A data interaction processing method based on low-code development components

Abstract

The present invention proposes a data interaction processing method based on low-code development components, which relates to the technical field of data interaction processing. A data sequence is received from each interface, a node tree is established for each data sequence respectively, and noise nodes are removed to form a new node tree corresponding to each interface; the data structure is cleaned and optimized to ensure the integrity and correctness of the data; quality ratios of data groups in multiple new node trees are evaluated respectively to obtain the quality ratios of each data group; each data group is sorted according to the quality ratio to obtain a data group with a quality ratio higher than a threshold; quality evaluation is performed on the data group to screen out high-quality data; a low-code interaction component is constructed through a mapping relationship; these data are converted into a low-code interaction component through a mapping relationship, which can intuitively and dynamically display the data and provide the function of interacting with the user.

Description

Technical Field

[0001] The present invention relates to the technical field of data interaction processing, and in particular to a data interaction processing method based on a low-code development component. Background Art

[0002] In order to adapt to the strategic development needs in the context of the digital and information age, enterprises have used the Internet to upgrade and improve their information systems. However, due to the particularity of the industry in which the enterprise is located and its own personalized needs, the information construction of different enterprises is also different. With the business development of enterprises and the growing and diversified demand for applications, the coding speed of the traditional project development model has been difficult to meet. In order to solve the contradiction between the rapid development of enterprise application needs and the inefficiency of application development, introducing low-code technology into application development is an effective solution. It can be foreseen that in the near future, this technology will inevitably be introduced more and more into software development.

[0003] "Low-code" technology refers to a rapid development technology concept mainly used in the field of enterprise informatization. The low-code development platform encapsulates common functional codes into components, allowing developers of different experience levels or non-professional users to create web pages and applications through a graphical user interface, using drag-and-drop components and model-driven logic, without having to write all the code to implement the function, greatly improving the efficiency of application development. With the help of the low-code platform, users can quickly implement professional functions without professional coding skills, or expand more application functions through a small amount of coding.

[0004] Drawing on the development model of native database applications, the low-code development platform extracts and encapsulates common codes such as functions, logic, processes, interfaces, and components, and provides a visual page for selecting, dragging, binding properties, and instantiating the required functional components. This replaces the complicated code-based process, eliminates the differentiated characteristics of requirements, and makes the development process convenient and efficient. Summary of the invention

[0005] In order to solve the above technical problems, the present invention proposes a data interaction processing method based on low-code development components, comprising the following steps:

[0006] S1, receiving a data sequence from each interface, establishing a node tree for each data sequence, removing noise nodes, and forming a new node tree corresponding to each interface;

[0007] S2, respectively calculating the quality ratio of the data groups in the multiple new node trees to obtain the quality ratio of each data group;

[0008] S3. Sort each data group according to the quality ratio, obtain the data group with a quality ratio higher than the threshold, and build a low-code interactive component through the mapping relationship.

[0009] Furthermore, the step S1 comprises:

[0010] S1.1. Receive a data sequence from each interface and establish a node tree for each data sequence;

[0011] The node tree D with r as the root is expressed as D=(V(v),T(v),A(v),C(v),E(v),r);

[0012] V(v) represents the set of all nodes v in the node tree, T(v) represents the set of label names of the label data represented by node v, A(v) represents the set of label attributes of the label data represented by node v, C(v) represents the set of child nodes contained in the label data represented by node v, and E(v) represents the set of all directed edges in the node tree;

[0013] S1.2, classify the nodes according to the label attribute set A(v), remove the noise nodes, and form a new node tree;

[0014] According to the label attribute set A(v), the nodes are classified into the node set A1(v) of the non-data label attribute class and the node set A2(v) of the data label attribute class. The nodes in the node set A1(v) are defined as noise nodes. All noise nodes are removed from the node tree, and the new node tree after removing the noise nodes is represented by D'.

[0015] Further, the mass ratio Y is:

[0016]

[0017] Where M is the number of nodes in the node tree before removing the noise nodes, r j is the number of child nodes under the jth node, Q is the number of nodes in the new node tree after removing the noise nodes, R J is the number of child nodes under the Jth node.

[0018] Furthermore, in step S3, a mapping relationship between a data group above a threshold and a low-code interaction component is established by the following mapping rule:

[0019] (1) Save the data groups whose quality ratio is higher than the threshold into the object set Ce;

[0020] (2) assigning a grade to the data group according to the quality ratio, and updating the grade value of the data group after the grade assignment to the object set Ce, and the object set Ce is updated after the grade assignment operation is performed;

[0021] (3) searching for all data groups with adjacent level values ​​in the object set Ce according to the level value I of the data group, and taking all data groups with adjacent level values ​​as mapping objects;

[0022] (4) The data group in the mapping object is used as the interaction object of the data group with level value I.

[0023] Furthermore, the proximity is calculated by the following formula:

[0024]

[0025] Where L represents proximity, A represents the vector of any data group whose level value is not 1 in the object set Ce, and B represents the vector of the data group whose level value is 1. , Respectively represent the magnitude of vectors A and B;

[0026] When the proximity value is less than the proximity threshold, it is judged that the data group whose level value is not 1 in the object set Ce is a data group having a level value adjacent to the data group whose level value is 1.

[0027] Furthermore, the interactive objects of the data group are encapsulated into independent components, and different independent components are added to the interactive application by dragging and dropping, and the positions and properties of the components are adjusted.

[0028] Compared with the prior art, the present invention has the following beneficial technical effects:

[0029] 1. Receive data sequences from each interface, establish a node tree for each data sequence, remove noise nodes, and form a new node tree corresponding to each interface; to clean and optimize the data structure to ensure the integrity and correctness of the data.

[0030] 2. Calculate the quality ratio of the data groups in the multiple new node trees respectively to obtain the quality ratio of each data group; sort the data groups according to the quality ratio to obtain the data groups with quality ratios higher than the threshold; evaluate the quality ratios of the data groups to screen out high-quality data.

[0031] 3. Build low-code interactive components through mapping relationships; convert these data into low-code interactive components through mapping relationships, which can display data intuitively and dynamically and provide functions for interacting with users. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0033] Figure 1 This is a flow chart of a data interaction processing method based on a low-code development component according to Example 1 of the present invention.

[0034] Figure 2 A flowchart is constructed for the mapping relationship between the data groups above the threshold and the low-code interactive components of the present invention.

[0035] Figure 3 This is a schematic diagram of encapsulating the interactive objects of commonly used data groups into independent components according to the present invention.

[0036] Figure 4 This is a structural diagram of a data interaction processing system based on a low-code development component according to Example 3 of the present invention. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0038] In the drawings of the specific embodiments of the present invention, in order to better and more clearly describe the working principles of the various components in the system, the connection relationship of the various parts in the device is shown, which only clearly distinguishes the relative position relationship between the various components, and cannot constitute a limitation on the signal transmission direction, connection sequence and size, dimensions and shape of the components or structures.

[0039] Example 1

[0040] like Figure 1 As shown, it is a flow chart of a data interaction processing method based on a low-code development component according to Embodiment 1 of the present invention, and the data interaction processing method comprises the following steps:

[0041] S1. Receive a data sequence from each interface, establish a node tree for each data sequence, remove noise nodes, and form a new node tree corresponding to each interface.

[0042] S1.1. Receive a data sequence from each interface and establish a node tree for each data sequence.

[0043] A node tree is defined as an ordered data structure consisting of nodes, child nodes, and directed edges. The data sequence received from each interface can be parsed into a node tree separately. Each node in the node tree represents a label data in the data sequence, each child node represents the data element under the label data in the data sequence, and the directed edges describe the relationship between child nodes. This node tree structure provides a way to represent data structures and relationships between data.

[0044] Assume that the data sequence D received from each interface consists of n data elements and M label data, the n data elements constitute n child nodes of the node tree, and the M label data constitute M nodes of the node tree. Use the symbol V(v) to represent the set of all nodes v in the node tree.

[0045] For each node v in the node tree, the symbol T(v) represents the set of label names of the label data represented by the node v, the symbol A(v) represents the set of label attributes of the label data represented by the node v, and the symbol C(v) represents the set of child nodes contained in the label data represented by the node v, that is, the set of data elements that conform to the attributes of the label data. Among them, the label attribute elements in the label attribute set A(v) are a set of key-value pairs, representing the attribute name and attribute value of the label data element v, using an ordered tuple (a v ,value(a v )) represents the attribute name and attribute value.

[0046] The child node set C(v) is a set of nodes, indicating that every node v has a set of subordinate child nodes, so each node v can be represented as a four-tuple (V(v), T(v), A(v), C(v)).

[0047] For each node v in the node tree, if its child node set C(v) is not empty, then a directed edge c can be created for each child node. Use the symbol E(v) to represent the set of all directed edges in the node tree. Use r to represent the root node of the node tree, that is, the root element of the node tree.

[0048] In summary, the node tree D with r as the root can be expressed as D=(V(v),T(v),A(v),C(v),E(v),r).

[0049] S1.2. Classify the nodes according to the label attribute set, remove the noise nodes, and form a new node tree.

[0050] According to the representation of the node tree D (V(v), T(v), A(v), C(v), E(v), r), the nodes are classified according to the label attribute set A(v), which are the node set A1(v) of the non-data label attribute class and the node set A2(v) of the data label attribute class.

[0051] The node of the data label attribute class refers to the node representing the label data of the data type with clear and practical meaning. Including the label data of the data type, data format, and data source class. For each node, check whether it has a data label attribute. If the node has a data label attribute, add it to the node set A2(v). If the node does not have a data label attribute, add it to the node set A1(v), and define the nodes in the node set A1(v) as noise nodes.

[0052] For the node tree D = (V (v), T (v), A (v), C (v), E (v), r), the noise node set is A1 (v). All noise nodes are removed from the node tree, and the new node tree after removing the noise nodes is represented by D'.

[0053] S2. Perform data quality evaluation on the data groups in the multiple new node trees respectively to obtain a quality evaluation value of each data group.

[0054] The data sequences received by the multiple interfaces are processed in step S1 respectively to form multiple new node trees. Since data interaction processing often involves multiple data groups, it is necessary to evaluate the quality of the multiple data groups.

[0055] The quality of a data set is closely related to the discovery of necessary values ​​of the data set. The results of necessary value discovery directly affect the quality evaluation of the data set, and the quality evaluation of the data set in turn affects the discovery of necessary values.

[0056] Essential values ​​are true values ​​or values ​​closest to the true values ​​in a data set. When these values ​​are accurately determined, the overall quality of the data set is improved. Accurate essential value discovery ensures the reliability of experimental results, thereby improving data quality.

[0057] Necessary value discovery also includes determining whether the data is complete, that is, whether it contains all the necessary information. If the data is missing or incomplete, the quality of the data set will be affected. The data in each part of the data set should be consistent. Necessary value discovery can help determine whether there are contradictions or inconsistencies in the data set, which is crucial to assessing the quality of the data set.

[0058] Count the quality ratio Y of the data group in the new node tree:

[0059] The mass ratio Y is

[0060]

[0061] M is the number of nodes in the node tree before removing the noise nodes, r j is the number of data elements under the jth node, Q is the number of nodes in the new node tree after removing the noise nodes, R J is the number of data elements under the Jth node.

[0062] S3. Sort each data group according to the quality ratio, obtain the data group with a quality ratio higher than the threshold, and build a low-code interactive component through the mapping relationship.

[0063] Low-code interactive components focus on application design, and mainly study the problem of efficient data interaction application for multi-source data sets. The process of building low-code interactive components requires referring to the mapping relationship of the logical model to determine the module function level.

[0064] Sort each data set by quality ratio to obtain data sets with quality ratios higher than the threshold, such as Figure 2 As shown, the mapping relationship between the data group above the threshold and the low-code interaction component is established through the following mapping rules:

[0065] (1) Save the data groups whose quality ratio is higher than the threshold into the object set Ce;

[0066] (2) assigning a grade to the data group according to the quality ratio, and updating the grade value of the data group after the grade assignment to the object set Ce, and the object set Ce is updated after the grade assignment operation is performed;

[0067] (3) searching for all data groups with adjacent level values ​​in the object set Ce according to the level value I of the data group, and taking all data groups with adjacent level values ​​as mapping objects;

[0068] The proximity is calculated using the following formula:

[0069]

[0070] Where L represents proximity, A represents the vector of any data group whose rank value is not 1 in the object set Ce, B represents the vector of the data group whose rank value is 1, and vector A or vector B represents the vector of the data group. In machine learning and data analysis, vectors are used to represent multiple features of a data group. Vector A is a multidimensional vector, and its components may represent different data attributes.

[0071] , Respectively represent the modulus of vectors A and B. The modulus represents the magnitude of a vector. For any vector A or B, its modulus is defined as:

[0072] where m1, m2, ..., m k are the k components of a vector. The magnitude is a scalar value that describes the magnitude of a vector in multidimensional space.

[0073] When the proximity value is less than the proximity threshold, it is judged that the data group whose level value is not 1 in the object set Ce is a data group having a level value adjacent to the data group whose level value is 1.

[0074] (4) The data group in the mapping object is used as the interaction object of the data group with level value I.

[0075] In this way, data interaction can cover most common data interaction operations in software development. In order to reduce users' dependence on programming, the low-code interaction component uses a graphical interaction interface, allowing users to complete the construction of interactive applications through simple drag and drop, click and other operations. This method encapsulates complex programming logic in the background, and users can quickly build data interaction applications that meet their needs without having to deeply understand the programming details.

[0076] Preferably, if Figure 3 As shown, the interactive objects of commonly used data groups are encapsulated into independent components, such as the "device data" component, "user data" component, and "application data" component in the figure. Users can directly use these components to build their own interactive applications. By dragging and dropping, different components can be added to the interactive application and their positions and properties can be adjusted. In order to obtain the position information of any data group in the object set Ce and the drag destination, the mouse position information during dragging can be obtained by clicking and dragging any data group in the object set Ce with the mouse, that is, the position information of any data group in the object set Ce; the mouse end position information can be obtained by stopping dragging any data group in the object set Ce and releasing it, which is the position information of the drag destination.

[0077] Example 2

[0078] Based on Example 1, in step S2, this Example 2 proposes a data quality evaluation model based on necessity and accuracy to replace the quality ratio evaluation process in Example 1. In the corresponding step S3, it is necessary to sort each data group according to the quality evaluation value, obtain the data group with a quality evaluation value higher than the threshold, and construct a low-code interactive component through the mapping relationship.

[0079] The data quality evaluation model of this embodiment is applied to a data interaction processing system, and assists the data interaction processing system in determining necessary values ​​of a data group formed by multiple interface data sequences by performing quality evaluation on the data.

[0080] The data quality evaluation model is as follows:

[0081] q(w)=a*s i +(1-a)*p i

[0082] Where w represents the data set in the new node tree D'; q(w) represents the quality evaluation value of w; a is the scaling factor, a∈[0,1], s i Indicates the necessity of the i-th type of label data, p i Represents the accuracy of the i-th category label data.

[0083] The necessity of the i-th type of labeled data s i It is expressed as:

[0084]

[0085] is the number of data elements with required values ​​provided by the i-th type of label data, Indicates the number of data elements with required values ​​that are not provided in the i-th category label data.

[0086] The precision p of the i-th class labeled data i It is expressed as:

[0087]

[0088] is the number of data elements whose fuzzy values ​​are not provided by the i-th label data, The number of data elements representing the fuzzy values ​​provided by the i-th category label data.

[0089] Precision measures the quality of a data source from the perspective of fuzzy values. The higher the precision, the lower the probability that the data source provides active errors.

[0090] In data processing and data quality assessment, precision is an important indicator used to measure the quality of a data source. Specifically, precision focuses on the proportion of erroneous observations in a data source to assess the possibility of errors in the data source.

[0091] An erroneous observation is an incorrect or inaccurate value in a data series. It can be caused by data entry errors, measurement equipment failures, data transmission errors, etc.

[0092] The entity error observations represent the sum of error observations in all data sources, including ambiguous values ​​from all data series.

[0093] Precision is a reverse indicator. The higher its value does not mean the quality of the data source is better. On the contrary, the higher the precision, the greater the probability of errors in the data source. Ideally, we want the precision to be as low as possible, which means that the data source is less likely to provide wrong information and the data quality is higher.

[0094] By establishing a data quality evaluation model based on necessity and accuracy, the quality evaluation of data groups formed by different interfaces can be realized. When conflicts occur in data groups formed by multiple interfaces, necessary value discovery and data group selection can be performed based on the evaluation quality of the conflicting data groups, thereby solving the data conflict problem in the data group interaction process.

[0095] Example 3

[0096] Embodiment 3 of the present invention also provides a data interaction processing system based on low-code development components, which can implement the data interaction processing method based on low-code development components in Embodiment 1.

[0097] like Figure 4 As shown, it is a structural diagram of a data interaction processing system based on a low-code development component of Example 3 of the present invention. The data interaction processing system includes: multiple interfaces, a data processing module, a quality evaluation module, a mapping module, and a low-code interaction component.

[0098] Multiple interfaces are responsible for receiving data from external systems. These interfaces can be APIs, database connections, file imports, etc.

[0099] The data processing module receives data sequences from each interface and performs necessary processing, such as cleaning, conversion, formatting, etc. It establishes a node tree for each processed data sequence, removes noise nodes, and forms a new node tree corresponding to each interface.

[0100] In a preferred embodiment, the data processing module also needs to uniformly align the timestamps of the data sequences received by each interface to ensure that the data sequences from different interfaces can be synchronized in time. This can be achieved through methods such as timestamp calibration and interpolation. Finally, after these alignment and fusion operations, the data sequences that were originally from a wide range of sources and independent of each other will be associated to form a new, more complete and accurate data sequence.

[0101] The quality evaluation module calculates the quality ratios of the data groups in the multiple new node trees respectively to obtain the quality ratios of the data groups.

[0102] The mapping module sorts each data group according to the quality ratio, obtains the data group whose quality ratio is higher than the threshold, and builds the low-code interactive component through the mapping relationship.

[0103] Based on the output of the mapping module, the user interface and interaction logic are generated to form a low-code interaction component. The low-code interaction component provides visual data display, user input and interaction functions, allowing users to easily interact with data. The low-code interaction component can also provide users with an efficient and convenient data interaction application building experience through logical mapping, low-code design ideas and interactive editors. This not only lowers the technical threshold for data interaction processing, but also helps promote the application and development of data low-code technology in more fields.

[0104] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or a data center that includes one or more available media integrated. The available medium may be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state disk (SSD)), etc.

[0105] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A data interaction processing method based on low-code development components, characterized in that: The steps include: S1. Receive a data sequence from each interface, establish a node tree for each data sequence, remove noise nodes, and form a new node tree corresponding to each interface, including: S1.

1. Receive a data sequence from each interface and establish a node tree for each data sequence; The node tree D with r as the root is expressed as D=(V(v),T(v),A(v),C(v),E(v),r); V(v) represents the set of all nodes v in the node tree, T(v) represents the set of label names of the label data represented by node v, A(v) represents the set of label attributes of the label data represented by node v, C(v) represents the set of child nodes contained in the label data represented by node v, and E(v) represents the set of all directed edges in the node tree; S1.2, classify the nodes according to the label attribute set A(v), remove the noise nodes, and form a new node tree; According to the label attribute set A(v), the nodes are classified into the node set A1(v) of the non-data label attribute class and the node set A2(v) of the data label attribute class. The nodes in the node set A1(v) are defined as noise nodes. All noise nodes are removed from the node tree. The new node tree after removing the noise nodes is represented by D'. S2, respectively calculating the quality ratio of the data group in each new node tree to obtain the quality ratio of the data group in each node tree; The mass ratio Y is: ， Where M is the number of nodes in the node tree before removing the noise nodes, r j is the number of child nodes under the jth node, Q is the number of nodes in the new node tree after removing the noise nodes, R J is the number of child nodes under the Jth node; S3. Sort each data group according to the quality ratio, obtain the data group with a quality ratio higher than the threshold, and build a low-code interactive component through the mapping relationship; The mapping relationship between data groups above the threshold and low-code interaction components is established through the following mapping rules: (1) Save the data groups whose quality ratio is higher than the threshold into the object set Ce; (2) assigning a grade to the data group according to the quality ratio, and updating the grade value of the data group after the grade assignment to the object set Ce, and the object set Ce is updated after the grade assignment operation is performed; (3) searching for all data groups with adjacent level values ​​in the object set Ce according to the level value I of the data group, and taking all data groups with adjacent level values ​​as mapping objects; (4) taking the data group in the mapping object as the interaction object of the data group with level value I; Encapsulate the interactive objects of the data group into independent components, add different independent components to the interactive application by dragging and dropping, and adjust the position and properties of the components.

2. The data interaction processing method based on low-code development components according to claim 1 is characterized in that: The proximity is calculated using the following formula: ， Where L represents proximity, A represents the vector of any data group whose level value is not 1 in the object set Ce, and B represents the vector of the data group whose level value is 1. , Respectively represent the magnitude of vectors A and B; When the proximity value is less than the proximity threshold, it is judged that the data group whose level value is not 1 in the object set Ce is a data group having a level value adjacent to the data group whose level value is 1.

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