag-of-words model to reduce the high-dimensional feature vector to a low-dimensional space, calculate the similarity between each image feature vector in the low-dimensional space, and obtain the cosine similarity between each image feature vector .

[0034] The bag-of-words model is the simplest text vectorization method. It treats text as an unordered collection of words, ignoring the order and grammatical relationship between words. The specific steps are as follows: Build a vocabulary: Traverse all texts, count all the words that appear, and build a vocabulary containing all the words. Text vectorization: For each text, create a vector based on the vocabulary, and each dimension of the vector corresponds to a word in the vocabulary. If the text contains the word, the value of the corresponding dimension is the number of times the word appears in the text (or other weight), otherwise it is 0.

[0035] Step 102: Obtain the cosine similarity between each image feature vector , calculate the first image feature weight of each image feature vector :

[0036] in, a and b Both represent the sequential numbers of image feature vectors, and a =1, 2, …, n, b =1, 2,…, n.

[0037] When using, combine the contents in steps 101 and 102: Use various feature extraction algorithms to extract image features of image data, then use dimensionality reduction technology to reduce the high-dimensional feature vector to a low-dimensional space, and calculate the cosine similarity between each image feature vector in the low-dimensional space. , and calculate the first image feature weight of each image feature vector , we can evaluate the similarity between different image feature vectors, further understand which features play a key role in image classification, retrieval, fusion and other tasks, and provide a basis for subsequent classification, retrieval, fusion and other tasks.

[0038] Step 2: Use the NLP library to analyze the structured data, extract the structured text features, and calculate the cosine similarity between each structured text feature vector in the low-dimensional space. , and calculate the first structural feature weight of the structured text feature vector .

[0039] The step 2 includes the following contents: Step 201: Use NLP libraries (such as NLTK, spaCy, Stanford NLP in Python, etc.) to perform more complex text analysis tasks on structured data, extract structured text features, and then use the bag-of-words model to reduce the high-dimensional feature vector to a low-dimensional space, calculate the similarity between each structured text feature vector in the low-dimensional space, and obtain the cosine similarity between each structured text feature vector. .

[0040] Structured data has clear fields and attributes, and each field has a specific data type and value range. This type of data is easy to organize, store, and analyze, and can be effectively processed and mined through various algorithms and tools. Structured data is usually presented in tables, tree structures, graphics, or other forms, such as tables in relational databases, CSV files, XML documents, JSON data, etc.

[0041] Step 202: Obtain the cosine similarity between each structured text feature vector , calculate the first structural feature of the structured text feature vector :

[0042] in, c and d Both represent the sequential numbers of image feature vectors, and c =1, 2, …, m, d =1, 2, …, m.

[0043] When using, combine the contents in steps 201 and 202: Use the NLP library to analyze structured data, extract structured text features, and calculate the cosine similarity between each structured text feature vector in a low-dimensional space. , and calculate the first structural feature of the structured text feature vector , the similarity between different structured text feature vectors can be evaluated, which helps to gain a deeper understanding of the meaning and context of the data and provide strong support for subsequent analysis and processing.

[0044] Step 3: Obtain image features and structured data features, and analyze the cosine similarity between each structured data feature and all image features , calculate the second structural feature weight of each structured data feature , and analyze the cosine similarity between each image feature and all structured data features , calculate the second image feature weight for each image feature .

[0045] The step three includes the following contents: Step 301: Obtain image features and structured data features, and analyze the cosine similarity between each structured data feature and all image features , calculate the second structural feature weight of each structured data feature :

[0046] Cosine similarity is a mathematical method to measure the similarity between two vectors in multidimensional space. The core idea is to regard the vectors as rays starting from the origin and evaluate their similarity by calculating the cosine value of the angle between the two rays.

[0047] Step 302: Obtain image features and structured data features, and analyze the cosine similarity between each image feature and all structured data features , calculate the second image feature weight for each image feature :

[0048] The second structural feature weight of each corresponding image feature The calculation formula is as above.

[0049] When used, combine the contents in steps 301 and 302: Get image features and structured data features, and analyze the cosine similarity between each structured data feature and all image features , calculate the second structural feature weight of each structured data feature , and analyze the cosine similarity between each image feature and all structured data features , calculate the second image feature weight for each image feature , the impact on the overall feature set of different data types can be analyzed, thereby improving the accuracy and effectiveness of feature representation, helping subsequent analysis and processing processes to focus more on key information and improve overall efficiency.

[0050] Step 4: Obtain the first image feature weight of each image feature vector and the second image feature weight , calculate the comprehensive weight of image features , and obtain the first structural feature of each structured data feature vector and the second structural feature weight , calculate the comprehensive weight of structural features After alignment, the redundant image features or structural features that are ranked later are deleted, and the comprehensive weight of the image features is Starting from the largest image feature, the image feature is matched with the structural feature corresponding to the largest cosine similarity among the cosine similarities of all structured data features, and the above steps are repeated until all image features are matched.

[0051] The step 4 includes the following contents: Step 401: Obtain the first image feature weight of each image feature vector and the second image feature weight , calculate the comprehensive weight of image features :

[0052] The comprehensive weight of the corresponding image features The calculation formula is as above.

[0053] Step 402: Obtain the first structural feature of each structured data feature vector and the second structural feature weight , calculate the comprehensive weight of structural features :

[0054] The comprehensive weight of the corresponding structural features The calculation formula is as above.

[0055] Step 403: Obtain comprehensive weights of image features and the comprehensive weight of structural features , after sorting them from large to small, the numbers are aligned and the redundant image features or structural features sorted later are deleted.

[0056] Step 404: After the number of image features and structural features is consistent, the comprehensive weight of the image features is calculated. Starting from the largest image feature, the image feature is matched with the structural feature corresponding to the largest cosine similarity among the cosine similarities of all structured data features, and the above steps are repeated until all image features are matched.

[0057] When using, combine the contents in steps 401 to 404: Get the first image feature weight of each image feature vector and the second image feature weight , calculate the comprehensive weight of image features , and obtain the first structural feature of each structured data feature vector and the second structural feature weight , calculate the comprehensive weight of structural features After alignment, the redundant image features or structural features that are ranked later are deleted, and the comprehensive weight of the image features is Starting from the largest image feature, the image feature is matched with the structural feature corresponding to the largest cosine similarity among the cosine similarities of all structured data features, and the above steps are repeated until all image features are matched. This not only considers the similarity between features of the same type, but also the similarity between features of different types, which can promote the cross-domain integration between image data and structured data, and provide support for comprehensive data analysis and decision-making.

[0058] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. A person of ordinary skill in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution.

[0059] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0060] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A method for fusion analysis of image data and structured data based on artificial intelligence algorithm, characterized by: The steps include: Use various feature extraction algorithms to extract image features of image data, then use dimensionality reduction technology to reduce the high-dimensional feature vector to a low-dimensional space, and calculate the cosine similarity between each image feature vector in the low-dimensional space. , and calculate the first image feature weight of each image feature vector ; Use the NLP library to analyze structured data, extract structured text features, and calculate the cosine similarity between each structured text feature vector in a low-dimensional space. , and calculate the first structural feature weight of the structured text feature vector ; Get image features and structured data features, and analyze the cosine similarity between each structured data feature and all image features , calculate the second structural feature weight of each structured data feature , and analyze the cosine similarity between each image feature and all structured data features , calculate the second image feature weight for each image feature ; Get the first image feature weight of each image feature vector and the second image feature weight , calculate the comprehensive weight of image features , and obtain the first structural feature of each structured data feature vector and the second structural feature weight , calculate the comprehensive weight of structural features After alignment, the redundant image features or structural features that are ranked later are deleted, and the comprehensive weight of the image features is Starting from the largest image feature, the image feature is matched with the structural feature corresponding to the largest cosine similarity among the cosine similarities of all structured data features, and the above steps are repeated until all image features are matched.

2. The method for fusion analysis of image data and structured data based on artificial intelligence algorithm according to claim 1, characterized in that: Get the cosine similarity between each image feature vector , calculate the first image feature weight of each image feature vector : in, a and b Both represent the sequential numbers of image feature vectors, and a =1, 2, …, n, b =1, 2,…, n.

3. The method for fusion analysis of image data and structured data based on artificial intelligence algorithm according to claim 1, characterized in that: Get the cosine similarity between each structured text feature vector , calculate the first structural feature of the structured text feature vector : in, c and d Both represent the sequential numbers of image feature vectors, and c =1, 2, …, m, d =1, 2, …, m.

4. The method for fusion analysis of image data and structured data based on artificial intelligence algorithm according to claim 1, characterized in that: Get image features and structured data features, and analyze the cosine similarity between each structured data feature and all image features , calculate the second structural feature weight of each structured data feature : The second structural feature weight of each corresponding structured data feature The calculation formula is as above.

5. The method for fusion analysis of image data and structured data based on artificial intelligence algorithm according to claim 1, characterized in that: Get image features and structured data features, and analyze the cosine similarity between each image feature and all structured data features , calculate the second image feature weight for each image feature : The second structural feature weight of each corresponding image feature The calculation formula is as above.

6. The method for fusion analysis of image data and structured data based on artificial intelligence algorithm according to claim 5, characterized in that: Get the first image feature weight of each image feature vector and the second image feature weight , calculate the comprehensive weight of image features : The comprehensive weight of the corresponding image features The calculation formula is as above.

7. The method for fusion analysis of image data and structured data based on artificial intelligence algorithm according to claim 4, characterized in that: Get the first structural feature of each structured data feature vector and the second structural feature weight , calculate the comprehensive weight of structural features : The comprehensive weight of the corresponding structural features The calculation formula is as above.

8. The method for fusion analysis of image data and structured data based on artificial intelligence algorithm according to claim 7, characterized in that: Get the comprehensive weight of image features and the comprehensive weight of structural features , after sorting them from large to small, the numbers are aligned and the redundant image features or structural features sorted later are deleted.

9. The method for fusion analysis of image data and structured data based on artificial intelligence algorithm according to claim 8, characterized in that: After the number of image features and structural features is consistent, the comprehensive weight of image features is Starting from the largest image feature, the image feature is matched with the structural feature corresponding to the largest cosine similarity among the cosine similarities of all structured data features, and the above steps are repeated until all image features are matched.

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