Model display processing method and system for slice data management

Abstract

The present invention discloses a model display processing method and system for slice data management, the method comprising: determining multiple similar users corresponding to the current user who uploaded the current model slice data; determining multiple historical display operations corresponding to each of the similar users in a historical operation database; screening multiple similar display operations from the multiple historical display operations according to the current model slice data; based on a prediction algorithm, determining a display operation sequence corresponding to the multiple similar display operations according to the operation track of the current user; the display operation sequence is used to display to the current user. It can be seen that the present invention can recommend the most suitable model display operation to the user based on the historical operation records of similar users, so as to improve the efficiency and effect of the user's model display operation, thereby giving the user a better model display experience.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a model display processing method and system for slice data management. Background Art

[0002] With the development of 3D printing technology, more and more users or organizations have begun to pay attention to technical issues such as the management and transmission of slice data of printed models. Some organizations have begun to develop platforms that can manage and display slice data. Among them, how to effectively improve the convenience and user experience of users' slice data display operations on the platform is a technical issue that has attracted much attention. However, most of the existing slice data processing technologies still only display models based on the user's direct display operation instructions, and do not use the historical display operation records of similar users to predict display operations for users. Therefore, the user's model display operation efficiency is low and the user experience is poor. It can be seen that the existing technology has defects that need to be solved urgently. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a model display processing method and system for slice data management, which can recommend the most appropriate model display operation to the user based on the historical operation records of similar users, so as to improve the efficiency and effect of the user's model display operation, and thus provide the user with a better model display experience.

[0004] In order to solve the above technical problems, the first aspect of the present invention discloses a model display processing method for slice data management, the method comprising:

[0005] Determine multiple similar users corresponding to the current user who uploaded the current model slice data;

[0006] Determining, in a historical operation database, a plurality of historical display operations corresponding to each of the similar users;

[0007] Filtering a plurality of similar display operations from the plurality of historical display operations according to the current model slice data;

[0008] Based on a prediction algorithm, a display operation sequence corresponding to the multiple similar display operations is determined according to the operation track of the current user; the display operation sequence is used to display to the current user.

[0009] As an optional implementation, in the first aspect of the present invention, the determining of multiple similar users corresponding to the current user who uploaded the current model slice data includes:

[0010] Get the current model slice data uploaded by the current user;

[0011] Determining user parameters of the current user;

[0012] Determining data parameters of the current model slice data;

[0013] For each candidate user, determining a user similarity between the candidate user and the current user according to the user parameter and the data parameter;

[0014] Users whose user similarity is greater than a first similarity threshold are screened out from all the candidate users to obtain a plurality of similar users.

[0015] As an optional implementation manner, in the first aspect of the present invention, determining the user similarity between the candidate user and the current user according to the user parameter and the data parameter includes:

[0016] Calculating a first similarity between the user parameter of the candidate user and the user parameter of the current user;

[0017] Obtain multiple historical upload models corresponding to the candidate user;

[0018] Calculating an average value of the similarity between the model parameters of each of the historical upload models and the data parameters to obtain a second similarity;

[0019] A weighted average of the first similarity and the second similarity is calculated to obtain a user similarity between the candidate user and the current user.

[0020] As an optional embodiment, in the first aspect of the present invention, the user parameters include at least one of user physiological parameters, user permissions, user historical operation records and user historical access records; the model parameters or the data parameters include at least one of data volume size, model representation object type, model physical parameters and model space complexity distribution curve.

[0021] As an optional implementation, in the first aspect of the present invention, the filtering out a plurality of similar display operations from the plurality of historical display operations according to the current model slice data includes:

[0022] For each of the historical display operations, obtaining operation detail parameters and operation model data corresponding to the historical display operation;

[0023] Calculating a third similarity between the operation model data and the current model slice data;

[0024] Determining a fit parameter between the operation detail parameter and the current model slice data;

[0025] Calculating the product of the third similarity and the adaptation parameter to obtain the priority corresponding to the history display operation;

[0026] The operations whose priorities are greater than the priority threshold are screened out from all the historical display operations to obtain a plurality of similar display operations.

[0027] As an optional implementation, in the first aspect of the present invention, the determining of the adaptation parameter between the operation detail parameter and the current model slice data includes:

[0028] Based on the correspondence between the preset parameters and the instructions, the corresponding operation instructions are determined according to the operation detail parameters;

[0029] Executing the operation instruction on the current model slice data to obtain a display image after execution;

[0030] Inputting the executed display image into a trained display effect prediction neural network to obtain an output display effect; the display effect prediction neural network is trained by a training data set including a plurality of training model display images and corresponding display effect annotations;

[0031] Inputting the executed display image into a trained model integrity prediction neural network to obtain an output model integrity; the model integrity prediction neural network is trained by a training data set including a plurality of training model display images and corresponding model integrity annotations;

[0032] The product of the display effect and the model integrity is calculated to obtain the adaptation parameter between the operation detail parameter and the current model slice data.

[0033] As an optional implementation, in the first aspect of the present invention, the determining, based on a prediction algorithm and according to the operation track of the current user, a display operation sequence corresponding to the multiple similar display operations includes:

[0034] Obtaining the movement track of the operation cursor of the current user on the current interface;

[0035] Determine the trajectory portion of the moving trajectory within the display area corresponding to the current model slice data to obtain the trajectory within the area;

[0036] For each of the similar display operations, determining the operation detail parameters corresponding to the similar display operation;

[0037] Based on the correspondence between the preset parameters and the operation direction, determine the three-dimensional rotation direction parameters corresponding to the operation detail parameters;

[0038] Calculating a fourth similarity between the three-dimensional rotation direction parameter and the trajectory in the area to obtain an operation priority corresponding to the similar display operation;

[0039] All the similar display operations are sorted from large to small according to the operation priority to obtain a display operation sequence.

[0040] As an optional implementation, in the first aspect of the present invention, the calculating the fourth similarity between the three-dimensional rotation direction parameter and the trajectory within the area to obtain the operation priority corresponding to the similar display operation includes:

[0041] Calculate the extension direction of the two-dimensional projection of the three-dimensional rotation direction parameter on the display interface;

[0042] The weighted average value of the directional angle difference between the extension direction of each trajectory segment of the trajectory in the area and the extension direction of the two-dimensional projection is calculated to obtain the operation priority corresponding to the similar display operation; wherein the weighted calculation weight corresponding to the directional angle difference corresponding to each trajectory segment is proportional to the trajectory length of the trajectory segment.

[0043] A second aspect of an embodiment of the present invention discloses a model display processing system for slice data management, the system comprising:

[0044] A first determination module is used to determine multiple similar users corresponding to the current user who uploaded the current model slice data;

[0045] A second determination module is used to determine, in a historical operation database, a plurality of historical display operations corresponding to each of the similar users;

[0046] A screening module, configured to screen out a plurality of similar display operations from the plurality of historical display operations according to the current model slice data;

[0047] A prediction module is used to determine, based on a prediction algorithm and according to the operation track of the current user, a display operation sequence corresponding to the multiple similar display operations; the display operation sequence is used to display to the current user.

[0048] As an optional implementation, in the second aspect of the present invention, the specific manner in which the first determination module determines multiple similar users corresponding to the current user who uploaded the current model slice data includes:

[0049] Get the current model slice data uploaded by the current user;

[0050] Determining user parameters of the current user;

[0051] Determining data parameters of the current model slice data;

[0052] For each candidate user, determining a user similarity between the candidate user and the current user according to the user parameter and the data parameter;

[0053] Users whose user similarity is greater than a first similarity threshold are screened out from all the candidate users to obtain a plurality of similar users.

[0054] As an optional implementation, in the second aspect of the present invention, the specific manner in which the first determination module determines the user similarity between the candidate user and the current user according to the user parameter and the data parameter includes:

[0055] Calculating a first similarity between the user parameter of the candidate user and the user parameter of the current user;

[0056] Obtain multiple historical upload models corresponding to the candidate user;

[0057] Calculating an average value of the similarity between the model parameters of each of the historical upload models and the data parameters to obtain a second similarity;

[0058] A weighted average of the first similarity and the second similarity is calculated to obtain a user similarity between the candidate user and the current user.

[0059] As an optional embodiment, in the second aspect of the present invention, the user parameters include at least one of user physiological parameters, user permissions, user historical operation records and user historical access records; the model parameters or the data parameters include at least one of data volume size, model representation object type, model physical parameters and model space complexity distribution curve.

[0060] As an optional implementation, in the second aspect of the present invention, the specific manner in which the screening module screens out multiple similar display operations from the multiple historical display operations according to the current model slice data includes:

[0061] For each of the historical display operations, obtaining operation detail parameters and operation model data corresponding to the historical display operation;

[0062] Calculating a third similarity between the operation model data and the current model slice data;

[0063] Determining a fit parameter between the operation detail parameter and the current model slice data;

[0064] Calculating the product of the third similarity and the adaptation parameter to obtain the priority corresponding to the history display operation;

[0065] The operations whose priorities are greater than the priority threshold are screened out from all the historical display operations to obtain a plurality of similar display operations.

[0066] As an optional implementation, in the second aspect of the present invention, the specific manner in which the screening module determines the fitness parameter between the operation detail parameter and the current model slice data includes:

[0067] Based on the correspondence between the preset parameters and the instructions, the corresponding operation instructions are determined according to the operation detail parameters;

[0068] Executing the operation instruction on the current model slice data to obtain a display image after execution;

[0069] Inputting the executed display image into a trained display effect prediction neural network to obtain an output display effect; the display effect prediction neural network is trained by a training data set including a plurality of training model display images and corresponding display effect annotations;

[0070] Inputting the executed display image into a trained model integrity prediction neural network to obtain an output model integrity; the model integrity prediction neural network is trained by a training data set including a plurality of training model display images and corresponding model integrity annotations;

[0071] The product of the display effect and the model integrity is calculated to obtain the adaptation parameter between the operation detail parameter and the current model slice data.

[0072] As an optional implementation, in the second aspect of the present invention, the prediction module determines the specific manner of the display operation sequence corresponding to the multiple similar display operations based on the prediction algorithm and according to the operation trajectory of the current user, including:

[0073] Obtaining the movement track of the operation cursor of the current user on the current interface;

[0074] Determine the trajectory portion of the moving trajectory within the display area corresponding to the current model slice data to obtain the trajectory within the area;

[0075] For each of the similar display operations, determining the operation detail parameters corresponding to the similar display operation;

[0076] Based on the correspondence between the preset parameters and the operation direction, determine the three-dimensional rotation direction parameters corresponding to the operation detail parameters;

[0077] Calculating a fourth similarity between the three-dimensional rotation direction parameter and the trajectory in the area to obtain an operation priority corresponding to the similar display operation;

[0078] All the similar display operations are sorted from large to small according to the operation priority to obtain a display operation sequence.

[0079] As an optional implementation, in the second aspect of the present invention, the prediction module calculates the fourth similarity between the three-dimensional rotation direction parameter and the trajectory in the area, and obtains the specific method of the operation priority corresponding to the similar display operation, including:

[0080] Calculate the extension direction of the two-dimensional projection of the three-dimensional rotation direction parameter on the display interface;

[0081] The weighted average value of the directional angle difference between the extension direction of each trajectory segment of the trajectory in the area and the extension direction of the two-dimensional projection is calculated to obtain the operation priority corresponding to the similar display operation; wherein the weighted calculation weight corresponding to the directional angle difference corresponding to each trajectory segment is proportional to the trajectory length of the trajectory segment.

[0082] A third aspect of the present invention discloses another model display processing system for slice data management, the system comprising:

[0083] A memory storing executable program code;

[0084] a processor coupled to the memory;

[0085] The processor calls the executable program code stored in the memory to execute part or all of the steps in the model display processing method for slice data management disclosed in the first aspect of the present invention.

[0086] The fourth aspect of the present invention discloses a computer storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute part or all of the steps in the model display processing method for slice data management disclosed in the first aspect of the present invention.

[0087] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0088] The present invention can determine multiple historical display operations of multiple similar users corresponding to the current user based on a historical operation database, and then screen out multiple similar display operations based on the current model slicing data, so as to comprehensively and accurately predict the display operation sequences corresponding to the multiple similar display operations, thereby being able to recommend the most appropriate model display operation to the user based on the historical operation records of similar users, so as to improve the efficiency and effectiveness of the user's model display operation, and thus provide the user with a better model display experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. 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.

[0090] Figure 1 It is a flow chart of a model display processing method for slice data management disclosed in an embodiment of the present invention.

[0091] Figure 2 It is a structural schematic diagram of a model display processing system for slice data management disclosed in an embodiment of the present invention.

[0092] Figure 3 It is a structural schematic diagram of another model display processing system for slice data management disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0093] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0094] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, device, product or equipment that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or equipment.

[0095] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0096] The present invention discloses a model display processing method and system for slice data management, which can determine multiple historical display operations of multiple similar users corresponding to the current user based on a historical operation database, and then screen out multiple similar display operations based on the current model slice data, so as to comprehensively and accurately predict the display operation sequence corresponding to the multiple similar display operations, so as to recommend the most suitable model display operation to the user based on the historical operation records of similar users, so as to improve the efficiency and effect of the user's model display operation, and thus provide the user with a better model display experience. The following are detailed descriptions.

[0097] Embodiment 1

[0098] See also Figure 1 , Figure 1 : is a flow chart of a model display processing method for slice data management disclosed in an embodiment of the present invention. Figure 1 The model display processing method for slice data management described above can be applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 1 As shown, the model display processing method for slice data management may include the following operations:

[0099] 101. Determine multiple similar users corresponding to the current user who uploaded the current model slice data.

[0100] 102. Determine, in a historical operation database, a plurality of historical display operations corresponding to each similar user.

[0101] 103. According to the current model slice data, multiple similar display operations are filtered out from multiple historical display operations.

[0102] 104. Based on the prediction algorithm and according to the operation track of the current user, determine a display operation sequence corresponding to multiple similar display operations.

[0103] Optionally, the display operation sequence is used to display to the current user.

[0104] It can be seen that the above-mentioned embodiment of the invention can determine multiple historical display operations of multiple similar users corresponding to the current user based on the historical operation database, and then filter out multiple similar display operations based on the current model slicing data, so as to comprehensively and accurately predict the display operation sequence corresponding to multiple similar display operations, so as to recommend the most suitable model display operation to the user based on the historical operation records of similar users, so as to improve the efficiency and effectiveness of the user's model display operation, and then provide the user with a better model display experience.

[0105] As an optional embodiment, in the above step, determining multiple similar users corresponding to the current user who uploaded the current model slice data includes:

[0106] Get the current model slice data uploaded by the current user;

[0107] Determine user parameters of the current user;

[0108] Determine the data parameters of the current model slice data;

[0109] For each candidate user, determine the user similarity between the candidate user and the current user based on the user parameters and data parameters;

[0110] Users whose user similarity is greater than a first similarity threshold are screened out from all candidate users to obtain a plurality of similar users.

[0111] It can be seen that through the above optional embodiments, the user similarity between the candidate user and the current user can be determined according to the user parameters and data parameters, so as to screen out reasonable similar users, facilitate subsequent display operation predictions, and assist in recommending the most appropriate model display operation to the user based on the historical operation records of similar users, so as to improve the efficiency and effectiveness of the user's model display operation, and thus provide the user with a better model display experience.

[0112] As an optional embodiment, in the above step, determining the user similarity between the candidate user and the current user according to the user parameter and the data parameter includes:

[0113] Calculating a first similarity between the user parameters of the candidate user and the user parameters of the current user;

[0114] Obtain multiple historical upload models corresponding to the candidate user;

[0115] Calculate the average of the similarities between the model parameters and the data parameters of each historical uploaded model to obtain a second similarity;

[0116] A weighted average of the first similarity and the second similarity is calculated to obtain the user similarity between the candidate user and the current user.

[0117] It can be seen that through the above-mentioned optional embodiments, the similarity between users can be accurately calculated based on the similarity between user parameters and the similarity between model data, so as to facilitate the subsequent screening of reasonable similar users, facilitate subsequent display operation predictions, and assist in recommending the most appropriate model display operation to the user based on the historical operation records of similar users, so as to improve the efficiency and effectiveness of the user's model display operation, and thus provide the user with a better model display experience.

[0118] As an optional embodiment, in the above steps, the user parameters include at least one of user physiological parameters, user permissions, user historical operation records and user historical access records; the model parameters or data parameters include at least one of data volume, model representation object type, model physical parameters and model space complexity distribution curve.

[0119] It can be seen that through the above optional embodiments, the content of user parameters and data parameters is limited, so as to effectively characterize the characteristics of users and model data, facilitate subsequent display operation predictions, and assist in recommending the most appropriate model display operations to users based on historical operation records of similar users, so as to improve the efficiency and effectiveness of users' model display operations, and thus provide users with a better model display experience.

[0120] As an optional embodiment, in the above step, based on the current model slice data, multiple similar display operations are screened out from multiple historical display operations, including:

[0121] For each historical display operation, obtain the operation detail parameters and operation model data corresponding to the historical display operation;

[0122] Calculating a third similarity between the operation model data and the current model slice data;

[0123] Determine the fit parameters between the operation detail parameters and the current model slice data;

[0124] Calculate the product of the third similarity and the adaptation parameter to obtain the priority corresponding to the history display operation;

[0125] Operations with priorities greater than a priority threshold are filtered out from all historical display operations to obtain multiple similar display operations.

[0126] It can be seen that through the above-mentioned optional embodiments, the priority of each operation can be accurately determined based on the similarity and adaptability between the operation and model data corresponding to each historical display operation and the current data, so as to screen out multiple suitable similar operations, so as to facilitate subsequent display operation predictions, and assist in recommending the most suitable model display operation to the user based on the historical operation records of similar users, so as to improve the efficiency and effectiveness of the user's model display operation, and thus provide the user with a better model display experience.

[0127] As an optional embodiment, in the above step, determining the adaptation parameter between the operation detail parameter and the current model slice data includes:

[0128] Based on the correspondence between the preset parameters and the instructions, the corresponding operation instructions are determined according to the operation detail parameters;

[0129] Execute the operation instructions on the current model slice data, and display the image after execution;

[0130] Inputting the displayed image after execution into a trained display effect prediction neural network to obtain an output display effect; optionally, the display effect prediction neural network is trained by a training data set including a plurality of training model display images and corresponding display effect annotations;

[0131] Inputting the displayed image after execution into a trained model integrity prediction neural network to obtain an output model integrity; optionally, the model integrity prediction neural network is trained using a training data set including a plurality of training model display images and corresponding model integrity annotations;

[0132] The product of the display effect and the model integrity is calculated to obtain the adaptation parameters between the operation detail parameters and the current model slice data.

[0133] It can be seen that through the above-mentioned optional embodiments, the current model can be operated based on the operation instructions corresponding to the operation detail parameters, and the display results after the operation can be predicted in effect and completeness based on two neural networks, so as to accurately calculate the degree of fit between the operation and the model, screen out multiple suitable similar operations, facilitate subsequent display operation predictions, and assist in recommending the most suitable model display operation to the user based on the historical operation records of similar users, so as to improve the efficiency and effect of the user's model display operation, and thus provide the user with a better model display experience.

[0134] As an optional embodiment, in the above steps, based on the prediction algorithm, according to the operation track of the current user, determining the display operation sequence corresponding to the multiple similar display operations includes:

[0135] Get the movement track of the current user's operating cursor on the current interface;

[0136] Determine the trajectory portion of the moving trajectory within the display area corresponding to the current model slice data, and obtain the trajectory within the area;

[0137] For each similar display operation, determining operation detail parameters corresponding to the similar display operation;

[0138] Based on the correspondence between the preset parameters and the operation direction, determine the three-dimensional rotation direction parameters corresponding to the operation detail parameters;

[0139] Calculate the fourth similarity between the three-dimensional rotation direction parameter and the trajectory in the area, and obtain the operation priority corresponding to the similar display operation;

[0140] All similar display operations are sorted from large to small according to the operation priority to obtain a display operation sequence.

[0141] It can be seen that through the above-mentioned optional embodiments, the priority of the operation can be determined based on the similarity between the user's cursor movement trajectory and the operation direction of each operation, so as to determine the most appropriate operation display order, and recommend the most appropriate model display operation to the user based on the historical operation records of similar users, so as to improve the efficiency and effectiveness of the user's model display operation, and thus provide the user with a better model display experience.

[0142] As an optional embodiment, in the above step, calculating the fourth similarity between the three-dimensional rotation direction parameter and the trajectory in the area to obtain the operation priority corresponding to the similar display operation includes:

[0143] Calculate the two-dimensional projection extension direction of the three-dimensional rotation direction parameter on the display interface;

[0144] The weighted sum average of the directional angle differences between the extension direction of each trajectory segment of the trajectory in the area and the extension direction of the two-dimensional projection is calculated to obtain the operation priority corresponding to the similar display operation; wherein the weighted calculation weight corresponding to the directional angle difference corresponding to each trajectory segment is proportional to the trajectory length of the trajectory segment.

[0145] It can be seen that through the above-mentioned optional embodiments, the priority of each operation can be determined based on the weighted calculation of the angle difference between each part of the cursor trajectory in the area and the two-dimensional projection direction of the three-dimensional direction of the operation, so as to determine the most appropriate operation display order, and recommend the most appropriate model display operation to the user based on the historical operation records of similar users, so as to improve the efficiency and effectiveness of the user's model display operation, and thus provide the user with a better model display experience.

[0146] Embodiment 2

[0147] See also Figure 2 , Figure 2 : is a schematic diagram of a model display processing system for slice data management disclosed in an embodiment of the present invention. Figure 2 The model display processing system for slice data management described above can be applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 2 As shown, the model display processing system for slice data management may include:

[0148] The first determining module 201 is used to determine a plurality of similar users corresponding to the current user who uploaded the current model slice data.

[0149] The second determining module 202 is used to determine a plurality of historical display operations corresponding to each similar user in the historical operation database.

[0150] The screening module 203 is used to screen out a plurality of similar display operations from a plurality of historical display operations according to the current model slice data.

[0151] The prediction module 204 is used to determine, based on a prediction algorithm and according to the operation track of the current user, a display operation sequence corresponding to a plurality of similar display operations.

[0152] Optionally, the display operation sequence is used to display to the current user.

[0153] It can be seen that the above-mentioned embodiment of the invention can determine multiple historical display operations of multiple similar users corresponding to the current user based on the historical operation database, and then filter out multiple similar display operations based on the current model slicing data, so as to comprehensively and accurately predict the display operation sequence corresponding to multiple similar display operations, so as to recommend the most suitable model display operation to the user based on the historical operation records of similar users, so as to improve the efficiency and effectiveness of the user's model display operation, and then provide the user with a better model display experience.

[0154] As an optional embodiment, the specific manner in which the first determining module determines multiple similar users corresponding to the current user who uploaded the current model slice data includes:

[0155] Get the current model slice data uploaded by the current user;

[0156] Determine user parameters of the current user;

[0157] Determine the data parameters of the current model slice data;

[0158] For each candidate user, determine the user similarity between the candidate user and the current user based on the user parameters and data parameters;

[0159] Users whose user similarity is greater than a first similarity threshold are screened out from all candidate users to obtain a plurality of similar users.

[0160] It can be seen that through the above optional embodiments, the user similarity between the candidate user and the current user can be determined according to the user parameters and data parameters, so as to screen out reasonable similar users, facilitate subsequent display operation predictions, and assist in recommending the most appropriate model display operation to the user based on the historical operation records of similar users, so as to improve the efficiency and effectiveness of the user's model display operation, and thus provide the user with a better model display experience.

[0161] As an optional embodiment, the specific manner in which the first determining module determines the user similarity between the candidate user and the current user according to the user parameter and the data parameter includes:

[0162] Calculating a first similarity between the user parameters of the candidate user and the user parameters of the current user;

[0163] Obtain multiple historical upload models corresponding to the candidate user;

[0164] Calculate the average of the similarities between the model parameters and the data parameters of each historical uploaded model to obtain a second similarity;

[0165] A weighted average of the first similarity and the second similarity is calculated to obtain the user similarity between the candidate user and the current user.

[0166] It can be seen that through the above-mentioned optional embodiments, the similarity between users can be accurately calculated based on the similarity between user parameters and the similarity between model data, so as to facilitate the subsequent screening of reasonable similar users, facilitate subsequent display operation predictions, and assist in recommending the most appropriate model display operation to the user based on the historical operation records of similar users, so as to improve the efficiency and effectiveness of the user's model display operation, and thus provide the user with a better model display experience.

[0167] As an optional embodiment, the user parameters include at least one of user physiological parameters, user permissions, user historical operation records and user historical access records; the model parameters or data parameters include at least one of data volume, model representation object type, model physical parameters and model space complexity distribution curve.

[0168] It can be seen that through the above optional embodiments, the content of user parameters and data parameters is limited, so as to effectively characterize the characteristics of users and model data, facilitate subsequent display operation predictions, and assist in recommending the most appropriate model display operations to users based on historical operation records of similar users, so as to improve the efficiency and effectiveness of users' model display operations, and thus provide users with a better model display experience.

[0169] As an optional embodiment, the screening module screens out a plurality of specific modes of similar display operations from a plurality of historical display operations according to the current model slice data, including:

[0170] For each historical display operation, obtain the operation detail parameters and operation model data corresponding to the historical display operation;

[0171] Calculating a third similarity between the operation model data and the current model slice data;

[0172] Determine the fit parameters between the operation detail parameters and the current model slice data;

[0173] Calculate the product of the third similarity and the adaptation parameter to obtain the priority corresponding to the history display operation;

[0174] Operations with priorities greater than a priority threshold are filtered out from all historical display operations to obtain multiple similar display operations.

[0175] It can be seen that through the above-mentioned optional embodiments, the priority of each operation can be accurately determined based on the similarity and adaptability between the operation and model data corresponding to each historical display operation and the current data, so as to screen out multiple suitable similar operations, so as to facilitate subsequent display operation predictions, and assist in recommending the most suitable model display operation to the user based on the historical operation records of similar users, so as to improve the efficiency and effectiveness of the user's model display operation, and thus provide the user with a better model display experience.

[0176] As an optional embodiment, the specific manner in which the screening module determines the fitness parameter between the operation detail parameter and the current model slice data includes:

[0177] Based on the correspondence between the preset parameters and the instructions, the corresponding operation instructions are determined according to the operation detail parameters;

[0178] Execute the operation instructions on the current model slice data, and display the image after execution;

[0179] Inputting the displayed image after execution into a trained display effect prediction neural network to obtain an output display effect; optionally, the display effect prediction neural network is trained by a training data set including a plurality of training model display images and corresponding display effect annotations;

[0180] Inputting the displayed image after execution into a trained model integrity prediction neural network to obtain an output model integrity; optionally, the model integrity prediction neural network is trained using a training data set including a plurality of training model display images and corresponding model integrity annotations;

[0181] The product of the display effect and the model integrity is calculated to obtain the adaptation parameters between the operation detail parameters and the current model slice data.

[0182] It can be seen that through the above-mentioned optional embodiments, the current model can be operated based on the operation instructions corresponding to the operation detail parameters, and the display results after the operation can be predicted in effect and completeness based on two neural networks, so as to accurately calculate the degree of fit between the operation and the model, screen out multiple suitable similar operations, facilitate subsequent display operation predictions, and assist in recommending the most suitable model display operation to the user based on the historical operation records of similar users, so as to improve the efficiency and effect of the user's model display operation, and thus provide the user with a better model display experience.

[0183] As an optional embodiment, the prediction module determines the specific manner of the display operation sequence corresponding to the multiple similar display operations based on the prediction algorithm and the operation track of the current user, including:

[0184] Get the movement track of the current user's operating cursor on the current interface;

[0185] Determine the trajectory portion of the moving trajectory within the display area corresponding to the current model slice data, and obtain the trajectory within the area;

[0186] For each similar display operation, determining operation detail parameters corresponding to the similar display operation;

[0187] Based on the correspondence between the preset parameters and the operation direction, determine the three-dimensional rotation direction parameters corresponding to the operation detail parameters;

[0188] Calculate the fourth similarity between the three-dimensional rotation direction parameter and the trajectory in the area, and obtain the operation priority corresponding to the similar display operation;

[0189] All similar display operations are sorted from large to small according to the operation priority to obtain a display operation sequence.

[0190] It can be seen that through the above-mentioned optional embodiments, the priority of the operation can be determined based on the similarity between the user's cursor movement trajectory and the operation direction of each operation, so as to determine the most appropriate operation display order, and recommend the most appropriate model display operation to the user based on the historical operation records of similar users, so as to improve the efficiency and effectiveness of the user's model display operation, and thus provide the user with a better model display experience.

[0191] As an optional embodiment, the prediction module calculates the fourth similarity between the three-dimensional rotation direction parameter and the trajectory in the area, and obtains the specific manner of the operation priority corresponding to the similar display operation, including:

[0192] Calculate the two-dimensional projection extension direction of the three-dimensional rotation direction parameter on the display interface;

[0193] The weighted sum average of the directional angle differences between the extension direction of each trajectory segment of the trajectory in the area and the extension direction of the two-dimensional projection is calculated to obtain the operation priority corresponding to the similar display operation; wherein the weighted calculation weight corresponding to the directional angle difference corresponding to each trajectory segment is proportional to the trajectory length of the trajectory segment.

[0194] It can be seen that through the above-mentioned optional embodiments, the priority of each operation can be determined based on the weighted calculation of the angle difference between each part of the cursor trajectory in the area and the two-dimensional projection direction of the three-dimensional direction of the operation, so as to determine the most appropriate operation display order, and recommend the most appropriate model display operation to the user based on the historical operation records of similar users, so as to improve the efficiency and effectiveness of the user's model display operation, and thus provide the user with a better model display experience.

[0195] Embodiment 3

[0196] See also Figure 3 , Figure 3 It is another model display processing system for slice data management disclosed in an embodiment of the present invention. Figure 3 The model display processing system for slice data management described is applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 3 As shown, the model display processing system for slice data management may include:

[0197] A memory 301 storing executable program codes;

[0198] a processor 302 coupled to the memory 301;

[0199] The processor 302 calls the executable program code stored in the memory 301 to execute the steps of the model display processing method for slice data management described in the first embodiment.

[0200] Embodiment 4

[0201] An embodiment of the present invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps of the model display processing method for slice data management described in the first embodiment.

[0202] Embodiment 5

[0203] An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps of the model display processing method for slice data management described in Example 1.

[0204] The above describes specific embodiments of the present specification, and other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily have to be performed in the specific order or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0205] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0206] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0207] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification may be in the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the embodiments of this specification may be in the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0208] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0209] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0210] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0211] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0212] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0213] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0214] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0215] This specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0216] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0217] Finally, it should be noted that the model display processing method and system for slice data management disclosed in the embodiment of the present invention discloses only the preferred embodiment of the present invention, which is only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A model display processing method for slice data management, characterized in that: The method comprises: Determine multiple similar users corresponding to the current user who uploaded the current model slice data, including: Get the current model slice data uploaded by the current user; Determining user parameters of the current user; Determining data parameters of the current model slice data; For each candidate user, calculating a first similarity between a user parameter of the candidate user and a user parameter of the current user; Obtain multiple historical upload models corresponding to the candidate user; Calculating an average value of the similarity between the model parameters of each of the historical upload models and the data parameters to obtain a second similarity; Calculating a weighted average of the first similarity and the second similarity to obtain a user similarity between the candidate user and the current user; Filter out users whose user similarity is greater than a first similarity threshold from among all the candidate users, to obtain a plurality of similar users; Determining, in a historical operation database, a plurality of historical display operations corresponding to each of the similar users; Filtering a plurality of similar display operations from the plurality of historical display operations according to the current model slice data; Based on the prediction algorithm, according to the operation track of the current user, determining the display operation sequence corresponding to the multiple similar display operations includes: Obtaining the movement track of the operation cursor of the current user on the current interface; Determine the trajectory portion of the moving trajectory within the display area corresponding to the current model slice data to obtain the trajectory within the area; For each of the similar display operations, determining operation detail parameters corresponding to the similar display operation; Based on the correspondence between the preset parameters and the operation direction, determine the three-dimensional rotation direction parameters corresponding to the operation detail parameters; Calculating a fourth similarity between the three-dimensional rotation direction parameter and the trajectory in the area to obtain an operation priority corresponding to the similar display operation; All the similar display operations are sorted from large to small according to the operation priority to obtain a display operation sequence; the display operation sequence is used to display to the current user.

2. The model display processing method for slice data management according to claim 1, characterized in that: The user parameters include at least one of user physiological parameters, user permissions, user historical operation records and user historical access records; the model parameters or the data parameters include at least one of data volume, model representation object type, model physical parameters and model space complexity distribution curve.

3. The model display processing method for slice data management according to claim 1, characterized in that: The step of filtering out a plurality of similar display operations from the plurality of historical display operations according to the current model slicing data includes: For each of the historical display operations, obtaining operation detail parameters and operation model data corresponding to the historical display operation; Calculating a third similarity between the operation model data and the current model slice data; Determining a fit parameter between the operation detail parameter and the current model slice data; Calculating the product of the third similarity and the adaptation parameter to obtain the priority corresponding to the history display operation; The operations whose priorities are greater than the priority threshold are screened out from all the historical display operations to obtain a plurality of similar display operations.

4. The model display processing method for slice data management according to claim 3, characterized in that: The determining of the adaptation parameter between the operation detail parameter and the current model slice data includes: Based on the correspondence between the preset parameters and the instructions, the corresponding operation instructions are determined according to the operation detail parameters; Executing the operation instruction on the current model slice data to obtain a display image after execution; Inputting the executed display image into a trained display effect prediction neural network to obtain an output display effect; the display effect prediction neural network is trained by a training data set including a plurality of training model display images and corresponding display effect annotations; Inputting the executed display image into a trained model integrity prediction neural network to obtain an output model integrity; the model integrity prediction neural network is trained by a training data set including a plurality of training model display images and corresponding model integrity annotations; The product of the display effect and the model integrity is calculated to obtain the adaptation parameter between the operation detail parameter and the current model slice data.

5. The model display processing method for slice data management according to claim 1, characterized in that: The calculating the fourth similarity between the three-dimensional rotation direction parameter and the trajectory in the area to obtain the operation priority corresponding to the similar display operation includes: Calculating the extension direction of the two-dimensional projection of the three-dimensional rotation direction parameter on the display interface; The weighted average value of the directional angle difference between the extension direction of each trajectory segment of the trajectory in the area and the extension direction of the two-dimensional projection is calculated to obtain the operation priority corresponding to the similar display operation; wherein the weighted calculation weight corresponding to the directional angle difference corresponding to each trajectory segment is proportional to the trajectory length of the trajectory segment.

6. A model display processing system for slice data management, characterized in that: The system comprises: The first determination module is used to determine multiple similar users corresponding to the current user who uploaded the current model slice data, including: Get the current model slice data uploaded by the current user; Determining user parameters of the current user; Determining data parameters of the current model slice data; For each candidate user, calculating a first similarity between a user parameter of the candidate user and a user parameter of the current user; Obtain multiple historical upload models corresponding to the candidate user; Calculating an average value of the similarity between the model parameters of each of the historical upload models and the data parameters to obtain a second similarity; Calculating a weighted average of the first similarity and the second similarity to obtain a user similarity between the candidate user and the current user; Filter out users whose user similarity is greater than a first similarity threshold from among all the candidate users, to obtain a plurality of similar users; A second determination module is used to determine, in a historical operation database, a plurality of historical display operations corresponding to each of the similar users; A screening module, configured to screen out a plurality of similar display operations from the plurality of historical display operations according to the current model slice data; A prediction module, used to determine, based on a prediction algorithm and according to the operation track of the current user, a display operation sequence corresponding to the multiple similar display operations, including: Obtaining the movement track of the operation cursor of the current user on the current interface; Determine the trajectory portion of the moving trajectory within the display area corresponding to the current model slice data to obtain the trajectory within the area; For each of the similar display operations, determining operation detail parameters corresponding to the similar display operation; Based on the correspondence between the preset parameters and the operation direction, determine the three-dimensional rotation direction parameters corresponding to the operation detail parameters; Calculating a fourth similarity between the three-dimensional rotation direction parameter and the trajectory in the area to obtain an operation priority corresponding to the similar display operation; All the similar display operations are sorted from large to small according to the operation priority to obtain a display operation sequence; the display operation sequence is used to display to the current user.

7. A model display processing system for slice data management, characterized in that: The system comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the model display processing method for slice data management as described in any one of claims 1-5.

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