A cloud-based intelligent editing method for drawings

Abstract

The present invention discloses a cloud-based intelligent drawing editing method, which relates to the technical field of drawing editing, and the method comprises: setting the editing target area of ​​the drawing through the cloud, performing target analysis according to the editing target area, obtaining an editing direct target layer and an effect recognition collaborative layer; generating layer locking instructions and collaborative display instructions according to the editing direct target layer and the effect recognition collaborative layer respectively; editing the drawing based on the editing direct target layer, performing editing effect recognition evaluation through the effect recognition collaborative layer, and outputting editing annotations. The present invention solves the technical problems of the prior art that the editing area is not accurate, the layer relationship is not fully considered, and the editing tool selection is unreasonable, and achieves the technical effect of improving the accuracy and efficiency of cloud-based drawing editing by accurately determining the editing area, intelligently analyzing and identifying the layer, and efficiently executing the editing operation.

Description

Technical Field

[0001] The present invention relates to the technical field of drawing editing, and in particular to a cloud-based intelligent drawing editing method. Background Art

[0002] With the continuous development of fields such as architecture, engineering, and design, the importance of cloud-based drawing editing has become increasingly prominent. However, traditional cloud-based drawing editing methods face a series of problems. First, there is a lack of accuracy in determining the editing area, which often requires a lot of time to identify and define manually, and it is easy to make incorrect selections or miss key areas. Secondly, the handling of layer relationships is not perfect, and it is difficult to fully and accurately identify the direct target layers that are closely related to the editing operation and the collaborative layers that may be affected by the effect identification, which may lead to the neglect of some important layer association effects during the editing process. Furthermore, the selection of editing tools lacks intelligence, and editors need to rely on their own experience to select suitable tools from a large number of CAD tools with different functions. This is inefficient when facing complex editing tasks and it is difficult to ensure the rationality of the selection.

[0003] The existing technology has technical problems such as inaccurate editing area, incomplete consideration of layer relationships, and unreasonable selection of editing tools. Summary of the invention

[0004] The present application provides a cloud-based intelligent drawing editing method, which is used to solve the technical problems in the prior art such as inaccurate editing areas, incomplete consideration of layer relationships, and unreasonable selection of editing tools.

[0005] In view of the above problems, the present application provides a cloud-based intelligent drawing editing method, the method comprising:

[0006] The editing target area of ​​the drawing is set through the cloud, and the target is parsed according to the editing target area to obtain the editing direct target layer and the effect recognition collaborative layer; according to the editing direct target layer and the effect recognition collaborative layer, a layer locking instruction and a collaborative display instruction are generated respectively, and the layer locking instruction is used to lock the editing direct target layer, and the collaborative display instruction is used to perform editing and synchronous viewing operations on the effect recognition collaborative layer; the drawing is edited based on the editing direct target layer, and the editing effect recognition evaluation is performed through the effect recognition collaborative layer, and the editing annotations are output, and the editing annotations can be collaboratively displayed through the collaborative display instructions.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] The editing target area of ​​the drawing is set in the cloud, and the target is parsed according to the editing target area to obtain the editing direct target layer and the effect recognition collaborative layer; according to the editing direct target layer and the effect recognition collaborative layer, the layer locking instruction and the collaborative display instruction are generated respectively; the drawing is edited based on the editing direct target layer, and the editing effect recognition and evaluation are performed through the effect recognition collaborative layer, and the editing annotation is output. The technical effect of improving the accuracy and efficiency of cloud drawing editing by accurately determining the editing area, intelligently parsing and identifying layers, and efficiently executing editing operations has been achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0010] Figure 1 A schematic diagram of a process flow of a cloud-based drawing intelligent editing method provided in an embodiment of the present application;

[0011] Figure 2 A schematic diagram of the edge node flow of a connection matching operation function of a cloud-based drawing intelligent editing method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0012] The present application provides a cloud-based intelligent drawing editing method to solve the technical problems in the prior art of inaccurate editing areas, incomplete consideration of layer relationships, and unreasonable selection of editing tools.

[0013] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only 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.

[0014] Examples, such as Figure 1 As shown, the present application provides a cloud-based drawing intelligent editing method, the method comprising:

[0015] Step S100: setting the editing target area of ​​the drawing through the cloud, performing target analysis according to the editing target area, and obtaining the editing direct target layer and the effect recognition collaborative layer.

[0016] Specifically, during the cloud drawing editing process, the editing target area of ​​the drawing is set according to specific needs through the cloud platform. This operation can be achieved in many ways, such as using specific tools to select a box on the cloud interface, or entering a specific coordinate range to determine the area. The setting of this editing target area provides a clear direction for subsequent operations. According to this determined editing target area, target parsing is performed. In this parsing process, various characteristics of the area are deeply analyzed. On the one hand, the layer structure in the area is analyzed to determine the relationship and hierarchy between different layers. On the other hand, the object properties are analyzed, including the type, size, color and other characteristics of the design elements. At the same time, the operation content, that is, the type of editing operation that the user may perform, is also considered. Through the comprehensive analysis of these aspects, the editing direct target layer can be accurately identified. This layer is the location of the object where the editing operation is directly performed, and contains specific design elements or graphic parts. At the same time, the effect recognition collaborative layer is also determined. This layer is a layer associated with the editing direct target layer. During the editing process, it can reflect the impact of the editing operation on other parts. For example, if the editing direct target layer is modified, the effect recognition collaborative layer will show effects such as color changes and spatial position adjustments, thereby helping users better evaluate the scope and effect of the editing operation.

[0017] Step S200: Generate a layer locking instruction and a collaborative display instruction respectively according to the editing direct target layer and the effect identification collaborative layer, wherein the layer locking instruction is used to lock the editing direct target layer, and the collaborative display instruction is used to perform editing and synchronous viewing operations on the effect identification collaborative layer.

[0018] Specifically, a layer lock instruction is generated based on the editing direct target layer. In the scenario where multiple people are collaborating to edit the same CAD drawing, in order to avoid conflicts, each user uses the layer lock instruction to lock the editing direct target layer to which the part to be edited belongs after determining it. This ensures that only one user can modify a specific layer or element at the same time. Although other users cannot modify the locked part, they can view its status and understand the current editing progress. This mechanism effectively prevents confusion and errors caused by multiple users editing the same part at the same time, and ensures the orderliness of the editing process. Other users can only operate on that part after the current user completes editing or actively unlocks it, thereby achieving high efficiency and accuracy in multi-person collaboration.

[0019] Secondly, generate collaborative display instructions based on the effect recognition collaborative layer. During the drawing editing process, different layers are often closely connected. The editing of one layer may affect other layers. When the edited layer has a relationship and mutual influence with other layers, the collaborative display instruction plays an important role. Through this instruction, users can synchronously view the changes of the effect recognition collaborative layer during the editing process, which enables users to fully consider the functional coordination with other layers and the matching of parameters such as size when editing drawings. For example, when adjusting the size of a layer, by synchronously viewing the effect recognition collaborative layer, it can be found in time whether the adjustment will affect the coordination effect of other layers, so as to make corresponding adjustments to ensure the integrity and coordination of the entire drawing. Such a collaborative display mechanism greatly improves the quality and efficiency of drawing editing, making multi-person collaborative editing smoother and more efficient.

[0020] Step S300: Editing the drawing based on the editing direct target layer, performing editing effect recognition and evaluation through the effect recognition collaborative layer, and outputting editing annotations, which can be collaboratively displayed through the collaborative display instructions.

[0021] Specifically, the drawing is edited based on the editing direct target layer, focusing on this specific layer to perform various specific editing operations, such as adding new graphic elements, modifying the shape of existing elements, adjusting colors, etc. Since the layer has been locked in step S200, it is ensured that there will be no interference from other users during the editing process, so that the required editing tasks can be completed efficiently.

[0022] At the same time, the editing effect recognition and evaluation is performed through the effect recognition collaborative layer. The effect recognition collaborative layer is closely related to the editing direct target layer. When the editing direct target layer is edited, the effect recognition collaborative layer will change accordingly. According to these changes, the effect of the editing operation is evaluated to determine whether the expected design goal has been achieved. For example, if the size of a graphic is adjusted on the editing direct target layer, the position or proportion of other related graphics will be observed in the effect recognition collaborative layer. In this way, problems that may arise in the editing process can be discovered in a timely manner so that adjustments and optimizations can be made.

[0023] Finally, the editing annotations are output. These annotations are a record and feedback of the editing process and effect, including the description of the editing operation, the problems encountered, the improvement suggestions, etc. These editing annotations can be collaboratively displayed through collaborative display instructions, which means that in a multi-person collaborative editing environment, all users participating in the editing can see these annotations, better understand the progress and situation of the editing, communicate and discuss, jointly solve problems, and improve the quality and efficiency of drawing editing. At the same time, collaborative display of annotations also helps maintain the consistency of team collaboration, thereby achieving better editing effects and ultimate design goals.

[0024] In a possible implementation, step S300 further includes:

[0025] Step S310: Obtain the editing operation type.

[0026] Step S320: Based on the editing operation type, a multi-operation function edge node is obtained, where the multi-operation function edge node is an editor constructed according to functional features of different CAD editing tools.

[0027] Step S330: Select a target function in the multi-operation function edge node, connect to a matching operation function edge node, load the editing operation task into the matching operation function edge node to perform a drawing editing operation, and transmit the editing completion result back to the cloud.

[0028] Specifically, obtaining the type of editing operation is a key link. Different CAD tools have differences in functions. Some have advantages in drawing annotation and can provide accurate and rich annotation functions; others are better at processing design elements, with a more diverse library of design elements and powerful processing capabilities. In view of these differences, corresponding edge editing nodes are built for different tool types, providing cloud users with a wealth of choices. Cloud users integrate multiple types of functional processing nodes and make flexible choices based on specific editing needs. For example, when a user needs to annotate a drawing in detail, select an edge editing node with a powerful annotation function; and when it is necessary to enrich the design elements, you can switch to a node that has advantages in design element processing. This mechanism enables users to make full use of the characteristics of different CAD tools, improve the efficiency and quality of drawing editing, and meet various complex editing needs.

[0029] It is of great significance to obtain multi-operation function edge nodes based on the determined editing operation type. Since different CAD editing tools have their own unique functional characteristics, multi-operation function edge nodes are constructed to meet diverse editing needs. These edge nodes are editors built according to the advantages and characteristics of different CAD tools. For example, some CAD tools perform well in graphics drawing and can provide high-precision drawing functions and rich drawing tool options. The edge nodes built for such tools will play an important role in editing operations related to graphics drawing. Other CAD tools have expertise in dimensioning, layer management or three-dimensional modeling, and the corresponding edge nodes will also show powerful functions in the corresponding editing operation types. In this way, the functional advantages of different CAD editing tools are integrated into multi-operation function edge nodes, providing users with richer and more flexible choices when editing drawings. When the user determines a specific editing operation type, the node that best suits the current task can be quickly selected from these multi-operation function edge nodes to ensure that the user can complete the drawing editing work in the most efficient way.

[0030] Users select the target function from a large number of multi-operation function edge nodes. Since these edge nodes are built according to the functional characteristics of different CAD editing tools, they are selected according to specific needs and usage habits. For example, if the user is performing a complex graphics drawing task, select an edge node with powerful graphics drawing functions. Once the target function is determined, the matching operation function edge node will be connected. This connection process ensures that the editing operation can be accurately performed in the appropriate environment. Then, the editing operation task is loaded into the matching operation function edge node for drawing editing. At this stage, the edge node uses its specific functions and advantages to perform efficient and accurate editing of the drawings. Whether it is the modification of graphics, the addition of annotations, or the adjustment of layers, operations can be properly handled in this dedicated edge node. When the editing operation is completed, the results will be sent back to the cloud. Users use different tools to edit on different devices. By sending the editing results back to the cloud, data synchronization and collaborative work between different devices can be achieved. All users participating in the editing can view the edited results in real time on the cloud for further discussion and adjustment. At the same time, the cloud storage and management functions also ensure the security and accessibility of data, allowing users to continue editing at any time and any place, greatly improving work efficiency and collaboration capabilities.

[0031] In a possible implementation, step S320 further includes:

[0032] Step S321: Collect multiple types of CAD tools, perform drawing function recognition and evaluation on the multiple types of CAD tools, and select tools whose target drawing function evaluation results meet preset requirements.

[0033] Step S322: Construct edge editing nodes for each screening target tool.

[0034] Step S323: parse the functional characteristics of the screening target tool, generate a functional parsing label, establish a mapping association between the target tool name and the functional parsing label, and add the mapping association label to the edge editing node to describe the operation function.

[0035] Specifically, we first collect various types of CAD tools, which means collecting a wide range of different CAD software tools from different developers with different functional characteristics and applicable scenarios. We then identify and evaluate the drawing functions of these various types of CAD tools. In this evaluation process, we conduct in-depth analysis of the drawing functions of each tool from multiple perspectives. For example, we examine the drawing accuracy of the tool to see whether it can accurately draw complex graphic details; we evaluate the drawing speed to determine the efficiency when processing large amounts of graphic data; we check whether the graphics types supported by the tool are rich and diverse and whether they can meet different design requirements; we also pay attention to whether the tool has some special drawing auxiliary functions, such as automatic alignment and intelligent capture. Through these comprehensive evaluation indicators, we have a more accurate understanding of the drawing function of each CAD tool. Then, we screen these evaluation results according to the preset requirements. The preset requirements are determined according to the specific drawing editing project requirements. For example, for an engineering drawing editing task with extremely high precision requirements, we screen out those CAD tools with high drawing precision and precise annotation functions. Through a rigorous screening process, we ensure that the tools that are finally left can meet the needs of specific projects in terms of drawing functions, providing reliable tool support for subsequent drawing editing work.

[0036] For the target tools screened out in step S321, edge editing nodes are constructed respectively, and for each screening target tool that meets the preset requirements, an edge editing node is constructed. This edge editing node is customized according to the characteristics and functions of the specific tool. Its role is to better integrate and utilize the advantages of the tool so that its functions can be efficiently called during the drawing editing process. For example, if a certain screening target tool performs well in the speed and accuracy of graphics drawing, then the edge editing node constructed for it will optimize the data transmission and processing flow, ensure that it can respond quickly when using the tool for drawing operations, and accurately integrate the graphics drawn into the entire drawing. At the same time, the edge editing node also designs corresponding interfaces and interaction methods based on the tool's operating interface and command system, so that users can use this tool for editing more conveniently and smoothly. By constructing edge editing nodes for each screening target tool respectively, the advantages of different tools are fully utilized, providing a richer and more efficient tool selection for drawing editing to meet various complex editing needs.

[0037] The function characteristics of the selected target tools are analyzed to analyze their functional performance in different aspects. For example, whether the tool can achieve high-precision line drawing in graphics drawing, whether it supports the import and export of multiple graphics formats; in editing operations, whether it has convenient undo and redo functions, whether it can quickly manage layers, etc. Through a detailed analysis of these functional characteristics, the unique advantages and applicable scenarios of each target tool can be accurately grasped. Functional analysis tags are generated according to the results of functional feature analysis. These tags are concise summaries and descriptions of the functions of the target tools, such as "high-precision drawing tools", "efficient layer management tools", "multi-format compatible tools", etc. Each tag can intuitively reflect an important functional feature of the tool. Then, a mapping association is established between the target tool name and the functional analysis tag. The purpose is to quickly find the corresponding functional description through the tool name in the subsequent use process, so that users can choose the appropriate tool. For example, when a user sees a tool name, the mapping association can immediately understand the main functional features of the tool, so as to determine whether it is suitable for the current editing task. Finally, the mapping association label is added to the edge editing node to describe the operation function. In this way, in the edge editing node, the user can not only see the name of the tool, but also understand the specific functions and advantages of the tool through the functional description label. This helps the user to more accurately understand the function of the tool corresponding to each node when selecting the edge editing node, so as to perform drawing editing operations more efficiently. Through this series of steps, the functional characteristics of the screening target tools are clearly presented and effectively utilized, providing users with a more convenient and efficient drawing editing environment.

[0038] In one possible implementation, Figure 2 As shown, step S330 also includes:

[0039] Step S331: Obtain the load operation characteristics of the multi-operation function edge node, where the load operation characteristics are used to characterize the current load processing status of the edge node.

[0040] Step S332: parsing the loading processing load characteristics of the cloud drawing based on the editing operation task, wherein the loading processing load characteristics are used to characterize the processing parameter characteristics required by the current task.

[0041] Step S333: Use the load processing load characteristics and the load operation characteristics to evaluate the loading efficiency and obtain a loading evaluation result.

[0042] Step S334: The loading evaluation result is used as node selection feedback information for cloud editing users to select target functions and determine the matching operation function edge node.

[0043] Specifically, it is of great significance to obtain the load operation characteristics of multi-operation function edge nodes. These load operation characteristics are key indicators for understanding the current working status of edge nodes. Load operation characteristics include multiple aspects. First, it involves the number of tasks being processed. If an edge node is processing multiple editing tasks at the same time, its load is relatively heavy. Understanding this feature determines whether the node has enough resources to undertake new tasks. Secondly, the progress of processing tasks is also an important load operation characteristic. If the progress of a node's tasks is slow, it means that it has encountered some difficulties or resource constraints in processing the current task, which will also affect its ability to accept new tasks. In addition, resource occupancy is also one of the key load operation characteristics, including CPU usage, memory occupancy, storage resource usage ratio, etc. High resource occupancy usually means that the node is heavily loaded, which will affect its speed and efficiency in processing new tasks. By obtaining these load operation characteristics, the current load processing status of edge nodes can be grasped in real time, and task allocation and scheduling are performed based on this information to ensure that the load of each edge node is relatively balanced, and to avoid excessive load of some nodes affecting the performance of the entire drawing editing system. At the same time, when users select edge nodes for editing operations, they can also refer to these load operation characteristics and choose nodes with lighter loads and stronger processing capabilities, thereby improving their editing efficiency and reducing waiting time.

[0044] When faced with an editing operation task, in-depth analysis of cloud drawings to determine its loading and processing load characteristics can accurately reflect the specific requirements of the current task for processing resources. First, the complexity of the task is an important consideration. If the editing operation involves a large number of complex graphics drawing, modification or complex layer operations, the processing power requirements will be relatively high. For example, high-precision 3D model editing requires powerful computing power and graphics processing capabilities to ensure smooth operation and fast response. Secondly, the amount of data involved will also significantly affect the loading and processing load characteristics. If the cloud drawings contain a large number of graphic elements, high-resolution images or complex geometric shapes, more memory and storage resources will be required during the loading and processing process. At the same time, the data transmission and processing speed will also be affected, requiring faster network connections and more efficient data management mechanisms. In addition, the demand for specific processing parameters will also become part of the load characteristics. For example, some tasks require specific algorithm support, such as complex geometric transformation algorithms or intelligent graphic recognition algorithms. The execution of these algorithms requires specific hardware resources or computing power, which affects the processing load of the entire task. By analyzing the loading and processing load characteristics of cloud drawings, we can better understand the requirements of the current task and provide an important basis for the subsequent selection of appropriate multi-operation function edge nodes. This ensures that the editing operations can be carried out efficiently while meeting resource requirements, thereby improving the quality and efficiency of the entire drawing editing process.

[0045] Using the load processing load characteristics and load operation characteristics to evaluate the loading efficiency is a key decision-making process. First, the load processing load characteristics of the current task are compared and analyzed with the load operation characteristics of each multi-operation function edge node. If the load operation characteristics of an edge node show that its resource occupancy rate is already high, and the load processing load characteristics of the current task indicate that a large amount of resources are required, then the loading efficiency of this node when processing the task will be low. On the contrary, if an edge node has a light load and its processing capacity matches the requirements of the task, it may have a high loading efficiency when processing the task. In the evaluation process, multiple factors need to be considered. On the one hand, it is necessary to evaluate whether the edge node has enough remaining resources to undertake the current task, including CPU processing power, memory space, storage capacity, etc. On the other hand, the urgency and time requirements of the task must also be considered. If the task needs to be completed in a shorter time, it is necessary to select edge nodes that can respond quickly and process the task efficiently. By comprehensively considering these factors, a loading evaluation result is obtained. This result can be expressed as a numerical value or divided into levels, such as high, medium, and low efficiency levels. The evaluation results provide an important basis for subsequent decision-making. Whether it is automatic task allocation or manual selection of edge nodes by users, more informed choices can be made based on the results to ensure that the drawing editing task can be completed with the highest efficiency.

[0046] The loading evaluation results are used as node selection feedback information, providing an important decision-making basis for cloud editing users. When cloud editing users are ready to perform drawing editing operations, they are faced with a large number of multi-operation function edge nodes to choose from, and the loading evaluation results help users find the target function that best suits the current task among these nodes. The loading evaluation results present the efficiency performance of each edge node in processing a specific task in a clear and easy-to-understand way. If the loading evaluation result of a node is shown as high efficiency, it means that it performs well in terms of resource utilization, processing speed, etc., and can quickly load and process the current task. Users can give priority to selecting such nodes for editing operations based on this feedback information. At the same time, the loading evaluation results can also help users avoid selecting nodes that will cause task delays or processing difficulties. For example, if the evaluation result of a node is low efficiency, users can know that this node may not be suitable for the current task, so as to select other more suitable nodes. By using the loading evaluation results as node selection feedback information, users can more accurately determine the matching operation function edge nodes, which not only improves the user's editing efficiency, reduces unnecessary waiting time and wrong selections, but also ensures the smooth progress of the entire drawing editing process. Users can make wise decisions based on their task requirements and time requirements, combined with the loading evaluation results, and choose the edge node that best meets their needs to achieve efficient and high-quality drawing editing.

[0047] In a possible implementation, step S100 further includes:

[0048] Step S110: performing a region selection on the drawing according to the editing target and the operation content, wherein the region selection target includes a specific layer and a design element, and determining the editing target region.

[0049] Step S120: parsing the layer structure, object attributes, and operation content of the editing target area to determine core operation parameters.

[0050] Step S130: performing direct editing object identification on the editing target area according to the core operation parameters to obtain the editing direct target layer.

[0051] Step S140: Based on the core operation parameters, an influence analysis is performed on the editing direct target layer and other layers in the connection space to obtain the effect identification collaborative layer, where the effect identification collaborative layer is layer information that has an editing influence relationship with the editing direct target layer under the editing of the core operation parameters.

[0052] Specifically, first of all, in order to determine the target area for editing, it is necessary to select the area of ​​the drawing according to the editing target and the operation content. Users can achieve this goal in a variety of ways. On the one hand, manual selection allows users to directly select the area to be edited through the mouse or touch screen. This method gives users the greatest flexibility and intuitiveness, and accurately selects specific areas based on their own visual judgment. For example, when a user wants to make fine edits to a small range in a drawing, the area can be accurately locked by manual selection. On the other hand, conditional selection automatically filters according to specific layers or design elements in the drawing. For example, if the user wants to edit the electrical layer or select specific design elements such as walls, doors and windows, the corresponding areas are automatically selected according to these conditions, which greatly improves the efficiency of selection, especially when the drawings are very complex and it is very time-consuming to manually find specific elements. In addition, parametric selection automatically selects the areas that meet the conditions based on preset conditions. For example, conditions such as specific size, material properties, or drawing annotations can be used as filtering criteria. If the user wants to find all components of a specific size or parts with specific material properties, parametric selection can quickly lock these areas. The process of selecting the area has a clear goal, which is to accurately lock the area that the user wants to operate and ensure the accuracy of subsequent editing.

[0053] The target area for editing is analyzed in many aspects to determine the core operation parameters. The first is the layer structure analysis. The target area for editing contains multiple layers, each of which has its specific functions and roles. By deeply analyzing the layer structure, the hierarchical relationship between different layers is clarified. For example, some layers are basic layout layers that carry the overall framework structure; while some layers are decorative or annotation layers. Understanding these hierarchical relationships helps to determine which layers are key design layers in the editing process, which directly affect the overall design effect, and which layers are auxiliary, such as annotation layers or reference layers, which are mainly used to provide additional information. At the same time, it is also important to analyze the connection relationship between layers. For example, the elements of some layers are associated with the elements of other layers. This association will affect the scope and degree of influence of the editing operation. Next is the object attribute analysis. In the target area for editing, each design object has specific attributes, which include physical attributes, such as the material and thickness of the wall, the size and power of the equipment, etc.; they may also include position attributes, such as the coordinate position of the object in the drawing; they may also include other specific attributes, such as color, texture, etc. These object attributes are analyzed to provide a specific basis for subsequent editing operations. For example, if you want to edit a wall, understanding its material properties can help determine the appropriate editing method, such as modifying thickness, changing materials, or adjusting position. The last step is to analyze the operation content. According to the user's specific operation needs, such as modification, deletion, addition, or adjustment, identify the corresponding editing tools and operation methods. Different operation contents require different tools and methods to implement. If the user chooses to modify the operation, determine which specific attribute to modify, whether it is size, position, or other attributes, and prepare the corresponding parametric operation options. If it is an add operation, determine the type and location of the added object, and provide a suitable adding tool. Through a comprehensive analysis of the layer structure, object properties, and operation content, the core operation parameters are determined. These parameters include the operation type, which clarifies the user's main operation intention; the target layer, which points out the specific layer to be operated; the object type, which determines the object type to be operated; and the property adjustment, which covers the property changes involved in the operation process. These core operation parameters will provide accurate guidance for subsequent editing operations and ensure that the editing process is carried out efficiently and accurately.

[0054] The core operation parameters provide an important basis for identifying direct edit objects. First, the operation type determines the nature of the edit, such as modification, addition, or deletion. If it is a modification operation, it is necessary to determine which objects are the direct targets that need to be modified. The target layer information further narrows the search scope and clarifies which specific layers to look for direct edit objects. The object type parameter helps to quickly filter out design objects that meet specific types, such as walls, equipment, or pipelines. During the identification process, the various layers and objects in the edit target area are carefully analyzed. For objects that match the core operation parameters, they are marked as direct edit objects, and the layers where these objects are located are the edit direct target layers. For example, if the core operation parameters indicate that a specific type of equipment needs to be modified, search for objects that match the equipment type in the entire edit target area and determine the layers they are located in. After obtaining the edit direct target layer, subsequent edit operations can be concentrated on this specific layer, which not only improves the efficiency of editing, but also ensures the accuracy of editing and avoids unnecessary interference with other irrelevant layers and objects. At the same time, it is clarified that editing the direct target layer also provides specific objects and scopes for other related operations, such as layer locking and collaborative display, making the entire drawing editing process more orderly and efficient.

[0055] This impact analysis step is crucial when editing cloud drawings. After identifying the editing direct target layer, we will start to explore the relationship between this layer and other layers in the connected space. Other layers in the connected space cover various layers that are logically associated or physically connected to the direct target layer. Taking architectural design as an example, when a specific wall is determined as the editing direct target layer, the related electrical pipeline layer, door and window position layer, etc. become other layers in the connected space. Because in actual architectural design, changes in walls often affect the layout of electrical pipelines, such as the movement of walls, which requires the electrical pipelines to be re-routed; at the same time, changes in walls may also affect the positions of doors and windows, such as the relative positions of doors and windows and walls need to be adjusted when the wall is thickened. When performing impact analysis, the potential impact of the modification of the direct target layer on other layers is judged based on the core operation parameters. If the core operation parameters indicate that the size of the wall needs to be modified, a series of chain reactions caused by this modification will be considered. For example, changes in size will cause the size information in the annotation layer to be updated synchronously, otherwise the annotation will not match the actual situation; at the same time, the design position of doors and windows also needs to be adjusted accordingly to ensure that it matches the modified wall. Through such analysis, we can determine the layer information that has an editing impact relationship with the editing direct target layer under the core operation parameter editing, and these layers constitute the effect recognition collaborative layer. With the effect recognition collaborative layer, users can more comprehensively consider the interaction between various layers when performing editing operations, thereby avoiding the situation where the overall design is inconsistent or wrong due to local editing. At the same time, this also provides a key basis for subsequent collaborative display and editing synchronous viewing, allowing users to understand the changes of other related layers in real time during the editing process, so as to make timely adjustments to ensure the integrity and coordination of the entire drawing.

[0056] In a possible implementation, step S140 further includes:

[0057] Step S141: Based on the editing direct target layer, the hierarchical relationship and spatial distribution relationship are identified to obtain a relationship layer.

[0058] Step S142: performing correlation analysis on each relationship layer according to the core operation parameters, determining the correlation between each layer, and establishing a layer correlation matrix.

[0059] Step S143: Based on the impact of the core operation parameters on the editing direct target layer, the layer association matrix is ​​analyzed for hierarchical and spatial connection impact relationships to obtain the operation impact.

[0060] Specifically, hierarchical relationships and spatial distribution relationships are identified based on the editing direct target layer to obtain the relationship layer. First of all, hierarchical relationship identification means determining the position of the editing direct target layer in the layer structure of the entire drawing. For example, it may be under an important design layer, or it may have a hierarchical relationship with some auxiliary layers. At the same time, spatial distribution relationship identification focuses on the positional relationship between the editing direct target layer and other layers in the spatial dimension. For example, if it is an architectural drawing, a certain building structure represented by the editing direct target layer may be adjacent, overlapping, or mutually influential in space with electrical facilities, pipeline layouts, etc. in other layers. Through such an identification process, find the relationship layer that is closely related to the editing direct target layer in terms of hierarchy and space.

[0061] According to the core operation parameters, the correlation analysis of each relationship layer is carried out to determine the correlation between each layer and establish a layer correlation matrix. The core operation parameters provide specific direction and basis for the correlation analysis. For example, if the core operation parameters indicate that the size of the edited direct target layer is modified, then for each relationship layer, the degree of impact that this size modification may have on it will be analyzed. The correlation degree is expressed as a numerical value. The higher the value, the closer the correlation between the two layers. By performing such an analysis on all relationship layers, a layer correlation matrix is ​​established. This matrix clearly shows the degree of correlation between each layer, providing important data support for subsequent analysis and decision-making.

[0062] Based on the impact of the core operation parameters on the editing of the direct target layer, the layer association matrix is ​​analyzed for hierarchical and spatial connection impact relationships to obtain the operation impact. In this step, the specific impact of the core operation parameters on the editing of the direct target layer is first considered, such as size change, position movement, etc. Then, combined with the layer association matrix, the propagation effect of this impact on other relationship layers in terms of hierarchy and spatial connection is analyzed. For example, if the size of the edited direct target layer increases, by analyzing the layer association matrix, it is determined which relationship layers will be directly affected, such as adjacent layers that may need to be adjusted accordingly to adapt to this change; at the same time, it is also possible to determine which layers will be indirectly affected, such as layers in other areas affected by spatial connection relationships. Through such analysis, a comprehensive understanding of the impact of the editing operation on each layer in the entire drawing is achieved, providing users with accurate references when performing editing operations, and ensuring the overall coordination and integrity of the drawing.

[0063] In a possible implementation, step S143 further includes:

[0064] Step S1431: Calculate the spatial relationship before and after editing based on the editing result of the editing direct target layer according to the core operation parameters.

[0065] Step S1432: Obtain the degree of association between layers according to the layer association matrix.

[0066] Step S1433: performing an influence analysis on each relationship layer in the layer association matrix according to the spatial relationship before and after the editing and the degree of association, combined with the initial spatial distance between layers, to obtain the operation influence.

[0067] Specifically, it is a key step to calculate the spatial relationship before and after editing the editing result of the direct target layer according to the core operation parameters. First, the type of editing operation indicated by the core operation parameters is clarified, such as translation, scaling, rotation or shape change, which will directly affect the position and form of the direct target layer in space. If it is a translation operation, it is necessary to determine the displacement in the horizontal and vertical directions. By comparing the coordinate changes of specific reference points (such as the center point or a corner point) on the target layer before and after editing, the spatial displacement values ​​in the two directions are calculated to obtain the spatial relationship before and after editing. For the scaling operation, it is necessary to pay attention to the size change ratio of the target layer in each dimension. By comparing the bounding box size of the target layer before and after editing or the size of specific key elements, the scaling factor is calculated to describe the size change of the spatial relationship before and after editing. In the case of rotation operation, it is necessary to determine the rotation angle and rotation center. By calculating the angle change of the elements on the target layer relative to the rotation center and observing the spatial position change of these elements before and after rotation, the changes in the angle and position of the spatial relationship before and after editing can be accurately determined. As for shape-changing operations, complex geometric transformations are involved, and it is necessary to analyze the shape changes of each element in the target layer, such as changes in the curvature of lines, increases, decreases, or deformations of graphics, etc. Through quantitative analysis of these shape changes, the perimeter and area changes of graphics, or changes in the curvature of lines, etc., are calculated to determine the complex changes in the shape of the spatial relationship before and after editing. By accurately calculating the changes in spatial relationships caused by these different types of editing operations, the foundation is laid for further analysis of the impact of editing operations on other layers.

[0068] Obtaining the degree of correlation between layers based on the layer correlation matrix is ​​a direct and critical operation. The layer correlation matrix (R) presents the degree of correlation between different layers in a clear and concise way. Each element R in the matrix ij Corresponding to two specific layers T i and T j For example, when you need to obtain the wall layer (T wall ) and the doors and windows layer (T window-door), directly check the corresponding element in the matrix, its value is 0.8, which indicates that there is a high correlation between the wall layer and the door and window layer. Similarly, if you want to understand the correlation between the wall layer and the electrical pipeline layer (T electric ), the value in the matrix is ​​0.5, indicating that the correlation between them is relatively low. For the correlation between any other two layers, the corresponding element values ​​can be quickly and accurately obtained in the layer association matrix in this way, so as to clarify the degree of correlation between them. This matrix-based acquisition method provides an important data basis for subsequent impact analysis and can more accurately evaluate the impact of editing operations on different layers.

[0069] When conducting an impact analysis to obtain the impact of an operation, it is necessary to comprehensively consider three key factors: the spatial relationship before and after editing, the correlation, and the initial spatial distance between layers. First, the spatial relationship before and after editing reflects the changes in the position, size, or shape of the direct target layer after the editing operation. For example, if the editing operation causes the target layer to translate, then its relative position in space with other layers will change; if it is a scaling operation, the size change of the target layer will affect its spatial interaction relationship with other layers. These changes in spatial relationships are one of the important factors affecting other layers. Secondly, the correlation reflects the degree of mutual dependence between layers based on hierarchical relationships, design logic, or functions. The correlation value obtained from the layer correlation matrix directly indicates the closeness between the target layer and each relationship layer. The higher the correlation, the greater the potential impact on the corresponding relationship layer when editing the target layer. Finally, the initial spatial distance between layers also plays an important role in the impact analysis. Even if there is a certain degree of correlation between two layers, if their initial spatial distance is far, the impact of the editing operation on them may be relatively small. For example, in an architectural drawing, two layers located on different floors and with relatively independent functions may have a certain degree of correlation, but due to the large initial spatial distance, editing a layer on one floor may have limited impact on the corresponding layer on the other floor. Taking these three factors into consideration, a specific calculation formula is used to calculate the , and conduct an impact analysis on each relationship layer in the layer association matrix. Through such analysis, the impact of the editing operation on each relationship layer, that is, the operation impact, can be accurately obtained to ensure the accuracy and coordination of the drawing editing process.

[0070] In a possible implementation, step S1433 further includes:

[0071] ,in, is the influence of target layer i on relationship layer j, The spatial relationship before and after editing, that is, the spatial displacement of the target layer. The size of the target layer changes. is the correlation between target layer i and relationship layer j, is the initial spatial distance between the target layer i and the relationship layer j, It is the adjustment coefficient, which is used to adjust the intensity of the impact.

[0072] Specifically, Represents the influence of target layer i on relationship layer j. It is the spatial relationship between the target layer before and after editing, specifically the spatial displacement of the target layer, which reflects the change in the position of the target layer during the editing operation. The size change of the target layer reflects the change of the physical size of the target layer when editing. It is the degree of association between the target layer i and the relationship layer j. This degree of association is based on the hierarchical relationship, design logic or functional interdependence of the layers. Its value ranges from 0 (no association) to 1 (strong association). It reveals the closeness of the intrinsic relationship between the two layers. It is the initial spatial distance between the target layer i and the relationship layer j. This parameter takes into account the original spatial position relationship between the layers, because the distance will have an effect on the influence. It is an adjustment coefficient used to adjust the intensity of the impact. It can be set according to specific drawing types, editing requirements and other factors.

[0073] By introducing the product of spatial displacement and size change, the formula can simultaneously capture the joint impact of changes in position and physical size on the associated layers, which means that changes in the position and size of the target layer caused by the editing operation will affect the associated layers in combination. At the same time, by introducing the initial distance, the formula can adjust the different effects of spatial displacement on distant and near layers. That is, layers that are closer are more affected by the spatial displacement of the target layer, while layers that are farther away are less affected. In addition, the degree of association is combined with spatial changes, so that the impact of the modification interacts dynamically with the intrinsic relationship and physical position between the layers, ensuring a more accurate impact assessment. This dynamic interaction reflects that while considering the changes in the physical position of the layers, the intrinsic association relationship between the layers is also fully considered, making the calculation of the impact more accurate and comprehensive, and better reflecting the degree of impact of the editing operation on each layer in the entire drawing.

[0074] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0075] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0076] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A cloud-based intelligent drawing editing method, characterized in that: The cloud-based drawing intelligent editing method comprises: Setting the editing target area of ​​the drawing through the cloud, performing target analysis according to the editing target area, and obtaining the editing direct target layer and the effect recognition collaborative layer; Generate a layer locking instruction and a collaborative display instruction respectively according to the editing direct target layer and the effect identification collaborative layer, wherein the layer locking instruction is used to lock the editing direct target layer, and the collaborative display instruction is used to perform an editing and synchronous viewing operation on the effect identification collaborative layer; Edit the drawing based on the editing direct target layer, perform editing effect recognition and evaluation through the effect recognition collaborative layer, and output editing annotations, which can be collaboratively displayed through the collaborative display instructions; Based on the editing direct target layer, the drawing is edited, including: Get the edit operation type; Based on the editing operation type, a multi-operation function edge node is obtained, where the multi-operation function edge node is an editor constructed according to functional features of different CAD editing tools; Select a target function in the multi-operation function edge node, connect to a matching operation function edge node, load an editing operation task into the matching operation function edge node to perform a drawing editing operation, and transmit the editing completion result back to the cloud; Target parsing is performed according to the editing target area to obtain an editing direct target layer and an effect recognition collaborative layer, including: Select an area of ​​the drawing according to the editing target and the operation content, where the target of the area selection includes a specific layer and a design element, and the editing target area is determined; Analyze the layer structure, object attributes, and operation content of the editing target area to determine core operation parameters; Performing direct editing object recognition on the editing target area according to the core operation parameters to obtain the editing direct target layer; According to the core operation parameters, an influence analysis is performed on the editing direct target layer and other layers in the connection space to obtain the effect identification collaborative layer. The effect identification collaborative layer is the layer information that has an editing influence relationship with the editing direct target layer under the editing of the core operation parameters.

2. The cloud-based drawing intelligent editing method according to claim 1, characterized in that: Based on the editing operation type, a multi-operation function edge node is obtained, which includes: Collect multiple types of CAD tools, perform drawing function recognition and evaluation on the multiple types of CAD tools, and select tools whose target drawing function evaluation results meet preset requirements; For each screening target tool, construct edge editing nodes respectively; Functional feature analysis is performed on the screening target tool to generate a functional analysis tag, a mapping association is established between the target tool name and the functional analysis tag, and the mapping association tag is added to the edge editing node to describe the operation function.

3. The cloud-based drawing intelligent editing method according to claim 1, characterized in that: Selecting a target function in the multi-operation function edge node and connecting the matching operation function edge node includes: Acquire the load operation characteristics of the multi-operation function edge node, where the load operation characteristics are used to characterize the current load processing status of the edge node; Analyze the loading processing load characteristics of the cloud drawing based on the editing operation task, wherein the loading processing load characteristics are used to characterize the processing parameter characteristics required by the current task; Using the load processing load characteristics and the load operation characteristics to evaluate loading efficiency, and obtain a loading evaluation result; The loading evaluation result is used as node selection feedback information for cloud editing users to select target functions and determine the matching operation function edge node.

4. The cloud-based drawing intelligent editing method according to claim 1, characterized in that: According to the core operation parameters, the impact analysis of the editing direct target layer and other layers in the connection space is performed, including: Based on the editing direct target layer, hierarchical relationship and spatial distribution relationship are identified to obtain a relationship layer; Performing correlation analysis on each relationship layer according to the core operation parameters, determining the correlation between each layer, and establishing a layer correlation matrix; Based on the impact of the core operation parameters on the editing direct target layer, the layer association matrix is ​​analyzed for hierarchical and spatial connection impact relationships to obtain the operation impact.

5. The cloud-based drawing intelligent editing method according to claim 4, characterized in that: Based on the impact of the core operation parameters on the editing direct target layer, the layer association matrix is ​​analyzed for hierarchical and spatial connection impact relationships to obtain the operation impact, including: Calculate the spatial relationship before and after editing based on the editing result of the editing direct target layer by the core operation parameters; Acquire the correlation between layers according to the layer correlation matrix; According to the spatial relationship before and after the editing and the degree of association, the influence of each relationship layer in the layer association matrix is ​​analyzed in combination with the initial spatial distance between the layers to obtain the operation influence.

6. The cloud-based drawing intelligent editing method according to claim 5, characterized in that: The calculation formula of impact analysis is: ,in, is the influence of target layer i on relationship layer j, The spatial relationship before and after editing, that is, the spatial displacement of the target layer. The size of the target layer changes. is the correlation between target layer i and relationship layer j, is the initial spatial distance between the target layer i and the relationship layer j, It is the adjustment coefficient, which is used to adjust the intensity of the impact.

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