Electronic whiteboard mind map editing method, device and equipment and storage medium

Abstract

The invention provides an electronic whiteboard mind map editing method and device, equipment and a storage medium, and the electronic whiteboard mind map editing method comprises the steps: obtaining the node information of each node in a mind map of an electronic whiteboard, carrying out the real-time analysis of the node information of each node, determining a dynamic distance threshold value, and carrying out the dragging operation of the electronic whiteboard, thereby achieving the editing of the mind map of the electronic whiteboard. Dragging a target node in the mind map from a first position to a second position, calculating a moving distance between the first position and the second position, and when the moving distance is greater than or equal to a dynamic distance threshold value, determining a target hierarchical relationship between the target node and other nodes according to the second position and node information, the target nodes are rearranged according to the target hierarchical relationship, the rearranged target nodes are displayed, effective dragging is accurately distinguished through the dynamic distance threshold value, and therefore the target nodes are rearranged and displayed according to the target hierarchical relationship, hierarchical relationship reconstruction and display are achieved, and the editing experience of a user is improved.

Description

Technical Field

[0001] The present application relates to the field of electronic whiteboards, and in particular to a method, device, equipment and storage medium for editing mind maps on an electronic whiteboard. Background Art

[0002] With the development of electronic whiteboard technology, electronic whiteboards have become widely used in scenarios such as education and conferences. During these presentations, mind maps are used to graphically present knowledge structures. Currently, when editing mind maps, valid dragging nodes is typically determined by comparing an empirical threshold with the drag distance. However, due to differences in mind map templates or the size ratio of the display page, the number of nodes in a mind map can affect the distance between nodes. Therefore, based on a fixed empirical threshold, it is impossible to accurately distinguish valid dragging, which is not conducive to mind map editing. Summary of the Invention

[0003] The present application provides a method, apparatus, device, and storage medium for editing a mind map on an electronic whiteboard to solve at least one problem existing in the related art. The technical solution is as follows:

[0004] In a first aspect, an embodiment of the present application provides a method for editing a mind map on an electronic whiteboard, comprising:

[0005] Get the node information of each node in the mind map of the electronic whiteboard;

[0006] Perform real-time analysis on node information of each node to determine the dynamic distance threshold;

[0007] By dragging the electronic whiteboard, dragging the target node in the mind map from a first position to a second position, and calculating the moving distance between the first position and the second position;

[0008] When the moving distance is greater than or equal to the dynamic distance threshold, the target hierarchical relationship between the target node and other nodes is determined based on the second position and the node information, the target nodes are rearranged according to the target hierarchical relationship, and the rearranged target nodes are displayed.

[0009] In one embodiment, performing real-time analysis on node information of each node to determine the dynamic distance threshold includes:

[0010] Determine each node in different levels based on the node information of each node;

[0011] Calculate the node distances between adjacent nodes in different levels respectively, and calculate the average distance based on all node distances;

[0012] Determine the dynamic adjustment coefficient based on the average distance;

[0013] The dynamic distance threshold is determined based on the product of the average distance and the dynamic adjustment coefficient.

[0014] In one embodiment, determining the dynamic adjustment coefficient according to the average distance includes:

[0015] If there is no historical real drag data of the mind map:

[0016] When the average distance value is less than the first node density threshold, the first value is used as a dynamic adjustment coefficient;

[0017] When the average distance value is greater than or equal to the first node density threshold and less than the second node density threshold, the second value is used as the dynamic adjustment coefficient;

[0018] When the average distance is greater than or equal to the second node density threshold, the third value is used as a dynamic adjustment coefficient;

[0019] The third value is greater than the second value, and the second value is greater than the first value.

[0020] In one embodiment, determining the dynamic adjustment coefficient according to the average distance includes:

[0021] If there is historical real drag data of the mind map, determine the ratio of the minimum effective drag distance recorded in the historical real drag data to the average distance, and use the ratio as the dynamic adjustment coefficient.

[0022] In one embodiment, determining the target hierarchical relationship between the target node and other nodes based on the second position and the node information includes:

[0023] Determine, based on the position coordinates in the node information, a first node closest to the second position;

[0024] According to the node information of the first node, it is determined that the level of the target node is the next level of the first node, and a target level relationship between the target node and the first node is obtained.

[0025] In one embodiment, determining the target hierarchical relationship between the target node and other nodes based on the second position and the node information includes:

[0026] Determine, based on the position coordinates in the node information, a first node closest to the second position;

[0027] When there are multiple first nodes closest to the second position, all of these first nodes are recorded as candidate nodes; or, when there is only one first node closest to the second position, a second node closest to the second position other than the first node is determined, and both the first node and the second node are recorded as candidate nodes;

[0028] Perform semantic analysis on candidate nodes and target nodes to determine the semantic similarity between each candidate node and the target node;

[0029] Determine a comprehensive score for each candidate node based on the distance and semantic similarity between each candidate node and the second position, and determine the candidate node with the highest comprehensive score as the final node;

[0030] According to the node information of the final node, the level of the target node is determined to be the next level of the final node, and the target level relationship between the target node and the final node is obtained.

[0031] In one embodiment, determining the comprehensive score of each candidate node based on the distance and semantic similarity between each candidate node and the target node includes:

[0032] Normalize the distance between each candidate node and the target node, and calculate the first product of the reciprocal of the normalized distance and the first weight;

[0033] Determine a second product of the semantic similarity corresponding to each candidate node and the second weight;

[0034] The sum of the first product and the second product is determined respectively to obtain the comprehensive score of each candidate node.

[0035] In a second aspect, an embodiment of the present application provides an electronic whiteboard mind map editing device, comprising:

[0036] An acquisition module is used to obtain node information of each node in the mind map of the electronic whiteboard;

[0037] The analysis module is used to analyze the node information of each node in real time and determine the dynamic distance threshold;

[0038] A drag module, configured to drag a target node in a mind map from a first position to a second position by dragging the electronic whiteboard, and calculate a moving distance between the first position and the second position;

[0039] The display module is used to determine the target hierarchical relationship between the target node and other nodes based on the second position and node information when the moving distance is greater than or equal to the dynamic distance threshold, rearrange the target nodes according to the target hierarchical relationship, and display the rearranged target nodes.

[0040] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a processor and a memory, wherein the memory stores instructions, and the instructions are loaded and executed by the processor to implement the method in any one of the above-mentioned embodiments.

[0041] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed, it implements the method in any one of the above-mentioned embodiments.

[0042] The beneficial effects of the above technical solution include at least:

[0043] By acquiring the node information of each node in the mind map of the electronic whiteboard, performing real-time analysis on the node information of each node, determining the dynamic distance threshold, and dragging the target node in the mind map from the first position to the second position through the dragging operation on the electronic whiteboard, and calculating the moving distance between the first position and the second position, when the moving distance is greater than or equal to the dynamic distance threshold, determining the target hierarchical relationship between the target node and other nodes based on the second position and the node information, rearranging the target nodes according to the target hierarchical relationship and displaying the rearranged target nodes, accurately distinguishing effective dragging through the dynamic distance threshold, and thereby rearranging and displaying the target nodes according to the target hierarchical relationship, realizing hierarchical relationship reconstruction and display, and improving the user's editing experience.

[0044] The above summary is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments and features described above, further aspects, embodiments and features of the present application will be readily apparent by reference to the accompanying drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In the accompanying drawings, unless otherwise specified, the same reference numerals throughout the multiple drawings represent the same or similar components or elements. These drawings are not necessarily drawn to scale. It should be understood that these drawings only depict some embodiments disclosed in this application and should not be construed as limiting the scope of this application.

[0046] Figure 1 This is a flowchart of the steps of a method for editing a mind map on an electronic whiteboard according to an embodiment of the present application;

[0047] Figure 2 This is a mind map diagram before the drag operation in one embodiment of the present application;

[0048] Figure 3 This is a diagram of a mind map after a drag operation according to an embodiment of the present application;

[0049] Figure 4 This is a structural block diagram of an electronic whiteboard mind map editing device according to an embodiment of the present application;

[0050] Figure 5 This is a structural block diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0051] Hereinafter, only certain exemplary embodiments are briefly described. As will be appreciated by those skilled in the art, the described embodiments may be modified in various ways without departing from the spirit or scope of the present application. Therefore, the drawings and description are to be regarded as illustrative in nature and not restrictive.

[0052] Reference Figure 1 , showing a flow chart of an electronic whiteboard mind map editing method according to an embodiment of the present application, the electronic whiteboard mind map editing method may include at least steps S100-S400:

[0053] S100: Obtain node information of each node in the mind map of the electronic whiteboard.

[0054] S200: Analyze the node information of each node in real time to determine a dynamic distance threshold.

[0055] S300: drag a target node in the mind map from a first position to a second position by dragging the electronic whiteboard, and calculate a moving distance between the first position and the second position.

[0056] S400: When the moving distance is greater than or equal to the dynamic distance threshold, determine the target hierarchical relationship between the target node and other nodes based on the second position and node information, rearrange the target nodes according to the target hierarchical relationship, and display the rearranged target nodes.

[0057] The electronic whiteboard mind map editing method of the embodiment of the present application can be executed by the processor of the electronic whiteboard.

[0058] The technical solution of the embodiment of the present application obtains the node information of each node in the mind map of the electronic whiteboard, performs real-time analysis on the node information of each node, determines the dynamic distance threshold, and drags the target node in the mind map from the first position to the second position through the drag operation of the electronic whiteboard, and calculates the moving distance between the first position and the second position. When the moving distance is greater than or equal to the dynamic distance threshold, the target hierarchical relationship between the target node and other nodes is determined according to the second position and the node information, the target nodes are rearranged according to the target hierarchical relationship and the rearranged target nodes are displayed, and the effective drag is accurately distinguished by the dynamic distance threshold, so that the target nodes are rearranged and displayed according to the target hierarchical relationship, hierarchical relationship reconstruction and display are realized, and the user's editing experience is improved.

[0059] In one embodiment, in step S100, when the mind map is displayed on the screen of the electronic whiteboard, the node information of each node in the mind map of the electronic whiteboard is obtained, and the node information includes but is not limited to the position coordinates of the node center on the screen, the level it is in, which node is the corresponding parent node, etc. Figure 2 As shown, for example, node 1 is the parent node of nodes 21, 22, and 23, and nodes 21, 22, and 23 are at the first level. Nodes 211, 212, 213, 214, 221, 222, 223, 224, 231, 232, 233, and 234 are at the second level. Node 21 is the parent node of nodes 211, 212, 213, and 214, and node 22 is the parent node of nodes 221, 222, 223, and 224. Node 23 is the parent node of nodes 231, 232, 233, and 234.

[0060] In one embodiment, step S200 includes steps S210-S240:

[0061] S210: Determine each node in different levels according to the node information of each node.

[0062] like Figure 2 As shown, based on the node information of the nodes, it can be determined that the first level has nodes 21, 22, and 23, and the second level has nodes 211, 212, 213, 214, 221, 222, 223, 224, 231, 232, 233, and 234.

[0063] S220 , respectively calculating the node distances between adjacent nodes in different levels, and calculating the average distance based on all node distances.

[0064] Optionally, the node distances between adjacent nodes in different levels (specifically, adjacent nodes belonging to the same parent node) are calculated separately. Since the node distances of adjacent nodes in the same level that usually belong to the same parent node are the same, only one group needs to be calculated. For example, the node distance between the center of node 21 and the center of node 22 is calculated as the node distance between adjacent nodes in the first level, and then the node distance between the center of node 213 and the center of node 214 is calculated as the node distance between adjacent nodes in the second level. It can be understood that if there are multiple levels, the calculation method is similar. Finally, the distance average is calculated based on all node distances. For example, in the above example, the distance average is (node ​​distance of the first level + node distance of the second level) / 2.

[0065] S230: Determine a dynamic adjustment coefficient based on the average distance value.

[0066] Optionally, based on whether there is historical real drag data of the mind map, there are two different methods, such as steps S2301 and S2302:

[0067] S2301. If there is no historical real drag data of the mind map: when the average distance is less than the first node density threshold, the first value is used as the dynamic adjustment coefficient; when the average distance is greater than or equal to the first node density threshold and less than the second node density threshold, the second value is used as the dynamic adjustment coefficient; when the average distance is greater than or equal to the second node density threshold, the third value is used as the dynamic adjustment coefficient; wherein the third value is greater than the second value, and the second value is greater than the first value.

[0068] Optionally, if there is no historical real drag data of the mind map, it means that the user may be using the mind map function of the electronic whiteboard for the first time. The processor of the electronic whiteboard is set with a first node density threshold and a second node density threshold by default. The first node density threshold and the second node density threshold can also be obtained by using AI analysis after the user allows the collection of mind map usage data, without specific limitation.

[0069] In the embodiment of the present application, when the distance average value d avg Less than the first node density threshold, such as the average distance d avg If it is less than the first node density threshold of 50px, it means that it is a high-density area and a smaller dynamic distance threshold is needed to avoid misoperation. At this time, the first value 0.4 is used as the dynamic adjustment coefficient k.

[0070] When the distance from the mean value d avg If the value is greater than or equal to the first node density threshold of 50px and less than the second node density threshold of 100px, it indicates that the area is a medium-density area, and the second value 0.5 is used as the dynamic adjustment coefficient k.

[0071] When the distance from the mean value d avg If the value is greater than or equal to the second node density threshold of 100px, it indicates a high-density area. The dynamic distance threshold needs to be relaxed to improve operation smoothness. The third value 0.6 is used as the dynamic adjustment coefficient.

[0072] S2302: If there is historical real drag data of the mind map, determine the ratio of the minimum effective drag distance recorded in the historical real drag data to the average distance, and use the ratio as a dynamic adjustment coefficient.

[0073] Optionally, if there is historical real drag data of the mind map, the minimum effective drag distance recorded in the historical real drag data is determined. For example, after the user completes the drag operation, whether the drag is valid can be determined by whether the user feedback is correct or whether the user's withdrawal operation is received within a preset time. If the user feedback is correct or the user's withdrawal operation is not received within the preset time, it means that the current drag operation is valid. At this time, the effective drag distance is recorded, that is, the distance between the initial position of the dragged node and the end position of the dragged node in the drag operation at this time, so that the minimum effective drag distance d can be obtained. min Then, determine the minimum effective drag distance d min The distance from the mean value d avg Ratio As the dynamic adjustment coefficient k.

[0074] In another possible implementation, if there is historical real drag data, the dynamic adjustment coefficient calculated in S2301 is recorded as the first candidate adjustment coefficient, and the minimum effective drag distance d is set as min The distance from the mean value d avg Ratio Recorded as the second candidate adjustment coefficient, and then the average of the first candidate adjustment coefficient and the second candidate adjustment coefficient is calculated as the final dynamic adjustment coefficient k.

[0075] S240: Determine a dynamic distance threshold according to the product of the average distance value and the dynamic adjustment coefficient.

[0076] Therefore, the dynamic distance threshold Y = k × d avg .

[0077] In one embodiment, in step S300, the user can drag the screen of the electronic whiteboard to move the position of the node. Figure 2 As shown, it is assumed that the user moves the target node 22 from the original first position to the second position near the nodes 212 and 213 by dragging. Figure 2 At this time, the processor can automatically calculate the moving distance between the first position and the second position (calculated by Euclidean distance), and the first position and the second position are both the center positions of the target node.

[0078] In one embodiment, when the moving distance is greater than or equal to the dynamic distance threshold in step S400, it indicates that the drag is valid. The target hierarchical relationship between the target node and other nodes is determined based on the second position and the node information, which may specifically include steps S410-S420:

[0079] S410: Determine a first node closest to the second position according to the position coordinates in the node information.

[0080] For example, the distances between the position coordinates in the node information (ie, the coordinates of the node center) and the second position are calculated respectively, thereby determining the first node closest to the second position (ie, the distance is the shortest).

[0081] S420: Determine, based on the node information of the first node, that the level of the target node is a level below the first node, and obtain a target level relationship between the target node and the first node.

[0082] For example, assuming that the first node is determined to be node 213, the level of the target node 22 can be determined to be the next level of the first node 213, that is, after moving, the first node 213 is the parent node of the target node 22, thereby obtaining the target level relationship between the target node 22 and the first node 213.

[0083] In one embodiment, when the moving distance is greater than or equal to the dynamic distance threshold in step S400, it indicates that the drag is valid. Determining the target hierarchical relationship between the target node and other nodes based on the second position and the node information may include steps S430-S470:

[0084] S430: Determine a first node closest to the second position according to the position coordinates in the node information.

[0085] Among them, those similar to S410 are not repeated here.

[0086] S440. When there are multiple first nodes closest to the second position, all of these first nodes are recorded as candidate nodes. Alternatively, when there is only one first node closest to the second position, the second node closest to the second position other than the first node is determined, and both the first node and the second node are recorded as candidate nodes.

[0087] It should be noted that if Figure 2 In some cases, there may be multiple first nodes at the second position after the user drags, with the same distance from the second position. For example, nodes 212 and 213 are both the first nodes closest to node 22 at the second position. In this case, nodes 212 and 213 are considered candidate nodes. In another case, due to possible errors in user dragging, when there is only one first node closest to the second position, the second node closest to the second position is further determined, and both the first and second nodes are recorded as candidate nodes for further analysis.

[0088] S450: Perform semantic analysis on the candidate nodes and the target node to determine the semantic similarity between each candidate node and the target node.

[0089] Optionally, a semantic analysis is performed on each candidate node and the target node using a Bert model to determine a semantic similarity S between each candidate node and the target node.

[0090] S460 , determining a comprehensive score for each candidate node based on the distance and semantic similarity between each candidate node and the second position, and determining the candidate node with the highest comprehensive score as the final node.

[0091] Specifically, the distance between each candidate node and the target node is normalized, and the first product of the inverse of the normalized distance D and the first weight W1 is calculated.

[0092] Determine the second product S×W2 of the semantic similarity S corresponding to each candidate node and the second weight W2;

[0093] Determine the sum of the first product and the second product respectively to obtain the comprehensive score of each candidate node:

[0094]

[0095] It should be noted that the candidate node with the highest comprehensive score is determined as the final node, and the first weight W1 is greater than the second weight W2. For example, the first weight W1 is 0.65 and the second weight W2 is 0.35.

[0096] S470: Determine, based on the node information of the final node, that the level of the target node is the next level of the final node, and obtain a target level relationship between the target node and the final node.

[0097] Alternatively, as Figure 2 As shown, for example, the final node is determined to be node 213. At this time, based on the node information of node 213, since node 213 belongs to the second level, the level of the target node 22 can be determined to be the third level, that is, the level of the target node 22 is determined to be the next level of the final node 213, and the target level relationship between the target node 22 and the final node 213 is obtained, and the final node 213 is the parent node of the target node 22.

[0098] In one embodiment, in step S400, the target nodes are rearranged according to the target hierarchical relationship and the rearranged target nodes are displayed. For example, in the above example, after the target hierarchical relationship between the target node 22 and the final node 213 is determined, the processor rearranges the target nodes according to the target hierarchical relationship, i.e., moves the target node 22 to the next level of the final node 213, and displays the rearrangement result, i.e., displays the rearranged target nodes. It should be noted that since there are nodes 221, 222, 223, and 224 under the target node 22, nodes 221, 222, 223, and 224 will move with the target node 22 to ensure that nodes 221, 222, 223, and 224 are the next level of the target node 22, so as to achieve accurate rearrangement and reconstruction results, as shown in FIG. Figure 3 shown.

[0099] Reference Figure 4 , shows a structural block diagram of an electronic whiteboard mind map editing device according to an embodiment of the present application, which may include:

[0100] An acquisition module is used to obtain node information of each node in the mind map of the electronic whiteboard;

[0101] The analysis module is used to analyze the node information of each node in real time and determine the dynamic distance threshold;

[0102] A drag module, configured to drag a target node in a mind map from a first position to a second position by dragging the electronic whiteboard, and calculate a moving distance between the first position and the second position;

[0103] The display module is used to determine the target hierarchical relationship between the target node and other nodes based on the second position and node information when the moving distance is greater than or equal to the dynamic distance threshold, rearrange the target nodes according to the target hierarchical relationship, and display the rearranged target nodes.

[0104] The functions of each module in the device of the embodiment of the present application can be found in the corresponding description in the above method and will not be repeated here.

[0105] Reference Figure 5 , shows a block diagram of an electronic device according to an embodiment of the present application. The electronic device includes a memory 310 and a processor 320. The memory 310 stores instructions executable on the processor 320. The processor 320 loads and executes the instructions to implement the electronic whiteboard mind map editing method according to the above embodiment. The number of the memory 310 and the processor 320 can be one or more.

[0106] In one embodiment, the electronic device further includes a communication interface 330 for communicating with external devices and performing data exchange transmission. If the memory 310, the processor 320, and the communication interface 330 are implemented independently, the memory 310, the processor 320, and the communication interface 330 can be interconnected through a bus and complete communication with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0107] Optionally, in a specific implementation, if the memory 310, the processor 320 and the communication interface 330 are integrated on a chip, the memory 310, the processor 320 and the communication interface 330 can communicate with each other through an internal interface.

[0108] An embodiment of the present application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the electronic whiteboard mind map editing method provided in the above embodiment.

[0109] An embodiment of the present application also provides a chip, which includes a processor for calling and executing instructions stored in the memory from the memory, so that a communication device equipped with the chip executes the method provided in the embodiment of the present application.

[0110] An embodiment of the present application also provides a chip, including: an input interface, an output interface, a processor and a memory. The input interface, the output interface, the processor and the memory are connected through an internal connection path. The processor is used to execute the code in the memory. When the code is executed, the processor is used to execute the method provided in the embodiment of the application.

[0111] It should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. It is worth noting that the processor may be a processor that supports the advanced reduced instruction set machine (ARM) architecture.

[0112] Furthermore, optionally, the above-mentioned memory may include read-only memory and random access memory, and may also include non-volatile random access memory. The memory may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. Among them, the non-volatile memory may include read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM) or flash memory. The volatile memory may include random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available. For example, static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM) and direct rambus RAM (DR RAM).

[0113] In the above embodiments, all or part of the embodiments may be implemented using software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments may be implemented in the form of a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions according to the present application are 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 transferred from one computer-readable storage medium to another.

[0114] In the description of this specification, the reference terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" mean that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. Moreover, the specific features, structures, materials, or characteristics described may be combined in any appropriate manner in any one or more embodiments or examples. In addition, those skilled in the art may combine and integrate different embodiments or examples described in this specification, as well as features of different embodiments or examples, unless they are mutually inconsistent.

[0115] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of technical features indicated. Thus, features specified as "first" or "second" may explicitly or implicitly include at least one of such features. In the description of this application, "plurality" means two or more, unless otherwise specifically defined.

[0116] Any process or method description in a flow chart or otherwise described herein can be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a specific logical function or process. The scope of the preferred embodiments of the present application includes additional implementations in which the functions may be performed in a different order than shown or discussed, including in a substantially simultaneous manner or in a reverse order depending on the functions involved.

[0117] The logic and / or steps represented in the flowchart or otherwise described herein may be considered, for example, as an ordered list of executable instructions for implementing logical functions, and may be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device).

[0118] It should be understood that various parts of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. All or part of the steps of the above embodiment method can be completed by instructing the relevant hardware through a program, which can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0119] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing module, or each unit may exist physically separately, or two or more units may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or in the form of software functional modules. If the aforementioned integrated modules are implemented in the form of software functional modules and sold or used as independent products, they may also be stored in a computer-readable storage medium. The storage medium may be a read-only memory, a magnetic disk, or an optical disk, etc.

[0120] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any person skilled in the art can easily conceive of various modifications or substitutions within the technical scope disclosed in this application, and such modifications or substitutions should be included within the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A method for editing a mind map on an electronic whiteboard, characterized in that: include: Get the node information of each node in the mind map of the electronic whiteboard; Perform real-time analysis on node information of each node to determine the dynamic distance threshold; By dragging the electronic whiteboard, dragging the target node in the mind map from a first position to a second position, and calculating the moving distance between the first position and the second position; When the moving distance is greater than or equal to the dynamic distance threshold, the target hierarchical relationship between the target node and other nodes is determined based on the second position and the node information, the target nodes are rearranged according to the target hierarchical relationship, and the rearranged target nodes are displayed.

2. The method for editing a mind map on an electronic whiteboard according to claim 1, wherein: The real-time analysis of node information of each node to determine the dynamic distance threshold includes: Determine each node in different levels based on the node information of each node; Calculate the node distances between adjacent nodes in different levels respectively, and calculate the average distance based on all node distances; Determine the dynamic adjustment coefficient based on the average distance; The dynamic distance threshold is determined based on the product of the average distance and the dynamic adjustment coefficient.

3. The method for editing a mind map on an electronic whiteboard according to claim 2, characterized in that: Determining the dynamic adjustment coefficient according to the average distance includes: If there is no historical real drag data of the mind map: When the average distance value is less than the first node density threshold, the first value is used as a dynamic adjustment coefficient; When the average distance value is greater than or equal to the first node density threshold and less than the second node density threshold, the second value is used as the dynamic adjustment coefficient; When the average distance is greater than or equal to the second node density threshold, the third value is used as a dynamic adjustment coefficient; The third value is greater than the second value, and the second value is greater than the first value.

4. The method for editing a mind map on an electronic whiteboard according to claim 2, wherein: Determining the dynamic adjustment coefficient according to the average distance includes: If there is historical real drag data of the mind map, determine the ratio of the minimum effective drag distance recorded in the historical real drag data to the average distance, and use the ratio as the dynamic adjustment coefficient.

5. The method for editing a mind map on an electronic whiteboard according to any one of claims 1 to 4, characterized in that: Determining the target hierarchical relationship between the target node and other nodes based on the second position and the node information includes: Determine, based on the position coordinates in the node information, a first node closest to the second position; According to the node information of the first node, it is determined that the level of the target node is the next level of the first node, and a target level relationship between the target node and the first node is obtained.

6. The method for editing a mind map on an electronic whiteboard according to any one of claims 1 to 4, characterized in that: Determining the target hierarchical relationship between the target node and other nodes based on the second position and the node information includes: Determine, based on the position coordinates in the node information, a first node closest to the second position; When there are multiple first nodes closest to the second position, all of these first nodes are recorded as candidate nodes; or, when there is only one first node closest to the second position, a second node closest to the second position other than the first node is determined, and both the first node and the second node are recorded as candidate nodes; Perform semantic analysis on candidate nodes and target nodes to determine the semantic similarity between each candidate node and the target node; Determine a comprehensive score for each candidate node based on the distance and semantic similarity between each candidate node and the second position, and determine the candidate node with the highest comprehensive score as the final node; According to the node information of the final node, the level of the target node is determined to be the next level of the final node, and the target level relationship between the target node and the final node is obtained.

7. The method for editing a mind map on an electronic whiteboard according to claim 6, characterized in that: Determining the comprehensive score of each candidate node based on the distance and semantic similarity between each candidate node and the target node includes: Normalize the distance between each candidate node and the target node, and calculate the first product of the reciprocal of the normalized distance and the first weight; Determine a second product of the semantic similarity corresponding to each candidate node and the second weight; The sum of the first product and the second product is determined respectively to obtain the comprehensive score of each candidate node.

8. An electronic whiteboard mind map editing device, characterized in that: include: An acquisition module is used to obtain node information of each node in the mind map of the electronic whiteboard; The analysis module is used to analyze the node information of each node in real time and determine the dynamic distance threshold; A drag module, configured to drag a target node in a mind map from a first position to a second position by dragging the electronic whiteboard, and calculate a moving distance between the first position and the second position; The display module is used to determine the target hierarchical relationship between the target node and other nodes based on the second position and node information when the moving distance is greater than or equal to the dynamic distance threshold, rearrange the target nodes according to the target hierarchical relationship, and display the rearranged target nodes.

9. An electronic device, characterized in that: include: A processor and a memory, wherein the memory stores instructions, and the instructions are loaded and executed by the processor to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is executed, the method according to any one of claims 1 to 7 is implemented.

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