Model synchronization processing method and system for slice data management

Abstract

The invention discloses a model synchronization processing method and system for slice data management. The method comprises the steps that modification operation of a target user on target model slice data is acquired in real time; on the basis of the multiple modification operations of the target user, predicting possible modification operations of the target user in the next time period; according to the possible modification operation, based on a prediction algorithm, determining a corresponding model modification mode and a model modification position; and generating modification synchronization data corresponding to the model modification mode and the model modification position in cloud model data of the target model slice data in a cloud server. Therefore, the cloud model data modification synchronization efficiency can be improved by fully combining the user modification operation prediction, the intelligent degree and the automation degree of the user model modification operation are improved, and better use experience is provided for the user.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a model synchronization 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 modify slice data. Among them, how to effectively improve the convenience and user experience of users' slice data modification operations on the platform is a technical issue that has received much attention. However, most of the existing slice data processing technologies still only update the model data in the cloud based on the modification results of the user's direct modification operations, and do not use the range prediction of the user's modification operation to predict the data modification method and modification location in the cloud in advance. Therefore, the cloud data synchronization efficiency of the user's model modification operation 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 synchronization processing method and system for slice data management, which can fully combine user modification operation prediction to improve the efficiency of cloud model data modification synchronization, improve the intelligence and automation of user model modification operations, and provide users with a better user experience.

[0004] In order to solve the above technical problems, the first aspect of the present invention discloses a model synchronization processing method for slice data management, the method comprising: Obtain the target user's modification operations on the target model slice data in real time; Based on the multiple modification operations of the target user, predicting possible modification operations of the target user in the next time period; According to the possible modification operation, based on the prediction algorithm, determine the corresponding model modification method and model modification position; Generate modification synchronization data corresponding to the model modification method and the model modification position in the cloud model data of the target model slice data in the cloud server; the modification synchronization data is used to modify the cloud model data in response to the next modification operation of the target user being the same as the possible modification operation.

[0005] As an optional implementation, in the first aspect of the present invention, the modification operation includes at least one of modifying a selected model portion, modifying an operation type, modifying a movement trajectory, modifying a time point, and modifying a number of undo operations.

[0006] As an optional implementation, in the first aspect of the present invention, predicting possible modification operations of the target user in the next time period based on the multiple modification operations of the target user includes: Sorting the multiple modification operations of the target user obtained at multiple historical time points from early to late based on the corresponding historical time points to obtain an operation sequence; Based on the prediction neural network, possible modification operations of the target user in the next time period are predicted according to the operation sequence, the user parameters of the target user and the data parameters of the target model slice data.

[0007] 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 data parameters include at least one of data volume, model representation object type, model physical parameters and model space complexity distribution curve.

[0008] As an optional implementation, in the first aspect of the present invention, the predicting neural network-based method predicts possible modification operations of the target user in the next time period according to the operation sequence, the user parameters of the target user, and the data parameters of the target model slice data, including: Using the user parameters of the target user and the data parameters of the target model slice data as operation related information; The operation sequence and the operation related information are input into a trained LSTM neural network to obtain the output of the possible modification operation of the target user in the next time period; the LSTM neural network is trained by a training data set including a plurality of training user operation sequences and corresponding operation related information annotations.

[0009] As an optional implementation, in the first aspect of the present invention, determining the corresponding model modification mode and model modification position according to the possible modification operation based on a prediction algorithm includes: Determining, according to a correspondence between a preset operation and a model change, a plurality of possible model modification modes and a plurality of possible model modification positions corresponding to the possible modification operation; Determine the modification operation corresponding to a historical time point closest to the current time point as a reference modification operation; Determine a reference model modification mode and a reference model modification position corresponding to the reference modification operation; For each possible model modification method, the possible model modification method and the reference model modification method are input into a trained continuous modification rationality prediction model to obtain a first rationality parameter corresponding to the possible model modification method; For each possible modification position of the model, the possible modification position of the model and the modification position of the reference model are input into the continuous modification rationality prediction model to obtain a second rationality parameter corresponding to the possible modification position of the model; the continuous modification rationality prediction model is trained by a training data set including a plurality of training continuous modification operation sequences and corresponding rationality annotations; Determine the possible model modification mode with the highest first rationality parameter as the model modification mode corresponding to the possible modification operation; The model possible modification position with the highest second rationality parameter is determined as the model modification position corresponding to the possible modification operation.

[0010] As an optional implementation, in the first aspect of the present invention, the modification synchronization data corresponding to the model modification mode and the model modification position in the cloud model data of the target model slice data in the cloud server is generated, including: Determine the cloud model data corresponding to the target model slice data in the cloud server; Determine the data portion corresponding to the model modification position of the cloud model data as the model data to be modified; The model data to be modified is modified based on the model modification method to obtain modified synchronization data.

[0011] As an optional embodiment, in the first aspect of the present invention, the method further comprises: In response to obtaining a new modification operation of the target user; Calculating the operation similarity between the new modification operation and the possible modification operation; When the operation similarity is greater than a preset similarity threshold, displaying the possible modification operation to the target user; When the target user agrees to apply the possible modification operation, the modified synchronization data is directly updated to the cloud model data.

[0012] A second aspect of an embodiment of the present invention discloses a model synchronization processing system for slice data management, the system comprising: An acquisition module is used to obtain the modification operations of the target user on the target model slice data in real time; A prediction module, used for predicting possible modification operations of the target user in the next time period based on the multiple modification operations of the target user; A determination module, used to determine a corresponding model modification method and a model modification position according to the possible modification operation based on a prediction algorithm; A generation module is used to generate modification synchronization data corresponding to the model modification method and the model modification position in the cloud model data of the target model slice data in the cloud server; the modification synchronization data is used to modify the cloud model data in response to the next modification operation of the target user being the same as the possible modification operation.

[0013] As an optional implementation, in the second aspect of the present invention, the modification operation includes at least one of modifying a selected model portion, modifying an operation type, modifying a movement trajectory, modifying a time point, and modifying a number of undo operations.

[0014] As an optional implementation, in the second aspect of the present invention, the prediction module predicts a specific manner of possible modification operations of the target user in the next time period based on the multiple modification operations of the target user, including: Sorting the multiple modification operations of the target user obtained at multiple historical time points from early to late based on the corresponding historical time points to obtain an operation sequence; Based on the prediction neural network, possible modification operations of the target user in the next time period are predicted according to the operation sequence, the user parameters of the target user and the data parameters of the target model slice data.

[0015] 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 data parameters include at least one of data volume, model representation object type, model physical parameters and model space complexity distribution curve.

[0016] As an optional implementation, in the second aspect of the present invention, the prediction module predicts the specific manner in which the target user may modify the operation in the next time period based on the prediction neural network according to the operation sequence, the user parameters of the target user, and the data parameters of the target model slice data, including: Using the user parameters of the target user and the data parameters of the target model slice data as operation related information; The operation sequence and the operation related information are input into a trained LSTM neural network to obtain the output of the possible modification operation of the target user in the next time period; the LSTM neural network is trained by a training data set including a plurality of training user operation sequences and corresponding operation related information annotations.

[0017] As an optional implementation, in the second aspect of the present invention, the determination module determines the corresponding model modification mode and model modification position according to the possible modification operation based on the prediction algorithm, including: Determining, according to a correspondence between a preset operation and a model change, a plurality of possible model modification modes and a plurality of possible model modification positions corresponding to the possible modification operation; Determine the modification operation corresponding to a historical time point closest to the current time point as a reference modification operation; Determine a reference model modification mode and a reference model modification position corresponding to the reference modification operation; For each possible model modification method, the possible model modification method and the reference model modification method are input into a trained continuous modification rationality prediction model to obtain a first rationality parameter corresponding to the possible model modification method; For each possible modification position of the model, the possible modification position of the model and the modification position of the reference model are input into the continuous modification rationality prediction model to obtain a second rationality parameter corresponding to the possible modification position of the model; the continuous modification rationality prediction model is trained by a training data set including a plurality of training continuous modification operation sequences and corresponding rationality annotations; Determine the possible model modification mode with the highest first rationality parameter as the model modification mode corresponding to the possible modification operation; The model possible modification position with the highest second rationality parameter is determined as the model modification position corresponding to the possible modification operation.

[0018] As an optional implementation, in the second aspect of the present invention, the specific method of the generation module generating the modification synchronization data corresponding to the model modification mode and the model modification position in the cloud model data of the target model slice data in the cloud server includes: Determine the cloud model data corresponding to the target model slice data in the cloud server; Determine the data portion corresponding to the model modification position of the cloud model data as the model data to be modified; The model data to be modified is modified based on the model modification method to obtain modified synchronization data.

[0019] As an optional embodiment, in the second aspect of the present invention, the system is further configured to perform the following steps: In response to obtaining a new modification operation of the target user; Calculating the operation similarity between the new modification operation and the possible modification operation; When the operation similarity is greater than a preset similarity threshold, displaying the possible modification operation to the target user; When the target user agrees to apply the possible modification operation, the modified synchronization data is directly updated to the cloud model data.

[0020] A third aspect of the present invention discloses another model synchronization processing system for slice data management, the system comprising: 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 part or all of the steps in the model synchronization processing method for slice data management disclosed in the first aspect of the present invention.

[0021] 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 some or all of the steps in the model synchronization processing method for slice data management disclosed in the first aspect of the present invention.

[0022] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: The present invention can predict the possible modification operations of the target user in the next time period based on multiple modification operations of the target user, and then determine the corresponding model modification method and model modification position according to the prediction algorithm to generate modification synchronization data corresponding to the cloud model data in the cloud server to realize early synchronization modification, thereby being able to fully combine the user modification operation prediction to improve the efficiency of cloud model data modification synchronization, improve the intelligence and automation of the user model modification operation, and provide users with a better user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] 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.

[0024] Figure 1 It is a flow chart of a model synchronization processing method for slice data management disclosed in an embodiment of the present invention.

[0025] Figure 2 It is a structural schematic diagram of a model synchronization processing system for slice data management disclosed in an embodiment of the present invention.

[0026] Figure 3 It is a structural schematic diagram of another model synchronization processing system for slice data management disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0027] 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.

[0028] 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.

[0029] 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.

[0030] The present invention discloses a model synchronization processing method and system for slice data management, which can predict the possible modification operations of a target user in the next time period based on multiple modification operations of the target user, and then determine the corresponding model modification method and model modification position according to the prediction algorithm to generate modification synchronization data corresponding to the cloud model data in the cloud server to realize early synchronization modification, thereby being able to fully combine the user modification operation prediction to improve the efficiency of cloud model data modification synchronization, improve the intelligence and automation of the user model modification operation, and provide users with a better user experience. The following are detailed descriptions.

[0031] Embodiment 1 See also Figure 1 , Figure 1 : is a flow chart of a model synchronization processing method for slice data management disclosed in an embodiment of the present invention. Figure 1 The described model synchronization processing method for slice data management 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 1As shown, the model synchronization processing method for slice data management may include the following operations: 101. Obtain the target user's modification operations on the target model slice data in real time.

[0032] 102. Based on the multiple modification operations of the target user, predict possible modification operations of the target user in the next time period. 103. According to the possible modification operations, based on the prediction algorithm, determine the corresponding model modification method and model modification position. 104. Generate modification synchronization data corresponding to the model modification method and the model modification position in the cloud model data of the target model slice data in the cloud server.

[0033] Optionally, the modification synchronization data is used to modify the cloud model data in response to a next modification operation of the target user being the same as a possible modification operation.

[0034] It can be seen that the above-mentioned embodiments of the invention can predict the possible modification operations of the target user in the next time period based on the target user's multiple modification operations, and then determine the corresponding model modification method and model modification position according to the prediction algorithm, so as to generate the modification synchronization data corresponding to the cloud model data in the cloud server to realize early synchronization modification, thereby being able to fully combine the user modification operation prediction to improve the efficiency of cloud model data modification synchronization, improve the intelligence and automation of the user model modification operation, and provide users with a better user experience.

[0035] As an optional embodiment, in the above steps, the modification operation includes at least one of modifying a selected model part, modifying an operation type, modifying a movement track, modifying a time point, and modifying a number of undo operations.

[0036] It can be seen that through the above optional embodiments, the content of the modification operation is limited to accurately characterize the user's modification characteristics, assist in fully combining the user's modification operation prediction to improve the efficiency of cloud model data modification synchronization, improve the intelligence and automation of the user's model modification operation, and provide users with a better user experience.

[0037] As an optional embodiment, in the above step, based on multiple modification operations of the target user, predicting possible modification operations of the target user in the next time period includes: Sort multiple modification operations of the target user obtained at multiple historical time points from early to late based on the corresponding historical time points to obtain an operation sequence; Based on the prediction neural network, the possible modification operations of the target user in the next time period are predicted according to the operation sequence and the user parameters of the target user and the data parameters of the target model slice data.

[0038] It can be seen that through the above optional embodiments, operations can be sorted based on time points to obtain an operation sequence, and then the next possible modification operation can be predicted based on the operation sequence and user model-related parameters based on a neural network, so as to facilitate the subsequent prediction of the modification range and synchronization data, and assist in fully combining user modification operation predictions to improve the efficiency of cloud model data modification synchronization, improve the intelligence and automation of user model modification operations, and provide users with a better user experience.

[0039] 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 data parameters include at least one of data volume, model representation object type, model physical parameters and model space complexity distribution curve.

[0040] 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, predict the next possible modification operation, and assist in fully combining user modification operation predictions to improve the efficiency of cloud model data modification synchronization, improve the intelligence and automation of user model modification operations, and provide users with a better user experience.

[0041] As an optional embodiment, in the above steps, based on the prediction neural network, according to the operation sequence and the user parameters of the target user and the data parameters of the target model slice data, predicting the possible modification operation of the target user in the next time period includes: Using user parameters of the target user and data parameters of the target model slice data as operation related information; The operation sequence and operation related information are input into the trained LSTM neural network to obtain the possible modification operations of the target user in the next time period; the LSTM neural network is trained by a training data set including multiple training user operation sequences and corresponding operation related information annotations.

[0042] In a specific implementation, firstly, the modification operation records of the target user are obtained from multiple historical time points. Each record includes the specific content of the operation and the time when the operation occurred. By sorting these operation records from early to late according to the time point, an ordered operation sequence can be generated. For example, assuming that the target user performed modification operations at these time points: time : Modify the model head size; time : Delete model head; time :Drag the second part of the model to the original head position. After sorting, these operations are organized into a sequence from early to late for subsequent analysis and processing.

[0043] Next, the operation-related information is constructed, including the user parameters of the target user and the data parameters of the target model slice data, while considering the personalized parameters of the target user and the data parameters related to the target model. For example, user parameters may include the user's identification (such as user ID), preferences for operation behaviors (such as commonly used editing operation types), and recent activity levels, while model data parameters may include information related to the system status, such as the current version number of the model and the size of the model slice. For example, a user's parameters may be expressed as: "The user ID is 12345, the role is editor, and the recent activity level is high"; the corresponding model parameters may be: "The version number is 2.1, and the slice size is 10MB." This information will be input into the prediction model as additional contextual data.

[0044] The operation sequence and operation-related information are input into the trained LSTM neural network. The network predicts the modification operations that the target user may perform in the next time period by analyzing the time pattern and context information of the operation sequence. The training process of the LSTM neural network is based on a labeled training data set that contains the historical operation sequences and related information of multiple users. By continuously optimizing the training parameters of the network, it can learn the operation patterns of different users.

[0045] It can be seen that through the above optional embodiments, the operation sequence and operation-related information can be predicted based on the trained LSTM neural network to obtain the possible modification operations of the target user in the next time period, so as to facilitate the subsequent prediction of the modification range and synchronization data, and assist in fully combining the user modification operation prediction to improve the efficiency of cloud model data modification synchronization, improve the intelligence and automation of user model modification operations, and provide users with a better user experience.

[0046] As an optional embodiment, in the above steps, according to the possible modification operation, based on the prediction algorithm, determining the corresponding model modification method and model modification position includes: According to the correspondence between the preset operation and the model change, multiple possible model modification modes and multiple possible model modification positions corresponding to the possible modification operations are determined; Determine a modification operation corresponding to a historical time point closest to the current time point as a reference modification operation; Determine a reference model modification method and a reference model modification position corresponding to the reference modification operation; For each possible model modification method, the possible model modification method and the reference model modification method are input into the trained continuous modification rationality prediction model to obtain a first rationality parameter corresponding to the possible model modification method; For each possible model modification position, the possible model modification position and the reference model modification position are input into the continuous modification rationality prediction model to obtain a second rationality parameter corresponding to the possible model modification position; optionally, the continuous modification rationality prediction model is obtained by training a training data set including a plurality of training continuous modification operation sequences and corresponding rationality annotations; Determine the possible model modification mode with the highest first rationality parameter as the model modification mode corresponding to the possible modification operation; The model possible modification position with the highest second rationality parameter is determined as the model modification position corresponding to the possible modification operation.

[0047] It can be seen that through the above-mentioned optional embodiments, multiple possible modification methods and modification positions of possible modification operations can be determined based on the corresponding relationship, and then based on the prediction of the rationality of continuous modification between the modification method and modification position of the reference operation, the corresponding optimal modification method and modification position can be screened out to facilitate the subsequent prediction of modification synchronization data, assist in fully combining user modification operation predictions to improve the efficiency of cloud model data modification synchronization, improve the intelligence and automation of user model modification operations, and provide users with a better user experience.

[0048] As an optional embodiment, in the above step, generating modification synchronization data corresponding to the model modification mode and the model modification position in the cloud model data of the target model slice data in the cloud server includes: Determine the cloud model data corresponding to the target model slice data in the cloud server; Determine the data portion corresponding to the model modification position of the cloud model data as the model data to be modified; The model data to be modified is modified based on the model modification mode to obtain modified synchronization data.

[0049] It can be seen that through the above-mentioned optional embodiments, the cloud model data of the target model slice data in the cloud server can be modified based on the model modification method and the model modification position to obtain modified synchronization data, thereby fully combining the user modification operation prediction to improve the efficiency of cloud model data modification synchronization, improve the intelligence and automation of user model modification operations, and provide users with a better user experience.

[0050] As an optional embodiment, in the above steps, the method further includes: In response to obtaining a new modification operation of the target user; Calculate the operation similarity between the new modification operation and the possible modification operations; When the operation similarity is greater than a preset similarity threshold, the possible modified operation is displayed to the target user; When the target user agrees to the instruction of possible modification operation of the application, the modified synchronization data is directly updated to the cloud model data.

[0051] It can be seen that through the above-mentioned optional embodiments, modification operations can be recommended to users based on similarity calculations when the target user actually makes modifications, and modification synchronization can be directly applied when the user agrees, thereby achieving a more seamless and intelligent model modification experience. The efficiency of cloud-based model data modification synchronization is improved by fully combining user modification operation predictions, thereby improving the intelligence and automation of user model modification operations, and providing users with a better user experience.

[0052] Embodiment 2 See also Figure 2 , Figure 2 : is a schematic diagram of a model synchronization processing system for slice data management disclosed in an embodiment of the present invention. Figure 2 The described model synchronization processing system for slice data management 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 synchronization processing system for slice data management may include: The acquisition module 201 is used to acquire the modification operation of the target user on the target model slice data in real time.

[0053] The prediction module 202 is used to predict possible modification operations of the target user in the next time period based on multiple modification operations of the target user. The determination module 203 is used to determine the corresponding model modification method and model modification position according to the possible modification operation based on the prediction algorithm. The generating module 204 is used to generate modification synchronization data corresponding to the model modification mode and the model modification position in the cloud model data of the target model slice data in the cloud server.

[0054] Optionally, the modification synchronization data is used to modify the cloud model data in response to a next modification operation of the target user being the same as a possible modification operation.

[0055] It can be seen that the above-mentioned embodiments of the invention can predict the possible modification operations of the target user in the next time period based on the target user's multiple modification operations, and then determine the corresponding model modification method and model modification position according to the prediction algorithm, so as to generate the modification synchronization data corresponding to the cloud model data in the cloud server to realize early synchronization modification, thereby being able to fully combine the user modification operation prediction to improve the efficiency of cloud model data modification synchronization, improve the intelligence and automation of the user model modification operation, and provide users with a better user experience.

[0056] As an optional embodiment, the modification operation includes at least one of modifying a selected model part, modifying an operation type, modifying a movement track, modifying a time point, and modifying a number of undo times.

[0057] It can be seen that through the above optional embodiments, the content of the modification operation is limited to accurately characterize the user's modification characteristics, assist in fully combining the user's modification operation prediction to improve the efficiency of cloud model data modification synchronization, improve the intelligence and automation of the user's model modification operation, and provide users with a better user experience.

[0058] As an optional embodiment, the prediction module predicts the specific manner in which the target user may perform modification operations in the next time period based on the multiple modification operations of the target user, including: Sort multiple modification operations of the target user obtained at multiple historical time points from early to late based on the corresponding historical time points to obtain an operation sequence; Based on the prediction neural network, the possible modification operations of the target user in the next time period are predicted according to the operation sequence and the user parameters of the target user and the data parameters of the target model slice data.

[0059] It can be seen that through the above optional embodiments, operations can be sorted based on time points to obtain an operation sequence, and then the next possible modification operation can be predicted based on the operation sequence and user model-related parameters based on a neural network, so as to facilitate the subsequent prediction of the modification range and synchronization data, and assist in fully combining user modification operation predictions to improve the efficiency of cloud model data modification synchronization, improve the intelligence and automation of user model modification operations, and provide users with a better user experience.

[0060] As an optional embodiment, user parameters include at least one of user physiological parameters, user permissions, user historical operation records and user historical access records; data parameters include at least one of data volume, model representation object type, model physical parameters and model space complexity distribution curve.

[0061] 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, predict the next possible modification operation, and assist in fully combining user modification operation predictions to improve the efficiency of cloud model data modification synchronization, improve the intelligence and automation of user model modification operations, and provide users with a better user experience.

[0062] As an optional embodiment, the prediction module predicts the specific way in which the target user may modify the operation in the next time period based on the prediction neural network and the operation sequence, the user parameters of the target user, and the data parameters of the target model slice data, including: Using user parameters of the target user and data parameters of the target model slice data as operation related information; The operation sequence and operation related information are input into the trained LSTM neural network to obtain the possible modification operations of the target user in the next time period; the LSTM neural network is trained by a training data set including multiple training user operation sequences and corresponding operation related information annotations.

[0063] It can be seen that through the above optional embodiments, the operation sequence and operation-related information can be predicted based on the trained LSTM neural network to obtain the possible modification operations of the target user in the next time period, so as to facilitate the subsequent prediction of the modification range and synchronization data, and assist in fully combining the user modification operation prediction to improve the efficiency of cloud model data modification synchronization, improve the intelligence and automation of user model modification operations, and provide users with a better user experience.

[0064] As an optional embodiment, the determination module determines the corresponding model modification mode and model modification position according to the possible modification operation based on the prediction algorithm, including: According to the correspondence between the preset operation and the model change, multiple possible model modification modes and multiple possible model modification positions corresponding to the possible modification operations are determined; Determine a modification operation corresponding to a historical time point closest to the current time point as a reference modification operation; Determine a reference model modification method and a reference model modification position corresponding to the reference modification operation; For each possible model modification method, the possible model modification method and the reference model modification method are input into the trained continuous modification rationality prediction model to obtain a first rationality parameter corresponding to the possible model modification method; For each possible model modification position, the possible model modification position and the reference model modification position are input into the continuous modification rationality prediction model to obtain a second rationality parameter corresponding to the possible model modification position; optionally, the continuous modification rationality prediction model is obtained by training a training data set including a plurality of training continuous modification operation sequences and corresponding rationality annotations; Determine the possible model modification mode with the highest first rationality parameter as the model modification mode corresponding to the possible modification operation; The model possible modification position with the highest second rationality parameter is determined as the model modification position corresponding to the possible modification operation.

[0065] It can be seen that through the above-mentioned optional embodiments, multiple possible modification methods and modification positions of possible modification operations can be determined based on the corresponding relationship, and then based on the prediction of the rationality of continuous modification between the modification method and modification position of the reference operation, the corresponding optimal modification method and modification position can be screened out to facilitate the subsequent prediction of modification synchronization data, assist in fully combining user modification operation predictions to improve the efficiency of cloud model data modification synchronization, improve the intelligence and automation of user model modification operations, and provide users with a better user experience.

[0066] As an optional embodiment, the specific method of modifying the synchronization data corresponding to the model modification mode and the model modification position in the cloud model data of the target model slice data in the cloud server generated by the generation module includes: Determine the cloud model data corresponding to the target model slice data in the cloud server; Determine the data portion corresponding to the model modification position of the cloud model data as the model data to be modified; The model data to be modified is modified based on the model modification mode to obtain modified synchronization data.

[0067] It can be seen that through the above-mentioned optional embodiments, the cloud model data of the target model slice data in the cloud server can be modified based on the model modification method and the model modification position to obtain modified synchronization data, thereby fully combining the user modification operation prediction to improve the efficiency of cloud model data modification synchronization, improve the intelligence and automation of user model modification operations, and provide users with a better user experience.

[0068] As an optional embodiment, the system is further configured to perform the following steps: In response to obtaining a new modification operation of the target user; Calculate the operation similarity between the new modification operation and the possible modification operations; When the operation similarity is greater than a preset similarity threshold, the possible modified operation is displayed to the target user; When the target user agrees to the instruction of possible modification operation of the application, the modified synchronization data is directly updated to the cloud model data.

[0069] It can be seen that through the above-mentioned optional embodiments, modification operations can be recommended to users based on similarity calculations when the target user actually makes modifications, and modification synchronization can be directly applied when the user agrees, thereby achieving a more seamless and intelligent model modification experience. The efficiency of cloud-based model data modification synchronization is improved by fully combining user modification operation predictions, thereby improving the intelligence and automation of user model modification operations, and providing users with a better user experience.

[0070] Embodiment 3 See also Figure 3 , Figure 3 It is another model synchronization processing system for slice data management disclosed in an embodiment of the present invention. Figure 3 The described model synchronization processing system for slice data management 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 synchronization processing system for slice data management may include: A memory 301 storing executable program codes; a processor 302 coupled to the memory 301; The processor 302 calls the executable program code stored in the memory 301 to execute the steps of the model synchronization processing method for slice data management described in the first embodiment.

[0071] Embodiment 4 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 synchronization processing method for slice data management described in the first embodiment.

[0072] Embodiment 5 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 synchronization processing method for slice data management described in Example 1.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] 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. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0080] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] Finally, it should be noted that the model synchronization 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 synchronization processing method for slice data management, characterized in that: The method comprises: Obtain the target user's modification operations on the target model slice data in real time; Based on the multiple modification operations of the target user, predicting possible modification operations of the target user in the next time period; According to the possible modification operation, based on the prediction algorithm, determine the corresponding model modification method and model modification position; Generate modification synchronization data corresponding to the model modification method and the model modification position in the cloud model data of the target model slice data in the cloud server; the modification synchronization data is used to modify the cloud model data in response to the next modification operation of the target user being the same as the possible modification operation.

2. The model synchronization processing method for slice data management according to claim 1, characterized in that: The modification operation includes at least one of modifying a selected model part, modifying an operation type, modifying a movement track, modifying a time point, and modifying a number of undo times.

3. The model synchronization processing method for slice data management according to claim 1, characterized in that: The predicting, based on the multiple modification operations of the target user, possible modification operations of the target user in the next time period includes: Sorting the multiple modification operations of the target user obtained at multiple historical time points from early to late based on the corresponding historical time points to obtain an operation sequence; Based on the prediction neural network, possible modification operations of the target user in the next time period are predicted according to the operation sequence, the user parameters of the target user and the data parameters of the target model slice data.

4. The model synchronization processing method for slice data management according to claim 3, 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 data parameters include at least one of data volume, model representation object type, model physical parameters and model space complexity distribution curve.

5. The model synchronization processing method for slice data management according to claim 3, characterized in that: The predicting neural network-based method predicts possible modification operations of the target user in the next time period according to the operation sequence, the user parameters of the target user, and the data parameters of the target model slice data, including: Using the user parameters of the target user and the data parameters of the target model slice data as operation related information; The operation sequence and the operation related information are input into a trained LSTM neural network to obtain the output of the possible modification operation of the target user in the next time period; the LSTM neural network is trained by a training data set including a plurality of training user operation sequences and corresponding operation related information annotations.

6. The model synchronization processing method for slice data management according to claim 1, characterized in that: The determining, according to the possible modification operation and based on a prediction algorithm, a corresponding model modification method and a model modification position includes: Determining, according to a correspondence between a preset operation and a model change, a plurality of possible model modification modes and a plurality of possible model modification positions corresponding to the possible modification operation; Determine the modification operation corresponding to a historical time point closest to the current time point as a reference modification operation; Determine a reference model modification mode and a reference model modification position corresponding to the reference modification operation; For each possible model modification method, the possible model modification method and the reference model modification method are input into a trained continuous modification rationality prediction model to obtain a first rationality parameter corresponding to the possible model modification method; For each possible modification position of the model, the possible modification position of the model and the modification position of the reference model are input into the continuous modification rationality prediction model to obtain a second rationality parameter corresponding to the possible modification position of the model; the continuous modification rationality prediction model is trained by a training data set including a plurality of training continuous modification operation sequences and corresponding rationality annotations; Determine the possible model modification mode with the highest first rationality parameter as the model modification mode corresponding to the possible modification operation; The model possible modification position with the highest second rationality parameter is determined as the model modification position corresponding to the possible modification operation.

7. The model synchronization processing method for slice data management according to claim 1, characterized in that: The modification synchronization data corresponding to the model modification mode and the model modification position in the cloud model data of the target model slice data in the cloud server is generated, including: Determine the cloud model data corresponding to the target model slice data in the cloud server; Determine the data portion corresponding to the model modification position of the cloud model data as the model data to be modified; The model data to be modified is modified based on the model modification method to obtain modified synchronization data.

8. The model synchronization processing method for slice data management according to claim 7, characterized in that: The method further comprises: In response to obtaining a new modification operation of the target user; Calculating the operation similarity between the new modification operation and the possible modification operation; When the operation similarity is greater than a preset similarity threshold, displaying the possible modification operation to the target user; When the target user agrees to apply the possible modification operation, the modified synchronization data is directly updated to the cloud model data.

9. A model synchronization processing system for slice data management, characterized in that: The system comprises: An acquisition module is used to obtain the modification operations of the target user on the target model slice data in real time; A prediction module, used for predicting possible modification operations of the target user in the next time period based on the multiple modification operations of the target user; A determination module, used to determine a corresponding model modification method and a model modification position according to the possible modification operation based on a prediction algorithm; A generation module is used to generate modification synchronization data corresponding to the model modification method and the model modification position in the cloud model data of the target model slice data in the cloud server; the modification synchronization data is used to modify the cloud model data in response to the next modification operation of the target user being the same as the possible modification operation.

10. A model synchronization 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 synchronization processing method for slice data management according to any one of claims 1 to 8.

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