Intelligent knowledge graph construction method and system based on big data

Abstract

The invention relates to the technical field of data processing, in particular to an intelligent knowledge graph construction method and system based on big data. According to the method, the unified style is designed to adapt to the judgment standard, when the knowledge graph of the multi-product interface is developed, a plurality of interfaces with the unified style are preset, and then when a developer carries out the layout design of the visual knowledge graph component, the development efficiency is greatly improved. According to the method, the style suitability between the visual knowledge graph component and the interface can be visually calculated by calculating the style suitability between the visual knowledge graph component and the interface, so that the style uniformity of different visual knowledge graph components developed by multiple persons can be effectively ensured, and the knowledge graph construction efficiency can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and specifically to a method and system for constructing an intelligent knowledge graph based on big data. Background Art

[0002] Knowledge graph is one of the important technologies for assisting decision making and is widely used in industries such as medical care, finance, e-commerce, justice and education.

[0003] Knowledge acquisition technology is a key step in creating a knowledge graph. Knowledge extraction generally includes the extraction of entities and the extraction of relationships between entities, which refers to the automatic identification of named entities from the original corpus. At present, the widely used method is to use the long short-term memory network model to predict sequence text and combine it with the conditional random field algorithm (CRF) to complete the task of entity naming recognition. In addition, some knowledge acquisition methods are to artificially create semantic grammar rules to identify entities and the relationship between entities.

[0004] In the process of building a knowledge graph, in order to improve construction efficiency, multiple people are usually required to simultaneously build multiple product interfaces of the knowledge graph. Due to the lack of unified evaluation criteria for product interface styles during the construction process, there will often be a large gap in styles between multiple product interfaces during the construction process, resulting in an increase in the workload of subsequent adjustments and adaptations to product styles, affecting the efficiency of knowledge graph construction. Summary of the invention

[0005] The present invention discloses a method, system and storage medium for constructing an intelligent knowledge graph based on big data, including: constructing a product function instruction file according to the function of the product, wherein the product function instruction file includes all event elements that realize the product function; establishing at least one visual knowledge graph component according to the product function instruction file. The present invention designs a unified style adaptation evaluation standard. When developing a knowledge graph of multiple product interfaces, by presetting several interfaces of a unified style, when a developer subsequently designs the layout of a visual knowledge graph component, the style adaptability between the visual knowledge graph component and the interface can be intuitively calculated by calculating the style adaptability between the two. In this way, it can be effectively ensured that different visual knowledge graph components developed by multiple people maintain the uniformity of style, which can effectively improve the efficiency of knowledge graph construction.

[0006] In order to solve the above technical problems, a method and system for constructing an intelligent knowledge graph based on big data are provided. This technical solution solves the problem raised in the above background technology that there is a lack of unified evaluation criteria for product interface styles during the construction process, which often leads to large differences in styles between multiple product interfaces during the construction process.

[0007] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0008] A method for constructing an intelligent knowledge graph based on big data, comprising:

[0009] Constructing a product function instruction file according to the product function, wherein the product function instruction file includes all event elements that realize the product function;

[0010] Establish at least one visual knowledge graph component according to the product function instruction file, wherein the visual knowledge graph component is a standardized visual knowledge graph component and / or a customized visual knowledge graph component;

[0011] Establishing a mapping relationship between each visual knowledge graph component and the interface to be laid out, each of the visual knowledge graph components corresponds to one or more interfaces;

[0012] Calculate the style adaptability between the style parameters of the visual knowledge graph component and the interface, obtain the stylized matching data of the visual knowledge graph component, and adjust the visual knowledge graph component based on the stylized matching data;

[0013] Assigning values ​​to the size parameters and layout parameters of the visual knowledge graph component according to the mapping relationship between the visual knowledge graph component and the platform to be laid out, and obtaining the assignment data of the visual knowledge graph component, wherein the assignment data of the visual knowledge graph component corresponds one-to-one to the mapping relationship between the visual knowledge graph component and the platform to be laid out;

[0014] Based on the assigned data of the visual knowledge graph component, several visual knowledge graph interfaces corresponding to the platform where each product is to be released are established.

[0015] Preferably, the step of establishing at least one visual knowledge graph component according to the product function instruction file comprises the following steps:

[0016] Obtain the operations performed by each product event element on the corresponding interface of each platform to obtain product configuration operation data;

[0017] Determine whether the standardized visual knowledge graph component can meet the operation according to the product configuration operation data. If so, call the standardized visual knowledge graph component. If not, create a customized visual knowledge graph component.

[0018] The step of establishing a customized visual knowledge graph component specifically includes:

[0019] Generate a product interface element corresponding to the product event element according to the product configuration operation data, wherein the product interface element is one or more of a product interface button, a static product interface pattern or a dynamic product interface pattern;

[0020] Determine the triggering action of the product event element based on the type of product interface element and product configuration operation data;

[0021] Generate size parameters and layout parameters of product interface elements based on product configuration operation data and the interface corresponding to product event elements;

[0022] Generate style parameters of product interface elements based on product interface elements;

[0023] Encapsulate the size parameters, layout parameters and style parameters of the product interface elements into attribute data of the product interface elements;

[0024] The product interface elements, the attribute data of the product interface elements and the triggering actions of the product event elements are encapsulated into a visual knowledge graph component.

[0025] Preferably, the style adaptability between the style parameters of the computing visualization knowledge graph component and the interface specifically includes:

[0026] According to the mapping relationship between the visual knowledge graph component and the platform to be laid out, the interface to be laid out associated with the visual knowledge graph component is obtained, and the style parameters of the interface to be laid out are obtained;

[0027] Assigning indexes to the style parameters of the product interface elements and the style parameters of the interface to be laid out according to the set rules, and obtaining one or more parameter index values ​​corresponding to each style parameter;

[0028] Calculate the similarity of each style parameter of the visual knowledge graph component and the associated interface to be laid out according to the parameter similarity calculation formula;

[0029] A different weight value is added to each style parameter, and the style adaptability between the style parameters of the visual knowledge graph component and the interface is calculated according to the style adaptability calculation formula.

[0030] Preferably, the parameter similarity calculation formula is:

[0031]

[0032] In the formula, S i To visualize the similarity between the knowledge graph component and the i-th style parameter of the interface to be laid out, w ij is the jth parameter index value of the i-th style parameter of the product interface element in the visualization knowledge graph component, v ijis the jth parameter index value of the ith style parameter of the interface to be laid out, and is the total number of parameter index values ​​of the ith style parameter;

[0033] The style adaptation calculation formula is:

[0034]

[0035] Where C is the style adaptation between the style parameters of the visual knowledge graph component and the interface, α i is the weight value of the i-th style parameter, and m is the total number of style parameters.

[0036] Preferably, the step of adjusting the visual knowledge graph component based on stylized matching data specifically includes:

[0037] Determine whether the style adaptability between the style parameters of the visual knowledge graph component and the interface is greater than a preset value. If so, it is determined that the style adaptability between the style parameters of the visual knowledge graph component and the interface is low. If not, it is determined that the style adaptability between the style parameters of the visual knowledge graph component and the interface is high.

[0038] For the visual knowledge graph components that are judged to have low style adaptation between the style parameters and the interface, α i S i The style parameter corresponding to the maximum value is obtained, and the parameter index value of the style parameter corresponding to the product interface element of the visual knowledge graph component is adjusted, and it is repeatedly determined whether the style adaptability between the style parameter of the visual knowledge graph component and the interface is greater than the preset value.

[0039] Preferably, assigning the size parameters and layout parameters of the visual knowledge graph component according to the mapping relationship between the visual knowledge graph component and the platform to be laid out specifically includes:

[0040] According to the mapping relationship between the visual knowledge graph component and the interface to be laid out, the interface to be laid out associated with the visual knowledge graph component is obtained, and the size of the interface to be laid out is obtained;

[0041] Assign size parameters of the visual knowledge graph component according to the size of the interface to be laid out and the types of product interface elements;

[0042] Setting the visual knowledge graph component at the layout position on the corresponding interface to be laid out;

[0043] According to the layout position of the visual knowledge graph component, set the layout parameters of the visual knowledge graph component on the corresponding interface to be laid out.

[0044] Preferably, the setting of layout parameters of the visual knowledge graph component on the corresponding interface to be laid out specifically includes:

[0045] Set the component coordinate system in the visual knowledge graph component, and set the interface basic coordinate system on the interface to be laid out;

[0046] Set at least two layout anchor points in the visual knowledge graph component, and calculate the coordinates of the layout anchor points in the component coordinate system;

[0047] Adjust the position and angle of the visual knowledge graph component on the corresponding interface to be laid out. When the visual knowledge graph component is at the layout position, calculate the offset data of the component coordinate system relative to the interface base coordinate system. The offset data includes the offset distance of the origin of the component coordinate system relative to the interface base coordinate system and the rotation angle of the component coordinate system.

[0048] The coordinates of the layout anchor point in the interface basic coordinate system are calculated based on the coordinates of the layout anchor point in the component coordinate system and the offset data of the component coordinate system relative to the interface basic coordinate system.

[0049] An intelligent knowledge graph construction system based on big data, comprising:

[0050] A front-end development module, which is used to construct a product function instruction file according to the functions of the product;

[0051] A product editing module sets several interfaces for each platform to be released for a product according to product attributes. The product editing module is used to establish at least one visual knowledge graph component according to a product function instruction file, establish a mapping relationship between each visual knowledge graph component and the platform to be laid out, calculate the style parameters of the visual knowledge graph component and the style adaptability between the interface, assign size parameters and layout parameters of the visual knowledge graph component according to the mapping relationship between the visual knowledge graph component and the platform to be laid out, and establish several visual product interfaces corresponding to the platform to be released for each product based on the assignment data of the visual knowledge graph component and the stylized matching data of the visual knowledge graph component.

[0052] The product editing module internally integrates:

[0053] A standard component unit, wherein at least one standard component library is stored inside the standard component unit, and a plurality of standardized visual knowledge graph components are stored inside the standard component library;

[0054] A self-developed component unit, which is used to establish a customized visual knowledge graph component;

[0055] A visual editing unit, which is used to set a number of interfaces for each platform to be released for each product according to product attributes, and to implement a visual drag-and-drop layout of a visual knowledge graph component on the interface;

[0056] A component importing unit, the component importing unit is used to import a standardized visual knowledge graph component and / or a customized visual knowledge graph component into the visual editing unit;

[0057] A style adaptation calculation unit, which is used to calculate the style adaptation degree between the style parameters of the visual knowledge graph component and the interface;

[0058] A layout assignment unit, which is used to assign size parameters of the visual knowledge graph component according to the size of the interface to be laid out and the types of product interface elements, and to set layout parameters of the visual knowledge graph component on the corresponding interface to be laid out according to the layout position of the visual knowledge graph component;

[0059] Product interface generation unit: The product interface generation unit is used to establish several visual product interfaces corresponding to the platform for each product to be released based on the assignment data of the visual knowledge graph component.

[0060] A computer-readable storage medium having a computer-readable program stored thereon, wherein the computer-readable program, when called, executes a method for constructing an intelligent knowledge graph based on big data as described above.

[0061] Compared with the prior art, the present invention has the following beneficial effects:

[0062] The present invention designs a unified style adaptation judgment standard. When developing a knowledge graph with multiple product interfaces, several interfaces with a unified style are pre-set. When developers then design the layout of a visual knowledge graph component, the style adaptability between the visual knowledge graph component and the interface can be intuitively calculated by calculating the style adaptability between the two. This method can effectively ensure that different visual knowledge graph components developed by multiple people maintain a unified style, which can effectively improve the efficiency of knowledge graph construction. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 It is a schematic diagram of the method flow of the present invention;

[0064] Figure 2 A schematic diagram of the flow of a method for calculating the style parameters of a visual knowledge graph component and the style adaptability between the interface in the present invention;

[0065] Figure 3A schematic diagram of the method flow for adjusting the visual knowledge graph component in the present invention;

[0066] Figure 4 A schematic flow chart of the method for assigning size parameters and layout parameters to a visual knowledge graph component in the present invention. DETAILED DESCRIPTION

[0067] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0068] As shown in FIG. X, a method for constructing an intelligent knowledge graph based on big data includes:

[0069] Construct a product function instruction file according to the product function. The product function instruction file includes all event elements that realize the product function;

[0070] Establish at least one visual knowledge graph component according to the product function instruction file, where the visual knowledge graph component is a standardized visual knowledge graph component and / or a customized visual knowledge graph component;

[0071] Establish a mapping relationship between each visual knowledge graph component and the interface to be laid out, and each visual knowledge graph component corresponds to one or more interfaces;

[0072] Calculate the style adaptability between the style parameters of the visual knowledge graph component and the interface, obtain the stylized matching data of the visual knowledge graph component, and adjust the visual knowledge graph component based on the stylized matching data;

[0073] Assigning values ​​to the size parameters and layout parameters of the visual knowledge graph component according to the mapping relationship between the visual knowledge graph component and the platform to be laid out, and obtaining the assignment data of the visual knowledge graph component, wherein the assignment data of the visual knowledge graph component corresponds one-to-one to the mapping relationship between the visual knowledge graph component and the platform to be laid out;

[0074] Based on the assigned data of the visual knowledge graph component, several visual knowledge graph interfaces corresponding to the platform where each product is to be released are established.

[0075] By designing a unified style adaptation evaluation standard, when developing knowledge graphs for multiple product interfaces, several interfaces with a unified style can be pre-set. When developers then design the layout of visual knowledge graph components, the style adaptability between the visual knowledge graph components and the interfaces can be intuitively calculated. This method can effectively ensure that different visual knowledge graph components developed by multiple people maintain a unified style.

[0076] Establishing at least one visual knowledge graph component according to the product function instruction file includes the following steps:

[0077] Obtain the operations performed by each product event element on the corresponding interface of each platform to obtain product configuration operation data;

[0078] Determine whether the standardized visual knowledge graph component can meet the operation according to the product configuration operation data. If so, call the standardized visual knowledge graph component. If not, create a customized visual knowledge graph component.

[0079] Among them, establishing a customized visual knowledge graph component specifically includes:

[0080] Generate a product interface element corresponding to the product event element according to the product configuration operation data, where the product interface element is one or more of a product interface button, a static product interface pattern or a dynamic product interface pattern;

[0081] Determine the triggering action of the product event element based on the type of product interface element and product configuration operation data;

[0082] Generate size parameters and layout parameters of product interface elements based on product configuration operation data and the interface corresponding to product event elements;

[0083] Generate style parameters of product interface elements based on product interface elements;

[0084] Encapsulate the size parameters, layout parameters and style parameters of the product interface elements into attribute data of the product interface elements;

[0085] The product interface elements, the attribute data of the product interface elements and the triggering actions of the product event elements are encapsulated into a visual knowledge graph component.

[0086] This solution uses a standardized visual knowledge graph component and a customized visual knowledge graph component, so users can generate the pages they want, which can effectively save labor and time costs, improve development efficiency, and increase product editing flexibility by not restricting the use of components.

[0087] The style parameters of the visual knowledge graph component and the style adaptability between the interface are calculated as follows:

[0088] According to the mapping relationship between the visual knowledge graph component and the platform to be laid out, the interface to be laid out associated with the visual knowledge graph component is obtained, and the style parameters of the interface to be laid out are obtained;

[0089] Assigning indexes to the style parameters of the product interface elements and the style parameters of the interface to be laid out according to the set rules, and obtaining one or more parameter index values ​​corresponding to each style parameter;

[0090] Calculate the similarity of each style parameter of the visual knowledge graph component and the associated interface to be laid out according to the parameter similarity calculation formula;

[0091] A different weight value is added to each style parameter, and the style adaptability between the style parameters of the visual knowledge graph component and the interface is calculated according to the style adaptability calculation formula.

[0092] The parameter similarity calculation formula is:

[0093]

[0094] In the formula, S i To visualize the similarity between the knowledge graph component and the i-th style parameter of the interface to be laid out, w ij is the jth parameter index value of the i-th style parameter of the product interface element in the visualization knowledge graph component, v ij is the jth parameter index value of the ith style parameter of the interface to be laid out, and is the total number of parameter index values ​​of the ith style parameter;

[0095] The formula for calculating style adaptation is:

[0096]

[0097] Where C is the style adaptation between the style parameters of the visual knowledge graph component and the interface, α i is the weight value of the i-th style parameter, and m is the total number of style parameters.

[0098] It is understandable that a style parameter may have different parameter indicators. For example, the color style parameter has a common parameter indicator including the RGB three-color parameter indicator. The similarity between the visualization knowledge graph component and the interface to be laid out is expressed by calculating the difference distance between the parameter indicator values ​​of the visualization knowledge graph component and the interface to be laid out. i The larger the value of , the greater the difference distance of the parameter index value between the visualization knowledge graph component and the interface to be laid out, and the lower the similarity between the visualization knowledge graph component and the interface to be laid out.

[0099] The visual knowledge graph components adjusted based on stylized matching data include:

[0100] Determine whether the style adaptability between the style parameters of the visual knowledge graph component and the interface is greater than a preset value. If so, it is determined that the style adaptability between the style parameters of the visual knowledge graph component and the interface is low. If not, it is determined that the style adaptability between the style parameters of the visual knowledge graph component and the interface is high.

[0101] For the visual knowledge graph components that are judged to have low style adaptation between the style parameters and the interface, α i S i The style parameter corresponding to the maximum value is obtained, and the parameter index value of the style parameter corresponding to the product interface element of the visual knowledge graph component is adjusted, and it is repeatedly determined whether the style adaptability between the style parameter of the visual knowledge graph component and the interface is greater than the preset value.

[0102] Understandably, S i The larger the value of , the greater the difference distance between the parameter index values ​​of the visualization knowledge graph component and the interface to be laid out, and the lower the similarity between the visualization knowledge graph component and the interface to be laid out. i The larger the value, the more important the style parameter is. Therefore, when adjusting the style of the visual knowledge graph component, i S i Adjusting the style parameters corresponding to the maximum value can effectively and quickly improve the adaptability between the visual knowledge graph component and the interface to be laid out.

[0103] The size parameters and layout parameters of the visual knowledge graph component are assigned according to the mapping relationship between the visual knowledge graph component and the platform to be laid out, specifically including:

[0104] According to the mapping relationship between the visual knowledge graph component and the interface to be laid out, the interface to be laid out associated with the visual knowledge graph component is obtained, and the size of the interface to be laid out is obtained;

[0105] Assign size parameters of the visual knowledge graph component according to the size of the interface to be laid out and the types of product interface elements;

[0106] Setting the visual knowledge graph component at the layout position on the corresponding interface to be laid out;

[0107] According to the layout position of the visual knowledge graph component, set the layout parameters of the visual knowledge graph component on the corresponding interface to be laid out.

[0108] The layout parameters of the visual knowledge graph component on the corresponding layout interface include:

[0109] Set the component coordinate system in the visual knowledge graph component, and set the interface basic coordinate system on the interface to be laid out;

[0110] Set at least two layout anchor points in the visual knowledge graph component, and calculate the coordinates of the layout anchor points in the component coordinate system;

[0111] Adjust the position and angle of the visual knowledge graph component on the corresponding interface to be laid out. When the visual knowledge graph component is at the layout position, calculate the offset data of the component coordinate system relative to the basic coordinate system of the interface. The offset data includes the offset distance of the origin of the component coordinate system relative to the basic coordinate system of the interface and the rotation angle of the component coordinate system.

[0112] The coordinates of the layout anchor point in the interface basic coordinate system are calculated based on the coordinates of the layout anchor point in the component coordinate system and the offset data of the component coordinate system relative to the interface basic coordinate system.

[0113] By implementing the visual drag-and-drop layout design of the visual knowledge graph component, product designers can intuitively feel the design results of the product interface, making it easier for staff to make real-time adjustments and designs to the product interface, thereby achieving efficient design of the product interface.

[0114] An intelligent knowledge graph construction system based on big data, comprising:

[0115] Front-end development module: The front-end development module is used to build product function instruction files according to product functions;

[0116] The product editing module sets several interfaces for each platform to be released for the product according to the product attributes. The product editing module is used to establish at least one visual knowledge graph component according to the product function instruction file, establish a mapping relationship between each visual knowledge graph component and the platform to be laid out, calculate the style parameters of the visual knowledge graph component and the style adaptability between the interface, assign the size parameters and layout parameters of the visual knowledge graph component according to the mapping relationship between the visual knowledge graph component and the platform to be laid out, and establish several visual product interfaces corresponding to the platform to be released for each product based on the assignment data of the visual knowledge graph component and the stylized matching data of the visual knowledge graph component.

[0117] The product editing module has the following internal integrations:

[0118] A standard component unit, which stores at least one standard component library, and the standard component library stores several standardized visual knowledge graph components;

[0119] Self-developed component unit, which is used to build customized visual knowledge graph components;

[0120] A visual editing unit, which is used to set several interfaces for each platform to be released for each product according to product attributes, and to implement visual drag-and-drop layout of visual knowledge graph components on the interface;

[0121] A component import unit, which is used to import standardized visual knowledge graph components and / or customized visual knowledge graph components into the visual editing unit;

[0122] A style adaptation calculation unit, which is used to calculate the style adaptation degree between the style parameters of the visual knowledge graph component and the interface;

[0123] A layout assignment unit, which is used to assign size parameters of the visual knowledge graph component according to the size of the interface to be laid out and the types of product interface elements, and to set layout parameters of the visual knowledge graph component on the corresponding interface to be laid out according to the layout position of the visual knowledge graph component;

[0124] Product interface generation unit: The product interface generation unit is used to establish several visual product interfaces corresponding to the platform for each product to be released based on the assignment data of the visual knowledge graph component.

[0125] A computer-readable storage medium having a computer-readable program stored thereon, which, when called, executes the above-mentioned intelligent knowledge graph construction method based on big data.

[0126] To sum up: by designing a unified style adaptation evaluation standard, different visual knowledge graph components in software product development with multiple product interfaces maintain the unity of style, which can effectively improve the efficiency of knowledge graph construction.

[0127] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

[0128] Beneficial Effects

[0129] A method for constructing an intelligent knowledge graph based on big data, characterized by comprising:

[0130] Constructing a product function instruction file according to the product function, wherein the product function instruction file includes all event elements that realize the product function;

[0131] Establish at least one visual knowledge graph component according to the product function instruction file, wherein the visual knowledge graph component is a standardized visual knowledge graph component and / or a customized visual knowledge graph component;

[0132] Establishing a mapping relationship between each visual knowledge graph component and the interface to be laid out, each of the visual knowledge graph components corresponds to one or more interfaces;

[0133] Calculate the style adaptability between the style parameters of the visual knowledge graph component and the interface, obtain the stylized matching data of the visual knowledge graph component, and adjust the visual knowledge graph component based on the stylized matching data;

[0134] Assigning values ​​to the size parameters and layout parameters of the visual knowledge graph component according to the mapping relationship between the visual knowledge graph component and the platform to be laid out, and obtaining the assignment data of the visual knowledge graph component, wherein the assignment data of the visual knowledge graph component corresponds one-to-one to the mapping relationship between the visual knowledge graph component and the platform to be laid out;

[0135] Based on the assigned data of the visual knowledge graph component, several visual knowledge graph interfaces corresponding to the platform where each product is to be released are established.

[0136] According to the method for constructing an intelligent knowledge graph based on big data according to claim 1, it is characterized in that the step of establishing at least one visual knowledge graph component according to the product function instruction file comprises the following steps:

[0137] Obtain the operations performed by each product event element on the corresponding interface of each platform to obtain product configuration operation data;

[0138] Determine whether the standardized visual knowledge graph component can meet the operation according to the product configuration operation data. If so, call the standardized visual knowledge graph component. If not, create a customized visual knowledge graph component.

[0139] The step of establishing a customized visual knowledge graph component specifically includes:

[0140] Generate a product interface element corresponding to the product event element according to the product configuration operation data, wherein the product interface element is one or more of a product interface button, a static product interface pattern or a dynamic product interface pattern;

[0141] Determine the triggering action of the product event element based on the type of product interface element and product configuration operation data;

[0142] Generate size parameters and layout parameters of product interface elements based on product configuration operation data and the interface corresponding to product event elements;

[0143] Generate style parameters of product interface elements based on product interface elements;

[0144] Encapsulate the size parameters, layout parameters and style parameters of the product interface elements into attribute data of the product interface elements;

[0145] The product interface elements, the attribute data of the product interface elements and the triggering actions of the product event elements are encapsulated into a visual knowledge graph component.

[0146] According to the method for constructing an intelligent knowledge graph based on big data according to claim 2, it is characterized in that the style adaptation degree between the style parameters of the calculated visualization knowledge graph component and the interface specifically includes:

[0147] According to the mapping relationship between the visual knowledge graph component and the platform to be laid out, the interface to be laid out associated with the visual knowledge graph component is obtained, and the style parameters of the interface to be laid out are obtained;

[0148] Assigning indexes to the style parameters of the product interface elements and the style parameters of the interface to be laid out according to the set rules, and obtaining one or more parameter index values ​​corresponding to each style parameter;

[0149] Calculate the similarity of each style parameter of the visual knowledge graph component and the associated interface to be laid out according to the parameter similarity calculation formula;

[0150] A different weight value is added to each style parameter, and the style adaptability between the style parameters of the visual knowledge graph component and the interface is calculated according to the style adaptability calculation formula.

[0151] According to the method for constructing an intelligent knowledge graph based on big data according to claim 3, it is characterized in that the parameter similarity calculation formula is:

[0152]

[0153] In the formula, S i To visualize the similarity between the knowledge graph component and the i-th style parameter of the interface to be laid out, w ij is the jth parameter index value of the i-th style parameter of the product interface element in the visualization knowledge graph component, v ij is the jth parameter index value of the ith style parameter of the interface to be laid out, and is the total number of parameter index values ​​of the ith style parameter;

[0154] The style adaptation calculation formula is:

[0155]

[0156] Where C is the style adaptation between the style parameters of the visual knowledge graph component and the interface, α i is the weight value of the i-th style parameter, and m is the total number of style parameters.

[0157] According to the method for constructing an intelligent knowledge graph based on big data according to claim 4, it is characterized in that the visual knowledge graph component adjusted based on stylized matching data specifically includes:

[0158] Determine whether the style adaptability between the style parameters of the visual knowledge graph component and the interface is greater than a preset value. If so, it is determined that the style adaptability between the style parameters of the visual knowledge graph component and the interface is low. If not, it is determined that the style adaptability between the style parameters of the visual knowledge graph component and the interface is high.

[0159] For the visual knowledge graph components that are judged to have low style adaptation between the style parameters and the interface, α i S i The style parameter corresponding to the maximum value is obtained, and the parameter index value of the style parameter corresponding to the product interface element of the visual knowledge graph component is adjusted, and it is repeatedly determined whether the style adaptability between the style parameter of the visual knowledge graph component and the interface is greater than the preset value.

[0160] According to the method for constructing an intelligent knowledge graph based on big data according to claim 5, it is characterized in that the step of assigning size parameters and layout parameters of the visual knowledge graph component according to the mapping relationship between the visual knowledge graph component and the platform to be laid out specifically comprises:

[0161] According to the mapping relationship between the visual knowledge graph component and the interface to be laid out, the interface to be laid out associated with the visual knowledge graph component is obtained, and the size of the interface to be laid out is obtained;

[0162] Assign size parameters of the visual knowledge graph component according to the size of the interface to be laid out and the types of product interface elements;

[0163] Setting the visual knowledge graph component at the layout position on the corresponding interface to be laid out;

[0164] According to the layout position of the visual knowledge graph component, set the layout parameters of the visual knowledge graph component on the corresponding interface to be laid out.

[0165] According to the method for constructing an intelligent knowledge graph based on big data according to claim 6, it is characterized in that the layout parameters of the visual knowledge graph component on the corresponding layout interface specifically include:

[0166] Set the component coordinate system in the visual knowledge graph component, and set the interface basic coordinate system on the interface to be laid out;

[0167] Set at least two layout anchor points in the visual knowledge graph component, and calculate the coordinates of the layout anchor points in the component coordinate system;

[0168] Adjust the position and angle of the visual knowledge graph component on the corresponding interface to be laid out. When the visual knowledge graph component is at the layout position, calculate the offset data of the component coordinate system relative to the interface base coordinate system. The offset data includes the offset distance of the origin of the component coordinate system relative to the interface base coordinate system and the rotation angle of the component coordinate system.

[0169] The coordinates of the layout anchor point in the interface basic coordinate system are calculated based on the coordinates of the layout anchor point in the component coordinate system and the offset data of the component coordinate system relative to the interface basic coordinate system.

[0170] An intelligent knowledge graph construction system based on big data, characterized by comprising:

[0171] A front-end development module, which is used to construct a product function instruction file according to the functions of the product;

[0172] A product editing module sets several interfaces for each platform to be released for a product according to product attributes. The product editing module is used to establish at least one visual knowledge graph component according to a product function instruction file, establish a mapping relationship between each visual knowledge graph component and the platform to be laid out, calculate the style parameters of the visual knowledge graph component and the style adaptability between the interface, assign size parameters and layout parameters of the visual knowledge graph component according to the mapping relationship between the visual knowledge graph component and the platform to be laid out, and establish several visual product interfaces corresponding to the platform to be released for each product based on the assignment data of the visual knowledge graph component and the stylized matching data of the visual knowledge graph component.

[0173] According to the method and system for constructing an intelligent knowledge graph based on big data according to claim 1, it is characterized in that the product editing module internally integrates:

[0174] A standard component unit, wherein at least one standard component library is stored inside the standard component unit, and a plurality of standardized visual knowledge graph components are stored inside the standard component library;

[0175] A self-developed component unit, which is used to establish a customized visual knowledge graph component;

[0176] A visual editing unit, which is used to set a number of interfaces for each platform to be released for each product according to product attributes, and to implement a visual drag-and-drop layout of a visual knowledge graph component on the interface;

[0177] A component importing unit, the component importing unit is used to import a standardized visual knowledge graph component and / or a customized visual knowledge graph component into the visual editing unit;

[0178] A style adaptation calculation unit, which is used to calculate the style adaptation degree between the style parameters of the visual knowledge graph component and the interface;

[0179] A layout assignment unit, which is used to assign size parameters of the visual knowledge graph component according to the size of the interface to be laid out and the types of product interface elements, and to set layout parameters of the visual knowledge graph component on the corresponding interface to be laid out according to the layout position of the visual knowledge graph component;

[0180] Product interface generation unit: The product interface generation unit is used to establish several visual product interfaces corresponding to the platform for each product to be released based on the assignment data of the visual knowledge graph component.

[0181] A computer-readable storage medium having a computer-readable program stored thereon, characterized in that when the computer-readable program is called, an intelligent knowledge graph construction method based on big data as described in any one of claims 1-7 is executed.

Claims

1. A method for constructing an intelligent knowledge graph based on big data, characterized in that The steps include: Constructing a product function instruction file according to the product function, wherein the product function instruction file includes all event elements that realize the product function; Establish at least one visual knowledge graph component according to the product function instruction file, wherein the visual knowledge graph component is a standardized visual knowledge graph component and / or a customized visual knowledge graph component; Establishing a mapping relationship between each visual knowledge graph component and the interface to be laid out, each of the visual knowledge graph components corresponds to one or more interfaces; Calculate the style adaptability between the style parameters of the visual knowledge graph component and the interface, obtain the stylized matching data of the visual knowledge graph component, and adjust the visual knowledge graph component based on the stylized matching data; Assigning values ​​to the size parameters and layout parameters of the visual knowledge graph component according to the mapping relationship between the visual knowledge graph component and the platform to be laid out, and obtaining the assignment data of the visual knowledge graph component, wherein the assignment data of the visual knowledge graph component corresponds one-to-one to the mapping relationship between the visual knowledge graph component and the platform to be laid out; Based on the assigned data of the visual knowledge graph component, several visual knowledge graph interfaces corresponding to the platform where each product is to be released are established.

2. According to claim 1, a method for constructing an intelligent knowledge graph based on big data is characterized in that The step of establishing at least one visual knowledge graph component according to the product function instruction file comprises the following steps: Obtain the operations performed by each product event element on the corresponding interface of each platform to obtain product configuration operation data; Determine whether the standardized visual knowledge graph component can meet the operation according to the product configuration operation data. If so, call the standardized visual knowledge graph component. If not, create a customized visual knowledge graph component. The step of establishing a customized visual knowledge graph component specifically includes: Generate a product interface element corresponding to the product event element according to the product configuration operation data, wherein the product interface element is one or more of a product interface button, a static product interface pattern or a dynamic product interface pattern; Determine the triggering action of the product event element based on the type of product interface element and product configuration operation data; Generate size parameters and layout parameters of product interface elements based on product configuration operation data and the interface corresponding to product event elements; Generate style parameters of product interface elements based on product interface elements; Encapsulate the size parameters, layout parameters and style parameters of the product interface elements into attribute data of the product interface elements; The product interface elements, the attribute data of the product interface elements and the triggering actions of the product event elements are encapsulated into a visual knowledge graph component.

3. According to claim 1, a method for constructing an intelligent knowledge graph based on big data is characterized in that The style adaptability between the style parameters of the computing visualization knowledge graph component and the interface specifically includes: According to the mapping relationship between the visual knowledge graph component and the platform to be laid out, the interface to be laid out associated with the visual knowledge graph component is obtained, and the style parameters of the interface to be laid out are obtained; Assigning indexes to the style parameters of the product interface elements and the style parameters of the interface to be laid out according to the set rules, and obtaining one or more parameter index values ​​corresponding to each style parameter; Calculate the similarity of each style parameter of the visual knowledge graph component and the associated interface to be laid out according to the parameter similarity calculation formula; Add different weight values ​​to each style parameter, and calculate the style adaptability between the style parameters of the visual knowledge graph component and the interface according to the style adaptability calculation formula; The parameter similarity calculation formula is: In the formula, S i To visualize the similarity between the knowledge graph component and the i-th style parameter of the interface to be laid out, w ij is the jth parameter index value of the i-th style parameter of the product interface element in the visualization knowledge graph component, v ij is the jth parameter index value of the ith style parameter of the interface to be laid out, and is the total number of parameter index values ​​of the ith style parameter; The style adaptation calculation formula is: Where C is the style adaptation between the style parameters of the visual knowledge graph component and the interface, α i is the weight value of the i-th style parameter, and m is the total number of style parameters.

4. The method for constructing an intelligent knowledge graph based on big data according to claim 1, characterized in that The visual knowledge graph component adjusted based on stylized matching data specifically includes: Determine whether the style adaptability between the style parameters of the visual knowledge graph component and the interface is greater than a preset value. If so, it is determined that the style adaptability between the style parameters of the visual knowledge graph component and the interface is low. If not, it is determined that the style adaptability between the style parameters of the visual knowledge graph component and the interface is high. For the visual knowledge graph components that are judged to have low style adaptation between the style parameters and the interface, α i S i The style parameter corresponding to the maximum value is selected, and the parameter index value of the style parameter corresponding to the product interface element of the visual knowledge graph component is adjusted, and it is repeatedly determined whether the style adaptability between the style parameter of the visual knowledge graph component and the interface is greater than the preset value.

5. The method for constructing an intelligent knowledge graph based on big data according to claim 1, characterized in that The assigning of the size parameters and layout parameters of the visual knowledge graph component according to the mapping relationship between the visual knowledge graph component and the platform to be laid out specifically includes: According to the mapping relationship between the visual knowledge graph component and the interface to be laid out, the interface to be laid out associated with the visual knowledge graph component is obtained, and the size of the interface to be laid out is obtained; Assign size parameters of the visual knowledge graph component according to the size of the interface to be laid out and the types of product interface elements; Setting the visual knowledge graph component at the layout position on the corresponding interface to be laid out; According to the layout position of the visual knowledge graph component, set the layout parameters of the visual knowledge graph component on the corresponding interface to be laid out.

6. The method for constructing an intelligent knowledge graph based on big data according to claim 1, characterized in that The layout parameters of the visual knowledge graph component on the corresponding layout interface specifically include: Set the component coordinate system in the visual knowledge graph component, and set the interface basic coordinate system on the interface to be laid out; Set at least two layout anchor points in the visual knowledge graph component, and calculate the coordinates of the layout anchor points in the component coordinate system; Adjust the position and angle of the visual knowledge graph component on the corresponding interface to be laid out. When the visual knowledge graph component is at the layout position, calculate the offset data of the component coordinate system relative to the interface base coordinate system. The offset data includes the offset distance of the origin of the component coordinate system relative to the interface base coordinate system and the rotation angle of the component coordinate system. The coordinates of the layout anchor point in the interface basic coordinate system are calculated based on the coordinates of the layout anchor point in the component coordinate system and the offset data of the component coordinate system relative to the interface basic coordinate system.

7. The system of the method for constructing an intelligent knowledge graph based on big data according to claim 1, comprising: A front-end development module, which is used to construct a product function instruction file according to the functions of the product; A product editing module sets several interfaces for each platform to be released for a product according to product attributes. The product editing module is used to establish at least one visual knowledge graph component according to a product function instruction file, establish a mapping relationship between each visual knowledge graph component and the platform to be laid out, calculate the style parameters of the visual knowledge graph component and the style adaptability between the interface, assign size parameters and layout parameters of the visual knowledge graph component according to the mapping relationship between the visual knowledge graph component and the platform to be laid out, and establish several visual product interfaces corresponding to the platform to be released for each product based on the assignment data of the visual knowledge graph component and the stylized matching data of the visual knowledge graph component. The product editing module internally integrates: A standard component unit, wherein at least one standard component library is stored inside the standard component unit, and a plurality of standardized visual knowledge graph components are stored inside the standard component library; A self-developed component unit, which is used to establish a customized visual knowledge graph component; A visual editing unit, which is used to set a number of interfaces for each platform to be released for each product according to product attributes, and to implement a visual drag-and-drop layout of a visual knowledge graph component on the interface; A component importing unit, the component importing unit is used to import a standardized visual knowledge graph component and / or a customized visual knowledge graph component into the visual editing unit; A style adaptation calculation unit, which is used to calculate the style adaptation degree between the style parameters of the visual knowledge graph component and the interface; A layout assignment unit, which is used to assign size parameters of the visual knowledge graph component according to the size of the interface to be laid out and the types of product interface elements, and to set layout parameters of the visual knowledge graph component on the corresponding interface to be laid out according to the layout position of the visual knowledge graph component; Product interface generation unit: The product interface generation unit is used to establish several visual product interfaces corresponding to the platform for each product to be released based on the assignment data of the visual knowledge graph component.

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