WO2023016111A1

WO2023016111A1 - Key value matching method and apparatus, and readable medium and electronic device

Info

Publication number: WO2023016111A1
Application number: PCT/CN2022/101765
Authority: WO
Inventors: 赵田雨; 陈露露; 黄灿; 王长虎
Original assignee: 北京有竹居网络技术有限公司
Priority date: 2021-08-10
Filing date: 2022-06-28
Publication date: 2023-02-16
Also published as: CN113590857A

Abstract

The present disclosure relates to a key value matching method and apparatus, and a readable medium and an electronic device. The key value matching method comprises: acquiring, from an image to be subjected to detection, first position information of at least one piece of attribute data and second position information of at least one piece of attribute value data; generating a first relationship graph according to the first position information of the at least one piece of attribute data, and generating a second relationship graph according to the second position information of the at least one piece of attribute value data; and inputting, into a preset graph matching model, the first position information of the at least one piece of attribute data, the second position information of the at least one piece of attribute value data, the first relationship graph, the second relationship graph and said image, so as to obtain a matching relationship between the attribute data and the attribute value data. In this way, key value matching is performed by means of a preset graph matching model, such that key value matching can be performed for different scenarios, thereby improving the generalization of the model, and the accuracy of a key value matching result can also be effectively ensured.

Description

Key-value matching method, device, readable medium and electronic device

Cross References to Related Applications

This application is based on the Chinese patent application with the application number 202110915753.5 and the filing date on August 10, 2021, entitled "Key value matching method, device, readable medium and electronic equipment", and claims the priority of the Chinese patent application , the entire content of this Chinese patent application is hereby incorporated into this application as a reference.

technical field

The present disclosure relates to the technical field of image processing, and in particular, to a key-value matching method, device, readable medium, and electronic equipment.

Background technique

The key-value matching in the document image refers to the process of grouping and extracting the texts constituting the key-value relationship in the document image. For example, in the image of the business license, the name and a certain company form a key-value relationship; in the image of an ID card, the name and Zhang San form a key-value relationship; in the image of a graduation certificate, the school and a certain university form a key-value relationship Relationship, key-value matching is to identify and extract the key-value pairs that form this key-value relationship.

The current key-value matching methods are usually either based on the positional relationship between the key-value pairs, combining the results of text recognition with a preset relational dictionary for search and matching, or classifying and predicting the key-value pairs to be matched. However, no matter Whether it is relational dictionary lookup matching or classification prediction matching, both have poor generalization and can only be applied to specific scenarios.

Contents of the invention

This Summary is provided to introduce a simplified form of concepts that are described in detail later in the Detailed Description. This summary of the invention is not intended to identify key features or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

The disclosure provides a key-value matching method, device, readable medium and electronic equipment.

In a first aspect, the present disclosure provides a key-value matching method, the method comprising:

Acquiring first position information of at least one attribute data and second position information of at least one attribute value data in the image to be detected;

generating a first relationship graph according to the first location information of the at least one attribute data, and generating a second relationship graph according to the second location information of the at least one attribute value data;

Matching the first location information of the at least one attribute data, the second location information of the at least one attribute value data, the first relationship diagram, the second relationship diagram and the image to be detected model to obtain the matching relationship between the attribute data and the attribute value data.

In a second aspect, the present disclosure provides a key-value matching device, the device comprising:

A first acquiring module, configured to acquire first position information of at least one attribute data and second position information of at least one attribute value data in the image to be detected;

A relationship graph generation module, configured to generate a first relationship graph according to the first position information of the at least one attribute data, and generate a second relationship graph according to the second position information of the at least one attribute value data;

A determination module, configured to use the first location information of the at least one attribute data, the second location information of the at least one attribute value data, the first relationship diagram, the second relationship diagram and the image to be detected A preset map matching model is input to obtain a matching relationship between the attribute data and the attribute value data.

In a third aspect, the present disclosure provides a computer-readable medium on which a computer program is stored, and when the program is executed by a processing device, the steps of the method described in the first aspect above are realized.

In a fourth aspect, the present disclosure provides an electronic device, including:

a storage device on which a computer program is stored;

A processing device configured to execute the computer program in the storage device to implement the steps of the method described in the first aspect above.

In the above technical solution, by combining the first location information of the at least one attribute data, the second location information of the at least one attribute value data, and the first relationship diagram corresponding to the first location information of the at least one attribute data, the A second relationship graph corresponding to the second position information of the at least one attribute value data, and the image to be detected is input into a preset graph matching model, so that the preset graph matching model outputs the attribute data and the attribute In this way, key-value matching through the preset graph matching model can not only perform key-value matching for different scenarios, improve the generalization of the model, but also effectively ensure the accuracy of key-value matching results.

Other features and advantages of the present disclosure will be described in detail in the detailed description that follows.

Description of drawings

The above and other features, advantages and aspects of the various embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic and that elements and elements are not necessarily drawn to scale. In the attached picture:

FIG. 1 is a flow chart of a key-value matching method shown in an exemplary embodiment of the present disclosure;

Fig. 2 is a schematic flow chart of a key-value matching method shown in an exemplary embodiment of the present disclosure;

Fig. 3 is a flow chart of a key-value matching method shown according to the embodiment shown in Fig. 1;

Fig. 4 is the flow chart of another kind of key-value matching method shown according to the embodiment shown in Fig. 1;

Fig. 5 is a block diagram of a key value matching device shown in another exemplary embodiment of the present disclosure;

Fig. 6 is a block diagram of a key-value matching device according to the embodiment shown in Fig. 5;

Fig. 7 is a block diagram of an electronic device shown in an exemplary embodiment of the present disclosure.

Detailed ways

Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the drawings, it should be understood that the disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein; A more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for exemplary purposes only, and are not intended to limit the protection scope of the present disclosure.

It should be understood that the various steps described in the method implementations of the present disclosure may be executed in different orders, and/or executed in parallel. Additionally, method embodiments may include additional steps and/or omit performing illustrated steps. The scope of the present disclosure is not limited in this respect.

As used herein, the term "comprise" and its variations are open-ended, ie "including but not limited to". The term "based on" is "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one further embodiment"; the term "some embodiments" means "at least some embodiments." Relevant definitions of other terms will be given in the description below.

It should be noted that concepts such as "first" and "second" mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the sequence of functions performed by these devices, modules or units or interdependence.

It should be noted that the modifications of "one" and "multiple" mentioned in the present disclosure are illustrative and not restrictive, and those skilled in the art should understand that unless the context clearly indicates otherwise, it should be understood as "one or more" multiple".

The names of messages or information exchanged between multiple devices in the embodiments of the present disclosure are used for illustrative purposes only, and are not used to limit the scope of these messages or information.

Before introducing the specific implementation of the present disclosure, the application scenarios of the present disclosure will be explained as follows. The present disclosure can be applied to the process of identifying and extracting key-value pairs in a document image, where the document image can be a business license Image, degree certificate image, graduation certificate image, ID card image and other document images. The key-value pair refers to a set of texts with a key-value relationship. For example, in a business license image, the name and a certain company form a key-value relationship. It belongs to a key-value pair; in the ID card image, the name and Zhang San form a key-value relationship and belong to a key-value pair; in the graduation certificate image, the school and a certain university form a key-value relationship and belong to a key-value pair.

In related technologies, the key-value matching method is either to search and match the result of text recognition with the preset relational dictionary according to the positional relationship between the key-values. The value pair data is less, and the scene involved is relatively single, resulting in the situation that the matching key value cannot be found in the dictionary; or the data in the processed key value pair is divided into n categories, and a multi-label detection model is used To detect the data in the image, this method usually needs to train different multi-label detection models for different scenarios, and the multi-label detection model in each scene requires a large amount of multi-label annotation data, that is, training the multi-label The label detection model needs to invest more labeling costs, and the generalization of the obtained multi-label detection model is weak, which can only be applied to specific scenarios, and cannot be applied to other scenarios other than specific scenarios. Obviously, both relational dictionary lookup matching and classification prediction matching have poor generalization and can only be applied to specific scenarios.

In order to solve the above technical problems, the present disclosure provides a key value matching method, device, readable medium and electronic equipment. The key value matching method obtains the first position information and at least one attribute data of at least one attribute data in the image The second location information of the value data; generate a first relationship diagram according to the first location information of the at least one attribute data, and generate a second relationship diagram according to the second location information of the at least one attribute value data; the at least one attribute data The first position information of the at least one attribute value data, the second position information of the at least one attribute value data, the first relationship graph, the second relationship graph and the image to be detected are input into the preset graph matching model to obtain the attribute data and the attribute value data matching relationship. In this way, performing key-value matching through the preset graph matching model can not only perform key-value matching for different scenarios, ensure the generalization of the model, but also effectively ensure the accuracy of key-value matching results.

Embodiments of the present disclosure will be described in detail below in conjunction with specific drawings.

Fig. 1 is a flow chart of a key-value matching method shown in an exemplary embodiment of the present disclosure; referring to Fig. 1, the method may include:

Step 101, acquiring first position information of at least one attribute data and second position information of at least one attribute value data in an image to be detected.

Wherein, the image to be detected may be a business license image, a degree certificate image, a graduation certificate image, an ID card image and other certificate images. The attribute data is the data corresponding to the Key in the key-value pair, the attribute value data is the data corresponding to the Value in the key-value pair, and the Key and the Value form a key-value pair.

In this step, the category and position of the attribute data and attribute value data in the image to be detected can be detected through the binary classification detection model in the prior art, so as to output the corresponding first attribute data of each attribute data in the image to be detected. A detection frame and a second detection frame corresponding to each attribute value data, the first position information is determined according to the first detection frame, and the second position information is determined according to the second detection frame. For example, in one embodiment: the position of the first detection frame can be used as the first position information, and the second detection frame can be used as the second position information; in another embodiment, the first The center position of the detection frame is used as the first position information, and the center position of the second detection frame is used as the second position information; in another embodiment, any point on the first detection frame can be used as the first position information. For location information, any point on the second detection frame is used as the second location information.

Step 102: Generate a first relationship diagram according to the first location information of the at least one attribute data, and generate a second relationship diagram according to the second location information of the at least one attribute value data.

Wherein, the first relationship diagram includes the attribute node corresponding to the location of each attribute data and the first connection between different attribute nodes, and the second relationship diagram includes the attribute value node corresponding to the location of each attribute value data , and a second link between nodes with different attribute values.

In this step, the first relational graph may be generated by means of Delaunay triangulation mapping according to the first position information of the at least one attribute data; by means of fully connected mapping according to the second position information of the at least one attribute value data The second relationship graph is generated.

It should be noted that the Delaunay triangulation method (Delaunay triangulation, Delaunay triangulation algorithm) and the fully-connected graph-building method (that is, the establishment of a fully-connected network topology map) are commonly used in the prior art. Figures, the present disclosure will not repeat them here.

Step 103, inputting the first location information of the at least one attribute data, the second location information of the at least one attribute value data, the first relationship graph, the second relationship graph and the image to be detected into a preset graph matching model, A matching relationship between the attribute data and the attribute value data is obtained.

Wherein, the preset image matching model is trained through the following steps S1 to S3:

S1, acquire model training sample data.

Wherein, the model training sample data includes a plurality of image samples to be detected, the first position information of each attribute data in each image sample to be detected, and the second position information of each attribute value data in the image samples to be detected information, and a first relational graph and a second relational graph corresponding to each image sample to be detected.

It should be noted that the image sample to be detected includes category annotations for attribute data and attribute value data, and annotation data for the corresponding relationship between attribute data and attribute value data.

S2, according to the first position information of each attribute data, obtain the first feature corresponding to each attribute node in the first relationship graph of the image sample to be detected and each first feature corresponding to the first relationship graph of the image sample to be detected through the preset network initial model One connects the corresponding second feature, and obtains each attribute value in the second relationship diagram of the image sample to be detected from the image sample to be detected through the preset network initial model according to the second position information of each attribute value data The third feature corresponding to the node and the fourth feature corresponding to each second link.

Wherein, the preset initial network model may include a first initial subnetwork and a second initial subnetwork, and the preset initial network model may be a neural network model or other machine learning algorithm models.

In this step, the first position information of at least one attribute data corresponding to the image sample to be detected, the first relationship graph and the image to be detected can be used as the input of the first initial subnetwork to output the image sample to be detected Corresponding to the first feature corresponding to each attribute node, and using the first position information of at least one attribute data corresponding to the image sample to be detected, the first relationship graph and the image to be detected as the input of the second initial subnetwork , to output the second feature corresponding to each first connection line in the image sample to be detected; the second position information of at least one attribute value data corresponding to the image sample to be detected, the second relationship diagram and the to be detected The image is used as the input of the first initial sub-network to output the third feature corresponding to each attribute value node in the image sample to be detected, and the second position of at least one attribute value data corresponding to the image sample to be detected Information, the second relational graph and the image to be detected are used as inputs of the second initial subnetwork to output the fourth feature corresponding to each second link in the image sample to be detected.

Wherein, the depth of the second initial subnetwork is greater than the depth of the first initial subnetwork, that is, the number of hidden layers of the first initial subnetwork is less than that of the second initial subnetwork, and the first feature corresponding to the attribute node and the The third feature corresponding to the attribute value node is output by the shallow network (the first initial sub-network), the second feature corresponding to the first connection and the fourth feature corresponding to the second connection are output by the deep network (the second initial sub-network). network) output.

It should be noted that firstly, the first feature and the third feature are obtained by the shallow network, and the second feature and the fourth feature are obtained by the deep network, so that the attribute node, the attribute value node, and the first connected feature can be effectively obtained. line, the image feature corresponding to the second connection line, and secondly, because the shallower the network depth, the closer the distance to the input, the more feature details it contains, the better the description of the extracted feature to the node (the attribute node or attribute value node) Accurate, therefore, can effectively guarantee the accuracy of the first feature of the mentioned attribute node, and the accuracy of the third feature of the attribute value node, which is conducive to providing a reliable data basis for key-value matching; extracting edges through the deep network (that is, the first The features of the first connection and the second connection) can effectively reduce the amount of data processing and improve the efficiency of model processing.

In addition, a pooling layer may be included in the first initial subnetwork and the second initial subnetwork, and when obtaining the first feature, the second feature, the third feature, and the fourth feature, the pooling layer may be Perform average pooling on the extracted features. For example, the shape of the feature map is 256×64×64 (the number of channels is 256, the length is 64, and the width is 64). When extracting features, the detection frame corresponding to the node can be scaled proportionally, and then Extract features at the corresponding positions, and then average pool the extracted features through the pooling layer to obtain the feature vector (256×1×1) of each detection frame. In this way, the data dimension can be effectively reduced, which can help simplify the network complexity, reduce the calculation amount of the model, and achieve the technical effect of improving the efficiency of the model.

S3, according to the first feature corresponding to each attribute node in the first relationship graph of the image sample to be detected and the second feature corresponding to each first connection, and each attribute in the second relationship graph of the image sample to be detected The third feature corresponding to the value node and the fourth feature corresponding to each second connection line, calculate the loss value corresponding to the distance vector between each attribute node and the attribute value node to be matched through the preset loss function, according to the loss value pair The preset initial network model is iteratively trained to obtain the preset graph matching model.

In this step, the node similarity matrix can be determined according to the first feature corresponding to each of the attribute nodes and the third feature corresponding to each of the attribute value nodes, and according to the second feature corresponding to each of the first connections The feature and the fourth feature corresponding to each of the second connections determine the connection similarity matrix; generate a target relationship matrix according to the node similarity matrix and the connection similarity matrix; obtain the pair corresponding to the target relationship matrix A random matrix; determine a distance vector between each attribute node and the attribute value node to be matched according to the double random matrix; determine the loss value through a preset loss function according to the distance vector.

Exemplarily, the first relational graph may be represented by the first adjacency matrix A1, the second relational graph may be represented by the second adjacency matrix A2, and the correlation matrices corresponding to the first adjacency matrix A1 are determined by the formula A=GH ^T as G ₁ and H ₁ , and the incidence matrices corresponding to the second adjacency matrix A2 are G ₂ and H ₂ respectively. When it is determined in step S2 that the first feature is P ₁ , the second feature is E ₁ , the third feature is P ₂ , and the fourth feature is E ₂ , it can be obtained by M _p =P ₁ P ₂ The node similarity matrix M _p , and the connection similarity matrix M _e is determined by the following formula 1:

M _e ＝[E ₁ G ₁ |E ₁ H ₁ ]Λ[E ₂ G ₂ |E ₂ H ₂ ] ^T ... Formula 1

In the above formula 1, the Λ can be a symmetric parameter matrix;

Then, the target relationship matrix M can be determined according to the node similarity matrix M _p and the connection similarity matrix M _e by the following formula 2:

In the above formula 2: vec(x) represents the row-wise expansion of x, [x] represents the diagonal matrix of x,

For Kronecker product, Kronecker product.

Further, the eigenvector V corresponding to the target relationship matrix M can be obtained, and then the eigenvector is double-randomized to obtain the double random matrix S corresponding to the eigenvector V, and the attribute is determined by the following formula 3 according to the double random matrix A vector of distances from the node to each attribute value node:

In the above formula 3, α is a preset coefficient, for example, it can be 200, S is a double random matrix, i represents the row number of the double random matrix S, j represents the column number of the double random matrix S, S(i, 1...m) Represents the i-th row of the double random matrix S, the double random matrix S has m rows, P is the location set of attribute value nodes, P _i is the location of the i-th attribute node,

Characterizes the weight of the feature of the i-th attribute value node relative to the position set P of the attribute value node.

Next, the loss value corresponding to the distance vector between each attribute node and the attribute value node to be matched can be calculated by the following preset loss function L(d), wherein the preset loss function is as follows:

In the above loss function,

In order to obtain the distance vector of the target calculated according to the location of the marked attribute data and the location of the corresponding attribute value data, ∈ is a random decimal number.

During the training process, the loss value corresponding to the distance vector between each attribute node and the attribute value node to be matched can be obtained. When the loss value is less than or equal to the preset loss value threshold, it is determined that the model training is over and the optimal Preset graph matching model.

The preset image matching model obtained through the above training method has strong generalization and can be applied to many different key-value matching scenarios. For example, it can be used for key-value matching of ID card images, and can also be applied Key-value matching in multiple scenarios such as business license images and degree certificate images.

For example, as shown in FIG. 2 , FIG. 2 is a schematic flow chart of a key-value matching method shown in an exemplary embodiment of the present disclosure. In FIG. 2 , images 1 to 4 are included, and the image 1 is an image to be detected. , the image to be detected includes attribute data Key1 and Key2, and attribute value data Value1 and Value2, respectively obtain the positions of Key1, Key2, Value1, and Value2 in image 1 through the binary classification detection model, thus obtaining the detection in image 2 Frame, each detection frame in image 2 represents the position of the identified attribute data or attribute value data. After matching the model through the preset image, Key1 matches Value1, and Key2 matches Value2 (3 in Figure 2). The text information Key1:Value1 and Key2:Value2 (4 in FIG. 2 ) can be obtained through character recognition according to the matching relationship. In this way, the key-value matching is performed through the preset graph matching model, which can effectively ensure the accuracy of the key-value matching result.

In the above technical solution, through the first position information of the at least one attribute data, the second position information of the at least one attribute value data, the first relationship diagram corresponding to the first position information of the at least one attribute data, the at least one attribute The second relationship graph corresponding to the second position information of the value data, and the image to be detected are input into the preset graph matching model, so that the preset graph matching model outputs the matching relationship between the attribute data and the attribute value data, thus, Using the preset graph matching model for key-value matching can not only perform key-value matching for different scenarios, improve the generalization of the model, but also effectively ensure the accuracy of key-value matching results.

Further, the preset map matching model in step 103 in FIG. 1 is based on the first location information of the at least one attribute data, the second location information of the at least one attribute value data, and the first relationship through the following steps shown in FIG. Fig. 1, the second relationship diagram and the image to be detected determine the matching relationship between the attribute data and the attribute value data, and Fig. 3 is a flow chart of a key-value matching method according to the embodiment shown in Fig. 1, see Fig. 3 , the preset graph matching model can be used for:

Step 1031, according to the first position information of the at least one attribute data, obtain the first feature corresponding to each attribute node in the first relationship diagram and the second feature corresponding to each first connection line from the image to be detected , and according to the second position information of the at least one attribute value data, the third feature corresponding to each attribute value node in the second relationship graph and the fourth feature corresponding to each second connection are obtained from the image to be detected .

In this step, the first position information of the at least one attribute data, the first relationship graph and the image to be detected can be used as the input of the first sub-network to output the first feature corresponding to each attribute node, and The first position information of the at least one attribute data, the first relationship graph and the image to be detected are used as the input of the second sub-network to output the second feature corresponding to each first connection line, wherein the first The number of hidden layers of a sub-network is less than that of the second sub-network; the second position information of the at least one attribute value data, the second relationship diagram and the image to be detected are used as the input of the first sub-network, and the output is obtained The third feature corresponding to each attribute value node, and the second position information of the at least one attribute value data, the second relationship graph and the image to be detected are used as the input of the second sub-network to output each item of the first sub-network The fourth feature corresponding to the two connecting lines.

It should be noted that the number of hidden layers of the first subnetwork is less than that of the second subnetwork, that is, the depth of the second subnetwork is greater than the depth of the first subnetwork, and the first feature corresponding to the attribute node and the attribute The third feature corresponding to the value node is output by the first sub-network (that is, a shallow network with fewer hidden layers), and the second feature corresponding to the first connection and the fourth feature corresponding to the second connection are output by the first sub-network. The output of the second sub-network (that is, a deep network with more hidden layers). Since the shallower the depth of the network, the closer it is to the input, the more feature details it contains, the more accurate the description of the extracted feature to the node (the attribute node or attribute value node), so the first feature and the attribute value node are output through the shallow network The third feature can effectively guarantee the accuracy of the first feature and the third feature, which is conducive to providing a reliable data basis for key-value matching; extracting edges (ie, the first connection and the second connection) through the deep network Features can effectively reduce the amount of data processing and improve the efficiency of model processing.

Step 1032, according to the first characteristic corresponding to each attribute node and the second characteristic corresponding to each first connection, and the third characteristic corresponding to each attribute value node and the first characteristic corresponding to each second connection The four features determine the matching relationship between the attribute data and the attribute value data.

In this step, a possible implementation may include the following steps shown in S21 to S23:

S21. Determine a node similarity matrix according to the first feature corresponding to each attribute node and the third feature corresponding to each attribute value node.

In this step, when the first feature corresponding to the attribute node is P ₁ , and the third feature corresponding to the attribute value node is P ₂ , the node similarity matrix M can be obtained by M _p =P ₁ P ₂ _p .

S22. Determine a connection similarity matrix according to the second feature corresponding to each first connection and the fourth feature corresponding to each second connection.

In this step, the first characteristic corresponding to the attribute node is P ₁ , the third characteristic corresponding to the attribute value node is P ₂ , the second characteristic corresponding to the first connection is E ₁ , and the second connection corresponds to In the case where the fourth feature of is E ₂ , the connection similarity matrix M _e can be determined by the following formula:

M _e ＝[E ₁ G ₁ |E ₁ H ₁ ]Λ[E ₂ G ₂ |E ₂ H ₂ ] ^T

In the above formula, the Λ may be a symmetric parameter matrix, for example, may be a 2×2 symmetric parameter matrix.

S23. Determine a matching relationship between each of the attribute data and each of the attribute value data according to the node similarity matrix and the link similarity matrix.

In this step, the target relationship matrix M can be determined according to the node similarity matrix M _p and the connection similarity matrix M _e by the following formula:

In the above formula: vec(x) represents the row-wise expansion of x, [x] represents the diagonal matrix of x,

For Kronecker product, Kronecker product.

After the target relationship matrix M is obtained, the feature vector V corresponding to the target relationship matrix M can be obtained, and the matching relationship between each attribute data and each attribute value data can be determined according to the feature vector V.

Optionally, in this step, the feature vector V may continue to be double-randomized to obtain a double-randomized matrix S, and the matching relationship between each attribute data and each attribute value data is determined according to the double-randomized matrix S. Wherein, the process of performing double randomization processing belongs to the prior art. For the process of performing double randomization processing on the feature vector V, reference may be made to the implementation manners in the prior art, which will not be repeated in this disclosure.

For example, if the image to be detected includes 3 attribute data (respectively Key1, Key2, Key3) and 3 attribute value data (respectively Value1, Value2, Value3), if the obtained double random matrix S is:

Wherein, the rows of the matrix represent Key1, Key2, and Key3, and the columns of the matrix represent Value1, Value2, and Value3, which means that the Key1 matches the Value1, the Key2 matches the Value3, and the Key3 matches the Value2.

The above technical solution can determine the relationship between the attribute data and the image to be detected according to the first position information of the at least one attribute data, the second position information of the at least one attribute value data, the first relationship diagram, the second relationship diagram and the image to be detected. The matching relationship of the attribute value data can effectively guarantee the accuracy of the key-value matching result.

Optionally, before generating the first relationship diagram according to the first location information of the at least one attribute data and generating the second relationship diagram according to the second location information of the at least one attribute value data described in step 102 in FIG. 1 , The method may also include the following steps shown in FIG. 4. FIG. 4 is a flowchart of another key-value matching method according to the embodiment shown in FIG. 1. As shown in FIG. 4, the method may also include:

Step 104, acquiring the first quantity of the attribute data and the second quantity of the attribute value data.

For example, the first quantity of the attribute data may be the number of detection boxes of Key in the image to be detected, and the second quantity of the attribute value data may be the number of detection boxes of Value in the image to be detected.

Step 105, in the case that the first quantity is not equal to the second quantity, by adding the same attribute data at least one position where the attribute data is located, or adding the same attribute data at at least one position where the attribute value data is located attribute value data such that the attribute data is equal in quantity to the attribute value data.

In this step, a possible implementation method includes: when the first quantity is less than the second quantity, adding the same attribute data at the location of each attribute data, and obtaining twice the first quantity and the second quantity The first difference of the second quantity is obtained from at least one of the attribute value data corresponding to the number of target attribute value data of the first difference, and an identical attribute value is added at each position of the target attribute value data data, to obtain twice the first quantity of attribute data, and twice the first quantity of attribute value data;

Correspondingly, according to the above implementation, the implementation of step 102 in FIG. 1 may include: generating a first relationship diagram according to the first location information corresponding to the twice the first amount of attribute data; The second position information corresponding to the quantity of attribute value data generates a second relationship graph.

For example, if the first quantity of attribute data is a, and the second quantity of attribute value data is b, in the case where a is less than b, directly add a piece of data at the position of each attribute data in the image to be detected, namely 2a attribute data are obtained, and for attribute value data, 2a-b attribute value data are randomly selected in the image to be detected, and an attribute value data is sequentially added to the position of these attribute value data. Thus, 2a attribute data and 2a attribute value data are obtained.

In this step, another possible implementation method includes: when the first number is greater than the second number, add an identical attribute value data at the location of each attribute value data, and obtain twice the second The second difference between the quantity and the first quantity, and obtain the target attribute data corresponding to the second difference from at least one of the attribute data, and add the same attribute data at the position of the target attribute data, so as to Twice the second amount of attribute data, and twice the second amount of attribute value data are obtained.

Correspondingly, according to the above implementation, the implementation of step 102 in FIG. 1 may include: generating a first relationship diagram according to the first position information corresponding to the twice the second amount of attribute data; The second position information corresponding to the quantity of attribute value data generates a second relationship diagram.

For example, if the first quantity of attribute data is a, and the second quantity of attribute value data is b, in the case that a is greater than b, directly add a piece of data at the position of each attribute value data in the image to be detected, That is, 2b attribute value data are obtained. For attribute data, 2b-a attribute data are randomly selected in the image to be detected, and one attribute data is sequentially added to the position of these attribute data. Thus, 2b attribute data and 2b attribute value data are obtained.

The above technical solutions can effectively ensure that the attribute data is equal to the attribute value data, thereby ensuring that each attribute data can be matched to the corresponding attribute value data, which is conducive to expanding the range of images to be detected that can be processed, expanding the The scope of the key-value matching method.

Fig. 5 is a block diagram of a key value matching device shown in another exemplary embodiment of the present disclosure; referring to Fig. 5, the key value matching device may include:

A first acquiring module 401, configured to acquire first position information of at least one attribute data and second position information of at least one attribute value data in the image to be detected;

A relationship diagram generation module 402, configured to generate a first relationship diagram according to the first location information of the at least one attribute data, and generate a second relationship diagram according to the second location information of the at least one attribute value data;

A determining module 403, configured to input the first position information of the at least one attribute data, the second position information of the at least one attribute value data, the first relationship diagram, the second relationship diagram and the image to be detected into a preset diagram Match the model to obtain the matching relationship between the attribute data and the attribute value data.

In the above technical solution, by using the first position information of the at least one attribute data, the second position information of the at least one attribute value data, and the first relationship diagram corresponding to the first position information of the at least one attribute data, the at least one attribute The second relationship graph corresponding to the second position information of the value data, and the image to be detected are input into the preset graph matching model, so that the preset graph matching model outputs the matching relationship between the attribute data and the attribute value data, thus, Using the preset graph matching model for key-value matching can not only perform key-value matching for different scenarios, improve the generalization of the model, but also effectively ensure the accuracy of key-value matching results.

Optionally, the first relationship diagram includes the attribute node corresponding to the location of each attribute data and the first connection between different attribute nodes, and the second relationship diagram includes the attribute corresponding to the location of each attribute value data Value nodes, and the second connection between different attribute value nodes, the preset graph matching model is used for:

According to the first position information of the at least one attribute data, the first feature corresponding to each attribute node in the first relationship graph and the second feature corresponding to each first connection line are obtained from the image to be detected, and according to The second position information of the at least one attribute value data obtains the third feature corresponding to each attribute value node in the second relationship graph and the fourth feature corresponding to each second connection from the image to be detected;

Determine according to the first feature corresponding to each attribute node and the second feature corresponding to each first connection, and the third feature corresponding to each attribute value node and the fourth feature corresponding to each second connection The matching relationship between the attribute data and the attribute value data.

Optionally, the preset graph matching model includes a first subnetwork and a second subnetwork, and the preset graph matching model is used for:

The first position information of the at least one attribute data, the first relationship graph and the image to be detected are used as the input of the first sub-network to output the first feature corresponding to each attribute node, and the at least one attribute The first location information of the data, the first relationship graph and the image to be detected are used as the input of the second subnetwork to output the second feature corresponding to each first connection line, wherein the hidden of the first subnetwork the number of layers is less than the second subnetwork;

The second position information of the at least one attribute value data, the second relationship graph and the image to be detected are used as the input of the first sub-network to output the third feature corresponding to each attribute value node, and the The second position information of at least one attribute value data, the second relationship diagram and the image to be detected are used as inputs of the second sub-network to output the fourth feature corresponding to each second connection line.

Optionally, the preset map matching model is used for:

Determine the node similarity matrix according to the first feature corresponding to each attribute node and the third feature corresponding to each attribute value node;

determining a connection similarity matrix according to the second feature corresponding to each of the first connections and the fourth feature corresponding to each of the second connections;

A matching relationship between each attribute data and each attribute value data is determined according to the node similarity matrix and the connection similarity matrix.

Optionally, the relationship graph generation module is used for:

generating the first relationship graph by means of Delaunay triangulation mapping according to the first position information of the at least one attribute data;

The second relational graph is generated by a fully connected graphing method according to the second position information of the at least one attribute value data.

Fig. 6 is a block diagram of a key-value matching device according to the embodiment shown in Fig. 5; referring to Fig. 6, the device may also include:

The second acquiring module 404 is configured to acquire the first quantity of the attribute data and the second quantity of the attribute value data;

A data adding module 405, configured to add the same attribute data at least one location where the attribute data is located, or at least one location where the attribute value data is located when the first quantity is not equal to the second quantity Add the same attribute value data at the place, so that the quantity of the attribute data and the attribute value data is equal.

Optionally, the data addition module is used for:

In the case that the first quantity is less than the second quantity, add the same attribute data at the position of each attribute data, and obtain twice the first difference between the first quantity and the second quantity, from at least Obtain the target attribute value data corresponding to the first difference from one attribute value data, and add the same attribute value data at each position of the target attribute value data to obtain twice the first amount of attribute data , and twice the first amount of attribute value data;

Correspondingly, the relationship graph generation module is used for:

generating a first relationship diagram according to the first location information corresponding to the twice the first quantity of attribute data;

A second relationship graph is generated according to the second position information corresponding to the twice the first quantity of attribute value data.

Optionally, the data augmentation module is used for:

When the first quantity is greater than the second quantity, add the same attribute value data at the position of each attribute value data, obtain twice the second difference between the second quantity and the first quantity, and Acquire the target attribute data corresponding to the second difference from at least one attribute data, and add the same attribute data at the location of the target attribute data to obtain twice the second amount of attribute data, and twice a second quantity of attribute value data;

Correspondingly, correspondingly, the relationship graph generation module is used for:

generating a first relationship diagram according to the first position information corresponding to the twice the second quantity of attribute data;

A second relationship graph is generated according to the second location information corresponding to the twice the second quantity of attribute value data.

Optionally, the preset image matching model is trained through the following steps:

Acquiring model training sample data, the model training sample data includes a plurality of image samples to be detected, the first position information of each attribute data in each image sample to be detected, and each attribute value data in the image sample to be detected The second position information, and the first relationship diagram and the second relationship diagram corresponding to each image sample to be detected;

According to the first position information of each attribute data, the first feature corresponding to each attribute node and each first connection in the first relationship diagram of the image sample to be detected are obtained from the image sample to be detected through a preset network initial model. corresponding to the second feature, and according to the second position information of each attribute value data, each attribute value node in the second relationship diagram of the image sample to be detected is obtained from the image sample to be detected through the preset network initial model corresponding to The third feature of and the fourth feature corresponding to each second connecting line;

According to the first feature corresponding to each attribute node in the first relationship graph of the image sample to be detected and the second feature corresponding to each first connection, and each attribute value node in the second relationship graph of the image sample to be detected For the corresponding third feature and the fourth feature corresponding to each second connection line, calculate the loss value corresponding to the distance vector between each attribute node and the attribute value node to be matched through the preset loss function, according to the loss value. The initial network model is set to perform iterative training to obtain the preset image matching model.

Optionally, according to the first feature corresponding to each attribute node in the first relationship graph of the image sample to be detected and the second feature corresponding to each first connection, and the second feature of the image sample to be detected The third feature corresponding to each attribute value node in the relationship diagram and the fourth feature corresponding to each second connection line, calculate the loss value corresponding to the distance vector between each attribute node and the attribute value node to be matched through the preset loss function ,include:

Determine the node similarity matrix according to the first feature corresponding to each attribute node and the third feature corresponding to each attribute value node, and determine the node similarity matrix according to the second feature corresponding to each of the first connections and each of the attribute value nodes The fourth feature corresponding to the second connection determines the connection similarity matrix;

generating a target relationship matrix according to the node similarity matrix and the connection similarity matrix;

Obtain the double random matrix corresponding to the target relationship matrix;

Determine the distance vector between each attribute node and the attribute value node to be matched according to the double random matrix;

The loss value is determined through a preset loss function according to the distance vector.

Referring now to FIG. 7 , it shows a schematic structural diagram of an electronic device 600 suitable for implementing the embodiments of the present disclosure. The terminal equipment in the embodiment of the present disclosure may include but not limited to such as mobile phone, notebook computer, digital broadcast receiver, PDA (personal digital assistant), PAD (tablet computer), PMP (portable multimedia player), vehicle terminal (such as mobile terminals such as car navigation terminals) and fixed terminals such as digital TVs, desktop computers and the like. The electronic device shown in FIG. 7 is only an example, and should not limit the functions and application scope of the embodiments of the present disclosure.

As shown in FIG. 7, an electronic device 600 may include a processing device (such as a central processing unit, a graphics processing unit, etc.) 601, which may be randomly accessed according to a program stored in a read-only memory (ROM) 602 or loaded from a storage device 608. Various appropriate actions and processes are executed by programs in the memory (RAM) 603 . In the RAM 603, various programs and data necessary for the operation of the electronic device 600 are also stored. The processing device 601, ROM 602, and RAM 603 are connected to each other through a bus 604. An input/output (I/O) interface 605 is also connected to the bus 604 .

Typically, the following devices can be connected to the I/O interface 605: input devices 606 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; including, for example, a liquid crystal display (LCD), speaker, vibration an output device 607 such as a computer; a storage device 608 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 609. The communication means 609 may allow the electronic device 600 to communicate with other devices wirelessly or by wire to exchange data. While FIG. 7 shows electronic device 600 having various means, it should be understood that implementing or having all of the means shown is not a requirement. More or fewer means may alternatively be implemented or provided.

In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product, which includes a computer program carried on a non-transitory computer readable medium, where the computer program includes program code for executing the method shown in the flowchart. In such an embodiment, the computer program may be downloaded and installed from a network via communication means 609, or from storage means 608, or from ROM 602. When the computer program is executed by the processing device 601, the above-mentioned functions defined in the methods of the embodiments of the present disclosure are performed.

It should be noted that the above-mentioned computer-readable medium in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. A computer readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, electrical connections with one or more wires, portable computer diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable Programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present disclosure, however, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave carrying computer-readable program code therein. Such propagated data signals may take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which can transmit, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device . Program code embodied on a computer readable medium may be transmitted by any appropriate medium, including but not limited to wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

In some embodiments, the server can communicate with any currently known or future-developed network protocol such as HTTP (HyperText Transfer Protocol, Hypertext Transfer Protocol), and can communicate with digital data in any form or medium (such as , communication network) interconnection. Examples of communication networks include local area networks ("LANs"), wide area networks ("WANs"), internetworks (e.g., the Internet), and peer-to-peer networks (e.g., adhoc peer-to-peer networks), as well as any currently known or future developed network.

The above-mentioned computer-readable medium may be included in the above-mentioned electronic device, or may exist independently without being incorporated into the electronic device.

The above-mentioned computer-readable medium carries one or more programs, and when the above-mentioned one or more programs are executed by the electronic device, the electronic device: acquires the first position information and at least one attribute of at least one attribute data in the image to be detected The second location information of the value data; generate a first relationship diagram according to the first location information of the at least one attribute data, and generate a second relationship diagram according to the second location information of the at least one attribute value data; The first position information of one attribute data, the second position information of the at least one attribute value data, the first relationship graph, the second relationship graph and the image to be detected are input into a preset graph matching model to obtain the The matching relationship between the attribute data and the attribute value data. .

Computer program code for carrying out operations of the present disclosure may be written in one or more programming languages, or combinations thereof, including but not limited to object-oriented programming languages—such as Java, Smalltalk, C++, and Includes conventional procedural programming languages - such as "C" or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (for example, using an Internet service provider to connected via the Internet).

The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, program segment, or portion of code that contains one or more logical functions for implementing specified executable instructions. It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or they may sometimes be executed in the reverse order, depending upon the functionality involved. It should also be noted that each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, can be implemented by a dedicated hardware-based system that performs the specified functions or operations , or may be implemented by a combination of dedicated hardware and computer instructions.

The modules involved in the embodiments described in the present disclosure may be implemented by software or by hardware. Wherein, the name of the module does not constitute a limitation of the module itself under certain circumstances, for example, the first acquisition module can also be described as "obtaining the first position information and at least one attribute data of at least one attribute data in the image to be detected Second location information for value data".

The functions described herein above may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), System on Chips (SOCs), Complex Programmable Logical device (CPLD) and so on.

In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media would include one or more wire-based electrical connections, portable computer discs, hard drives, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM or flash memory), optical fiber, compact disk read only memory (CD-ROM), optical storage, magnetic storage, or any suitable combination of the foregoing.

According to one or more embodiments of the present disclosure, Example 1 provides a key-value matching method, the method comprising:

generating a first relationship diagram according to the first location information of the at least one attribute data, and generating a second relationship diagram according to the second location information of the at least one attribute value data;

According to one or more embodiments of the present disclosure, Example 2 provides the method of Example 1, the first relationship graph includes the attribute node corresponding to the location of each attribute data and the first connection between different attribute nodes line, the second relationship graph includes an attribute value node corresponding to the location of each attribute value data, and a second connection between different attribute value nodes, and the preset graph matching model is used for:

According to the first position information of the at least one attribute data, the first feature corresponding to each attribute node in the first relationship graph and the first feature corresponding to each first connection line are obtained from the image to be detected. two features, and according to the second position information of the at least one attribute value data, obtain the third feature corresponding to each of the attribute value nodes in the second relationship diagram and each of the first item from the image to be detected The fourth feature corresponding to the two connections;

According to the first feature corresponding to each of the attribute nodes and the second feature corresponding to each of the first connections, and the third feature corresponding to each of the attribute value nodes and the corresponding to each of the second connections The fourth feature determines the matching relationship between the attribute data and the attribute value data.

According to one or more embodiments of the present disclosure, Example 3 provides the method of Example 2, the preset graph matching model includes a first sub-network and a second sub-network, and the first sub-network according to the at least one attribute data The location information acquires the first feature corresponding to each attribute node in the first relationship graph and the second feature corresponding to each first connection line from the image to be detected, and according to the at least one attribute The second position information of the value data acquires the third feature corresponding to each attribute value node in the second relationship graph and the fourth feature corresponding to each second connection from the image to be detected, including:

Using the first position information of the at least one attribute data, the first relationship graph and the image to be detected as the input of the first sub-network to output the first feature corresponding to each of the attribute nodes, and The first position information of the at least one attribute data, the first relationship diagram and the image to be detected are used as inputs of the second sub-network to output the second feature corresponding to each first connection line, wherein , the number of hidden layers of the first subnetwork is less than that of the second subnetwork;

Using the second position information of the at least one attribute value data, the second relationship graph and the image to be detected as the input of the first sub-network to output the third sub-network corresponding to each attribute value node features, and use the second position information of the at least one attribute value data, the second relationship diagram and the image to be detected as the input of the second subnetwork to output the fourth corresponding to each second connection line feature.

According to one or more embodiments of the present disclosure, Example 4 provides the method of Example 2, according to the first feature corresponding to each of the attribute nodes and the second feature corresponding to each of the first links, and The third feature corresponding to each attribute value node and the fourth feature corresponding to each second connection determine the matching relationship between the attribute data and the attribute value data, including:

determining a node similarity matrix according to the first feature corresponding to each attribute node and the third feature corresponding to each attribute value node;

A matching relationship between each of the attribute data and each of the attribute value data is determined according to the node similarity matrix and the connection similarity matrix.

According to one or more embodiments of the present disclosure, Example 5 provides the method of Example 1, generating a first relationship graph according to the first location information of the at least one attribute data, and generating a first relationship graph according to the at least one attribute value data The second location information generates a second relationship diagram, including:

According to one or more embodiments of the present disclosure, Example 6 provides the method of Example 1, generating a first relationship graph according to the first location information of the at least one attribute data, and generating a first relationship graph according to the at least one attribute value data Before generating the second relationship diagram based on the second location information, the method further includes:

acquiring the first quantity of the attribute data and the second quantity of the attribute value data;

In the case that the first quantity is not equal to the second quantity, by adding the same attribute data at at least one location of the attribute data, or adding the same attribute data at at least one location of the attribute value data attribute value data, so that the number of the attribute data and the attribute value data is equal.

According to one or more embodiments of the present disclosure, Example 7 provides the method of Example 6. In the case where the first quantity is not equal to the second quantity, by Add the same attribute data, including:

In the case that the first quantity is less than the second quantity, add the same attribute data at the location of each attribute data, and obtain twice the first difference between the first quantity and the second quantity , acquire the target attribute value data corresponding to the first difference from at least one of the attribute value data, and add the same attribute value data at each position of the target attribute value data to obtain twice a first amount of attribute data, and twice the first amount of attribute value data;

Correspondingly, the generating the first relationship diagram according to the first location information of the at least one attribute data, and generating the second relationship diagram according to the second location information of the at least one attribute value data include:

generating a first relationship diagram according to first position information corresponding to twice the first quantity of attribute data;

A second relationship graph is generated according to the second position information corresponding to the attribute value data twice the first amount.

According to one or more embodiments of the present disclosure, Example 8 provides the method of Example 6, adding the same attribute value data at at least one location where the attribute value data is located, so that the attribute data and the attribute The amount of value data is equal and also includes:

In the case that the first number is greater than the second number, add the same attribute value data at the location of each attribute value data, and obtain the second difference between the second number and the first number twice value, and obtain the target attribute data corresponding to the second difference from at least one of the attribute data, and add an identical attribute data at the position of the target attribute data to obtain twice the second amount of attribute data, and twice the second amount of attribute value data;

Correspondingly, the preset graph matching model generates a first relationship graph according to the first location information of the at least one attribute data, and generates a second relationship graph according to the second location information of the at least one attribute value data, including:

generating a first relationship diagram according to first position information corresponding to twice the second quantity of attribute data;

A second relationship graph is generated according to the second position information corresponding to the twice the second quantity of attribute value data.

According to one or more embodiments of the present disclosure, Example 9 provides the method of any one of Examples 1-8, wherein the preset image matching model is trained through the following steps:

Obtain model training sample data, the model training sample data includes a plurality of image samples to be detected, the first position information of each attribute data in each of the image samples to be detected, each of the image samples to be detected The second position information of the attribute value data, and the first relationship diagram and the second relationship diagram corresponding to each image sample to be detected;

According to the first position information of each attribute data, the first feature corresponding to each attribute node in the first relational graph of the image sample to be detected and each first feature corresponding to the first relationship graph of the image sample to be detected are obtained from the image sample to be detected through a preset network initial model. One connects the corresponding second feature, and according to the second position information of each attribute value data, obtains each in the second relationship diagram of the image sample to be detected from the image sample to be detected through the preset network initial model The third characteristic corresponding to the attribute value node and the fourth characteristic corresponding to each second connecting line;

According to the first feature corresponding to each attribute node in the first relationship graph of the image sample to be detected and the second feature corresponding to each first connection, and each attribute in the second relationship graph of the image sample to be detected The third feature corresponding to the value node and the fourth feature corresponding to each second connection line, calculate the loss value corresponding to the distance vector between each attribute node and the attribute value node to be matched through the preset loss function, according to the loss value Iterative training is performed on the preset initial network model to obtain the preset graph matching model.

According to one or more embodiments of the present disclosure, Example 10 provides the method shown in Example 9, according to the first feature corresponding to each attribute node in the first relationship graph of the image sample to be detected and each item The second feature corresponding to a connection, and the third feature corresponding to each attribute value node in the second relationship graph of the image sample to be detected and the fourth feature corresponding to each second connection line are calculated by a preset loss function The loss value corresponding to the distance vector between each attribute node and the attribute value node to be matched, including:

According to one or more embodiments of the present disclosure, Example 11 provides a key-value matching device, the device comprising:

According to one or more embodiments of the present disclosure, Example 12 provides a computer-readable medium on which a computer program is stored, and when the program is executed by a processing device, the steps of any one of the methods described in Examples 1-10 are implemented .

According to one or more embodiments of the present disclosure, Example 13 provides an electronic device, comprising:

a storage device on which a computer program is stored;

A processing device configured to execute the computer program in the storage device to implement the steps of any one of the methods in Examples 1-10.

The above description is only a preferred embodiment of the present disclosure and an illustration of the applied technical principle. Those skilled in the art should understand that the disclosure scope involved in this disclosure is not limited to the technical solution formed by the specific combination of the above-mentioned technical features, but also covers the technical solutions formed by the above-mentioned technical features or Other technical solutions formed by any combination of equivalent features. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in this disclosure.

In addition, while operations are depicted in a particular order, this should not be understood as requiring that the operations be performed in the particular order shown or performed in sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while the above discussion contains several specific implementation details, these should not be construed as limitations on the scope of the disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.

Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are merely example forms of implementing the claims. Regarding the apparatus in the foregoing embodiments, the specific manner in which each module executes operations has been described in detail in the embodiments related to the method, and will not be described in detail here.

Claims

A key-value matching method, characterized in that the method comprises:

Acquiring first position information of at least one attribute data and second position information of at least one attribute value data in the image to be detected;

generating a first relationship graph according to the first location information of the at least one attribute data, and generating a second relationship graph according to the second location information of the at least one attribute value data;

Matching the first location information of the at least one attribute data, the second location information of the at least one attribute value data, the first relationship diagram, the second relationship diagram and the image to be detected model to obtain the matching relationship between the attribute data and the attribute value data.
The method according to claim 1, wherein the first relationship graph includes an attribute node corresponding to the location of each attribute data and first connections between different attribute nodes, and the second relationship The graph includes an attribute value node corresponding to the location of each attribute value data, and a second connection between different attribute value nodes, and the preset graph matching model is used for:

According to the first position information of the at least one attribute data, the first feature corresponding to each attribute node in the first relationship graph and the first feature corresponding to each first connection line are obtained from the image to be detected. two features, and according to the second position information of the at least one attribute value data, obtain the third feature corresponding to each of the attribute value nodes in the second relationship diagram and each of the first item from the image to be detected The fourth feature corresponding to the two connections;

According to the first feature corresponding to each of the attribute nodes and the second feature corresponding to each of the first connections, and the third feature corresponding to each of the attribute value nodes and the corresponding to each of the second connections The fourth feature determines the matching relationship between the attribute data and the attribute value data.
The method according to claim 2, wherein the preset image matching model includes a first sub-network and a second sub-network, and the first position information according to the at least one attribute data is obtained from the to-be-detected Obtaining the first feature corresponding to each of the attribute nodes in the first relationship graph and the second feature corresponding to each of the first lines in the image, and according to the second position information of the at least one attribute value data Obtaining the third feature corresponding to each attribute value node in the second relationship diagram and the fourth feature corresponding to each second connection from the image to be detected, including:

Using the first position information of the at least one attribute data, the first relationship graph and the image to be detected as the input of the first sub-network to output the first feature corresponding to each of the attribute nodes, and The first position information of the at least one attribute data, the first relationship diagram and the image to be detected are used as inputs of the second sub-network to output the second feature corresponding to each first connection line, wherein , the number of hidden layers of the first subnetwork is less than that of the second subnetwork;

Using the second position information of the at least one attribute value data, the second relationship graph and the image to be detected as the input of the first sub-network to output the third sub-network corresponding to each attribute value node features, and use the second position information of the at least one attribute value data, the second relationship diagram and the image to be detected as the input of the second subnetwork to output the fourth corresponding to each second connection line feature.
The method according to claim 2, wherein, according to the first feature corresponding to each of the attribute nodes and the second feature corresponding to each of the first lines, and each of the attribute value nodes The corresponding third feature and the fourth feature corresponding to each of the second connections determine the matching relationship between the attribute data and the attribute value data, including:

determining a node similarity matrix according to the first feature corresponding to each attribute node and the third feature corresponding to each attribute value node;

determining a connection similarity matrix according to the second feature corresponding to each of the first connections and the fourth feature corresponding to each of the second connections;

A matching relationship between each of the attribute data and each of the attribute value data is determined according to the node similarity matrix and the connection similarity matrix.
The method according to claim 1, characterized in that, the first relationship diagram is generated according to the first position information of the at least one attribute data, and the second relationship graph is generated according to the second position information of the at least one attribute value data. Relationship diagram, including:

generating the first relationship graph by means of Delaunay triangulation mapping according to the first position information of the at least one attribute data;

The second relational graph is generated by a fully connected graphing method according to the second position information of the at least one attribute value data.
The method according to claim 1, characterized in that, generating a first relationship diagram according to the first position information of the at least one attribute data, and generating a second position information according to the second position information of the at least one attribute value data Before the two relationship diagrams, the method also includes:

acquiring the first quantity of the attribute data and the second quantity of the attribute value data;

In the case that the first quantity is not equal to the second quantity, by adding the same attribute data at at least one location of the attribute data, or adding the same attribute data at at least one location of the attribute value data attribute value data, so that the number of the attribute data and the attribute value data is equal.
The method according to claim 6, characterized in that, in the case where the first quantity is not equal to the second quantity, by adding the same attribute data at at least one position where the attribute data is located, include:

In the case that the first quantity is less than the second quantity, add the same attribute data at the location of each attribute data, and obtain twice the first difference between the first quantity and the second quantity , acquire the target attribute value data corresponding to the first difference from at least one of the attribute value data, and add the same attribute value data at each position of the target attribute value data to obtain twice a first amount of attribute data, and twice the first amount of attribute value data;

Correspondingly, the generating the first relationship diagram according to the first location information of the at least one attribute data, and generating the second relationship diagram according to the second location information of the at least one attribute value data include:

generating a first relationship diagram according to first position information corresponding to twice the first quantity of attribute data;

A second relationship graph is generated according to the second position information corresponding to the attribute value data twice the first amount.
The method according to claim 6, characterized in that adding the same attribute value data at at least one position where the attribute value data is located, so that the quantity of the attribute data and the attribute value data is equal, and further include:

In the case that the first number is greater than the second number, add the same attribute value data at the location of each attribute value data, and obtain the second difference between the second number and the first number twice value, and obtain the target attribute data corresponding to the second difference from at least one of the attribute data, and add an identical attribute data at the position of the target attribute data to obtain twice the second amount of attribute data, and twice the second amount of attribute value data;

Correspondingly, the preset graph matching model generates a first relationship graph according to the first location information of the at least one attribute data, and generates a second relationship graph according to the second location information of the at least one attribute value data, including:

generating a first relationship diagram according to first position information corresponding to twice the second quantity of attribute data;

A second relationship graph is generated according to the second position information corresponding to the twice the second quantity of attribute value data.
The method according to any one of claims 1-8, wherein the preset image matching model is obtained through the following steps of training:

Obtain model training sample data, the model training sample data includes a plurality of image samples to be detected, the first position information of each attribute data in each of the image samples to be detected, each of the image samples to be detected The second position information of the attribute value data, and the first relationship diagram and the second relationship diagram corresponding to each image sample to be detected;

According to the first position information of each attribute data, the first feature corresponding to each attribute node in the first relational graph of the image sample to be detected and each first feature corresponding to the first relationship graph of the image sample to be detected are obtained from the image sample to be detected through a preset network initial model. One connects the corresponding second feature, and according to the second position information of each attribute value data, obtains each in the second relationship diagram of the image sample to be detected from the image sample to be detected through the preset network initial model The third characteristic corresponding to the attribute value node and the fourth characteristic corresponding to each second connecting line;

According to the first feature corresponding to each attribute node in the first relationship graph of the image sample to be detected and the second feature corresponding to each first connection, and each attribute in the second relationship graph of the image sample to be detected The third feature corresponding to the value node and the fourth feature corresponding to each second connection line, calculate the loss value corresponding to the distance vector between each attribute node and the attribute value node to be matched through the preset loss function, according to the loss value Iterative training is performed on the preset initial network model to obtain the preset graph matching model.
The method according to claim 9, characterized in that, according to the first feature corresponding to each attribute node in the first relationship graph of the image sample to be detected and the second feature corresponding to each first connection, and The third feature corresponding to each attribute value node in the second relationship graph of the image sample to be detected and the fourth feature corresponding to each second connection line, calculate the relationship between each attribute node and the attribute to be matched by using a preset loss function The loss value corresponding to the distance vector of the value node, including:

Determine the node similarity matrix according to the first feature corresponding to each attribute node and the third feature corresponding to each attribute value node, and determine the node similarity matrix according to the second feature corresponding to each of the first connections and each of the attribute value nodes The fourth feature corresponding to the second connection determines the connection similarity matrix;

generating a target relationship matrix according to the node similarity matrix and the connection similarity matrix;

Obtain the double random matrix corresponding to the target relationship matrix;

Determine the distance vector between each attribute node and the attribute value node to be matched according to the double random matrix;

The loss value is determined through a preset loss function according to the distance vector.
A key-value matching device, characterized in that the device comprises:

A first acquiring module, configured to acquire first position information of at least one attribute data and second position information of at least one attribute value data in the image to be detected;

A relationship graph generation module, configured to generate a first relationship graph according to the first position information of the at least one attribute data, and generate a second relationship graph according to the second position information of the at least one attribute value data;

A determination module, configured to use the first location information of the at least one attribute data, the second location information of the at least one attribute value data, the first relationship diagram, the second relationship diagram and the image to be detected A preset map matching model is input to obtain a matching relationship between the attribute data and the attribute value data.
A computer-readable medium, on which a computer program is stored, wherein the program implements the steps of any one of claims 1-10 when executed by a processing device.
An electronic device, characterized in that it comprises:

a storage device on which a computer program is stored;

A processing device configured to execute the computer program in the storage device to implement the steps of the method according to any one of claims 1-10.