CN117827192A - Three-dimensional model generation system - Google Patents
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Abstract
The invention discloses a three-dimensional model generation system, and particularly relates to the field of model construction. Meanwhile, by recording computer performance data, calculating a performance attenuation coefficient, monitoring hardware performance attenuation in real time, adjusting risk control according to performance attenuation conditions, and improving reliability and efficiency of a three-dimensional model construction task.
Description
Technical Field
The invention relates to the field of model construction, in particular to a three-dimensional model generation system.
Background
In the construction of three-dimensional models, users often face errors in operation, data entry errors, or unreasonable parameters in the construction of a certain step of the model, which may seem to be trivial, but in fact may have a significant negative impact on the overall modeling process. When these problems occur, they typically result in sudden increases in computation and computation pressures, resulting in performance problems for the modeling software, and even software crashes. This not only causes frustration to the user, but also greatly prolongs the processing time of the project, and affects the smooth progress of the modeling work.
In order to solve the above problems, a technical solution is now provided.
Disclosure of Invention
In order to overcome the defects in the prior art, the embodiment of the invention provides the steps of inputting parameters of a current step of constructing a three-dimensional model, constructing a low-power lightweight virtual model, collecting parameters of the low-power virtual model, comprehensively calculating step execution risk indexes for predicting operation risks, generating risk signals of different grades, and providing clear execution risk prompts so as to ensure reasonable allocation of calculation resources, reduce project processing time and ensure smooth execution of modeling work. Meanwhile, by recording computer performance data, calculating a performance attenuation coefficient, monitoring hardware performance attenuation in real time, adjusting risk control according to performance attenuation conditions, and improving the reliability and efficiency of a three-dimensional model construction task, the problems in the background technology are solved.
In order to achieve the above purpose, the present invention provides the following technical solutions: the system comprises a model building module and a model rendering module, wherein the model building module comprises a parameter simulation unit, a parameter collection unit, a data analysis unit, a performance acquisition unit and a dynamic and static switching unit;
the parameter simulation unit is used for simulating the low-power virtual model after the user inputs the parameters and sending the low-power virtual model to the parameter collection unit;
the parameter collection unit is used for collecting construction parameters and resource parameters of the low-power simulation model and sending the construction parameters and the resource parameters to the data analysis unit;
the data analysis unit builds an analysis model through the building parameters and the resource parameters, generates step execution risk coefficients, compares the step execution risk coefficients with the distinguishing threshold values, and generates different operation risk levels;
the performance acquisition unit is used for acquiring original performance scores and corresponding use frequencies of a plurality of factors of the computer, generating performance attenuation coefficients according to the original performance scores and the corresponding use frequencies of the plurality of factors, judging whether to generate a trigger signal according to the performance attenuation coefficients, and transmitting the trigger to the dynamic and static switching unit under the condition of acquiring the trigger signal;
the dynamic and static switching unit is used for adjusting the distinguishing threshold value, generating an adjusting signal and sending the adjusting signal to the data analysis unit.
In a preferred embodiment, the operation of the parameter simulation unit comprises the following:
step 11), a user inputs parameters required by the current step of constructing the three-dimensional model on model software, including the size, shape, quantity, material properties and the like of the model;
step 12), the model software analyzes and verifies the parameters to ensure that the parameters are valid and meet the requirements of model construction;
step 13), generating a low-power virtual model according to parameters provided by a user, wherein the model is built according to the user requirement, but is reduced and simplified by fixed times according to the input parameters of the user, and the low-power virtual model does not output and display and is focused on the building and calculation of the model.
In a preferred embodiment, the build parameters include a dimensional complexity index and an edit complexity index, and the resource parameters include an overstep performance float index.
In a preferred embodiment, the dimension complexity index acquisition logic is:
step 21), counting the number of triangles, the number of vertexes, the number of components and the nesting depth of the components of the low-power virtual model;
step 22), calculating a dimension complexity index, wherein the calculation formula is as follows:
where mci, tn, vn, cn, cnd is the dimension complexity index, the number of triangles, the number of vertices, the number of components, and the component nesting depth, respectively.
In a preferred embodiment, the acquisition logic for editing the complex index is:
step 31), counting the model volume, the material quantity, the texture resolution and the editing step number of the low-power virtual model;
step 32), calculating a dimension complex index, wherein the calculation formula is as follows:
wherein ci, mv, mc, tr, esc is the model volume, the material number, the texture resolution, and the number of editing steps, respectively.
In a preferred embodiment, the superscalar performance floating index acquisition logic is:
step 41), recording frame rate changes when constructing the low-power virtual model, and collecting a plurality of frame rate data at the same interval;
step 42), calculating an out-of-standard performance change index, wherein the calculation formula is as follows:
in the formula, fh i Representing the frame rate of the ith frame, th representing the frame rate difference threshold, Δt representing the time required to complete the construction of the low-power virtual model, if |fh i -fh i+1 I is greater than th, return I fh i -fh i+1 Otherwise, returning to 0, i represents the sequence number of the recorded frame rate, i=1, 2 … … n, n represents the total number of acquired frame rates, and n is a positive integer.
In a preferred embodiment, the operation of the data analysis unit comprises the following:
the step execution risk coefficient is obtained by comprehensively analyzing and calculating the dimension complex index, the editing complex index and the exceeding performance floating index, and the calculation formula is as follows:
wherein Ri is a step execution risk coefficient, C is a real model and a low-power virtual modelScaling factors between the pseudo models, the scaling factors being used to represent scale values between the real model and the low-power virtual model, mci, ci, fi being the dimensional complexity index, the edit complexity index, and the superscalar performance floating index, k, respectively 1 、k 2 、k 3 The preset proportional coefficients of the dimension complex index, the editing complex index and the exceeding performance floating index are respectively set;
after the step execution risk index is obtained, comparing the step execution risk coefficient with a distinguishing threshold, wherein the distinguishing threshold comprises a first risk distinguishing threshold and a second risk distinguishing threshold, if the step execution risk coefficient is larger than or equal to the first risk distinguishing threshold, generating a high-level risk signal, and sending out an early warning prompt; if the risk coefficient of the step execution is smaller than the risk distinguishing threshold value II and larger than or equal to the risk distinguishing threshold value I, generating a moderate risk signal, and storing the three-dimensional file before the step execution; and if the risk coefficient of step execution is smaller than the first risk discrimination threshold, generating a low-level risk signal.
In a preferred embodiment, the operation of the performance acquisition unit comprises the following:
step 51), recording the original performance scores and corresponding frequencies of use for a plurality of factors of the computer, including: CPU reference performance score, memory, hard disk read-write speed, 3D performance score of display card, energy efficiency grade of computer;
step 52), calculating a performance attenuation coefficient as follows:
wherein PD is a performance decay coefficient used for measuring the reduction degree of calculation resources and Rps j Is the raw performance score, wf, of each factor j Is a weight factor of each factor for adjusting the relative importance thereof, uf j Is the frequency of use, af, of each factor j Is the aging factor of each factor, i.e., the rate of performance change over time, j is the number of factors, j=1, 2 … … m, m represents the total number of factors, and m is a positive integer.
In a preferred embodiment, after the performance attenuation coefficient is obtained, comparing the performance attenuation coefficient with a performance attenuation threshold, and if the performance attenuation coefficient is greater than or equal to the performance attenuation threshold, generating a trigger signal; otherwise, no trigger signal is generated;
the operation process of the dynamic and static switching unit comprises the following steps:
after the trigger signal is obtained, the first and second risk distinguishing thresholds are adjusted through the performance attenuation coefficient, and the calculation formula is as follows:
wherein (RT 1, RT 2) represents the original risk classification threshold of one, two, TZ [ RT (1), RT (2)]Representing the adjusted risk classification threshold value of one and two, PD and PD' are respectively the performance attenuation coefficient and the performance attenuation threshold value, and eta isAnd η is greater than 0;
and replacing the original risk classification threshold value I and the original risk classification threshold value II by the adjusted risk classification threshold value I and the adjusted risk classification threshold value II.
In a preferred embodiment, after each step is performed, the fabricated three-dimensional model is sent to a model rendering module for performing rendering operation on the three-dimensional model, and the three-dimensional model is output after rendering is completed.
The three-dimensional model generation system has the technical effects and advantages that:
1. the model constructed by each step is subjected to low-power light weight in the process of constructing the three-dimensional model, so that the low-power virtual model is generated, the calculation complexity is reduced, the calculation efficiency is improved, the calculation resource consumption can be reduced while the calculation model complexity is met, and the method is particularly suitable for large complex models; collecting a dimension complex index, an editing complex index and an exceeding performance floating index of a low virtual model, comprehensively calculating the parameters to obtain a step execution risk index, and using the step execution risk index to predict the running risk before executing the step in advance, respectively comparing the step execution risk index with corresponding risk distinguishing thresholds I and II, generating different-level risk signals according to the comparison result, further giving an operator clear execution risk prompt, ensuring that each execution step is in a reasonable calculation resource range, reducing the time of project processing, and ensuring the smooth execution of modeling work;
2. the original performance frequency spectrum and the corresponding use frequency of the factors of the computer are recorded, the performance attenuation coefficient is obtained through calculation, the performance attenuation coefficient is used for evaluating the performance attenuation degree of hardware for constructing the three-dimensional model, the performance attenuation coefficient is compared with the performance attenuation threshold value and is used for judging whether a trigger signal is generated or not, after the trigger signal is obtained, the first risk classification threshold value and the second risk classification threshold value are actively interfered and adjusted in time based on the performance attenuation coefficient, and further the risk gate of the three-dimensional model of the framework can be adjusted based on the performance condition of the hardware, the reliability and the efficiency of the task for constructing the three-dimensional model are improved, and risk misjudgment is avoided.
Drawings
FIG. 1 is a schematic diagram of a three-dimensional model generating system according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
FIG. 1 shows a three-dimensional model generating system of the invention, which comprises a model building module and a model rendering module, wherein the model building module comprises a parameter simulation unit, a parameter collection unit, a data analysis unit, a performance acquisition unit and a dynamic and static switching unit;
the parameter simulation unit is used for simulating the low-power virtual model after the user inputs the parameters and sending the low-power virtual model to the parameter collection unit;
the parameter collection unit is used for collecting construction parameters and resource parameters of the low-power simulation model and sending the construction parameters and the resource parameters to the data analysis unit;
the data analysis unit builds an analysis model through the building parameters and the resource parameters, generates step execution risk coefficients, compares the step execution risk coefficients with the distinguishing threshold values, and generates different operation risk levels;
the performance acquisition unit is used for acquiring original performance scores and corresponding use frequencies of a plurality of factors of the computer, generating performance attenuation coefficients according to the original performance scores and the corresponding use frequencies of the plurality of factors, judging whether to generate a trigger signal according to the performance attenuation coefficients, and transmitting the trigger to the dynamic and static switching unit under the condition of acquiring the trigger signal;
the dynamic and static switching unit is used for adjusting the distinguishing threshold value, generating an adjusting signal and sending the adjusting signal to the data analysis unit.
The operation process of the parameter simulation unit comprises the following steps:
step 11), a user inputs parameters required by the current step of constructing the three-dimensional model on model software, including the size, shape, quantity, material properties and the like of the model;
step 12), the model software analyzes and verifies the parameters to ensure that the parameters are valid and meet the requirements of model construction; for example, if the user entered a model's size parameters, the system should verify whether these sizes are physically reasonable or whether the software constraints are met;
examples:
if a user were to build a model of a bridge, they might enter parameters such as length, width, height, material properties, etc. of the bridge. In the parameter validation and parsing stage, the system will verify these parameters, ensuring that the dimensions of the bridge are engineering viable, and that the selected material properties are available in the model.
Further, the parameters input by the user are ensured to be reasonable, and errors and unnecessary calculation in the subsequent model construction stage can be avoided.
Step 13), generating a low-power virtual model according to parameters provided by a user, wherein the model is constructed according to the user requirement, but is reduced and simplified by fixed times according to the input parameters of the user, and the low-power virtual model does not output and display, but is focused on the establishment and calculation of the model;
examples:
taking the bridge model as an example, the system constructs a low-power virtual bridge model by using parameters input by a user so as to accelerate the calculation speed.
The method has the advantages of reducing the calculation complexity and improving the calculation efficiency, and can reduce the consumption of calculation resources when meeting the calculation model complexity, especially for large complex models.
The operation process of the parameter collecting unit comprises the following steps:
the construction parameters comprise a dimension complexity index and an edit complexity index, and the resource parameters comprise a exceeding performance floating index.
The acquisition logic of the dimension complex index is as follows:
step 21), counting the number of triangles, the number of vertexes, the number of components and the nesting depth of the components of the low-power virtual model;
step 22), calculating a dimension complexity index, wherein the calculation formula is as follows:
where mci, tn, vn, cn, cnd is the dimension complexity index, the number of triangles, the number of vertices, the number of components, and the component nesting depth, respectively.
The number of triangles refers to the total number of triangles in the polygons that make up the three-dimensional model. In computer graphics, a triangle is typically one of the basic building blocks used to represent geometry. More triangles generally means that the model has more detail and higher complexity;
the number of vertices refers to the total number of vertices or vertex coordinates that make up the three-dimensional model. Each vertex typically contains three-dimensional coordinates (x, y, z) to define the geometry of the model. The number of vertices is generally related to the details and accuracy of the model;
the number of components refers to independently operable or manageable parts of the model, which may be sub-parts of the model or components, such as floors in a building model, tires in an automobile model, etc. The number of components is used to organize and manage complex models;
component nesting depth refers to the nesting level depth of a component in a model. For example, an automobile model may contain a body component, which may contain multiple sub-components, such as an engine, a seat, and the like. The component nesting depth represents a hierarchy between components in the model.
The dimension complexity index is used for evaluating the computational resource requirements of the low-power virtual model to help evaluate the computational pressure and performance requirements of the model, so that the complexity of the low-power virtual model can be better known when the low-power virtual model is constructed, the computational resources can be more effectively distributed, the computational pressure is reduced, and meanwhile, a reference is provided for the construction of a real three-dimensional model.
The acquisition logic for editing the complex index is:
step 31), counting the model volume, the material quantity, the texture resolution and the editing step number of the low-power virtual model;
step 32), calculating a dimension complex index, wherein the calculation formula is as follows:
wherein ci, mv, mc, tr, esc is the model volume, the material number, the texture resolution, and the number of editing steps, respectively.
Model volume refers to the actual volume or spatial extent that a three-dimensional model occupies in three-dimensional space. Typically, the model volume is used to take into account the physical size and space occupation of the model in order to assess the complexity of the model;
the number of materials refers to the number of different materials used in the model. Materials are attributes that are used to impart surface properties (e.g., color, reflectivity, gloss, etc.) to a model. The material quantity considers the material diversity of the model;
texture resolution refers to the pixel resolution of the texture map applied on the model. Texture resolution is commonly used to describe the sharpness and level of detail of a texture map. Higher resolutions generally require more computing resources;
the number of editing steps refers to the number of steps required to create or edit the model. The number of editing steps reflects the manufacturing history and complexity of the model. A larger number of editing steps may represent a more complex model making process.
The performance requirements of the low power virtual model in terms of memory pressure are evaluated. A higher complexity index indicates that the model requires more resources in memory to store and process, which may result in greater memory pressure for the construction and rendering process of the low power virtual model, requiring more computing resources to ensure fluent performance. Thus, the index helps the decision maker better understand the memory resource requirements of the low-power virtual model in order to efficiently allocate resources to meet the model building and rendering requirements.
The acquisition logic of the exceeding performance floating index is as follows:
the frame rate of modeling software typically varies with computer performance. The frame rate is an important indicator of the performance of a computer graphic and represents the number of frames of an image that are displayed in one second. Typically, the frame rate of modeling software will be higher when the computer performance is better, and lower when the performance is worse.
Modeling software, when performing operations such as rendering, rotation, scaling, selection, etc., requires handling a large number of geometric and graphics computations that place high demands on hardware resources such as the computer's processor, graphics card, and memory. When the computer performs better, it can process these computing tasks faster, resulting in a higher frame rate. Conversely, a poorly performing computer may not be able to handle these tasks at the same speed, resulting in a lower frame rate.
High frame rates generally mean a smoother user experience, especially in terms of modeling and interaction. The user can more easily manipulate and view the model. Therefore, for three-dimensional modeling work requiring high performance, efficiency can be improved by using a computer with better performance
Step 41), recording frame rate changes when constructing the low-power virtual model, and collecting a plurality of frame rate data at the same interval;
step 42), calculating an out-of-standard performance change index, wherein the calculation formula is as follows:
in the formula, fh i Representing the frame rate of the ith frame, th representing the frame rate difference threshold, Δt representing the time required to complete the construction of the low-power virtual model, if |fh i -fh i+1 I is greater than th, return I fh i -fh i+1 Otherwise, returning to 0, i represents the sequence number of the recorded frame rate, i=1, 2 … … n, n represents the total number of acquired frame rates, and n is a positive integer.
The superscalar performance floating index is an important metric for measuring the degree of influence on the calculated performance in calculating the low-power virtual model. When the number of the exceeding performance floating index is larger, this means that the computing pressure born by the low-power virtual model calculation in the current step is larger, the demand of computing resources is relatively higher, and thus the computing performance is more adversely affected. This increase in metrics may reflect an increase in model complexity, resource requirements, or computational load, or an inadequacy of the current computing environment. In practice, the monitoring and analysis of the out-of-standard performance floating index helps to optimize the construction flow of the low-power virtual model, ensuring that the computational performance is within an acceptable range to provide a fluent user experience.
The operation process of the data analysis unit comprises the following steps:
the step execution risk coefficient is obtained by comprehensively analyzing and calculating the dimension complex index, the editing complex index and the exceeding performance floating index, and the calculation formula is as follows:
wherein Ri is a step execution risk coefficient, C is a scaling factor between the real model and the low-power virtual model, the scaling factor is used for representing a scale value between the real model and the low-power virtual model, mci, ci, fi is a dimension complexity index, an edit complexity index and an overstandard performance floating index, k respectively 1 、k 2 、k 3 The preset proportionality coefficients are respectively a dimension complexity index, an editing complexity index and an exceeding performance floating index.
The step execution risk factor is used to assess the degree of risk that may be faced when performing a particular task or step in the process of building a three-dimensional model. This index takes into account the dimensional complexity index, the edit complexity index, and the superscalar performance float index in combination to help evaluate the risk and performance impact of task execution.
The larger the number of step execution risk indices, the higher the risk that may be faced in executing the task or step, and also means that more computing resources, time or optimization measures may be needed to ensure successful completion of the task. Conversely, if the step execution risk index is smaller, then the risk of the task is lower, less resources may be required, and performance of the task execution may be more stable.
Thus, the step performance risk index is used to help a decision maker better understand the possible risk and performance issues before performing a particular task or step. By analyzing and monitoring this index, appropriate measures can be taken to optimize task performance to ensure that the desired performance level is achieved and to reduce potential problems.
After the step execution risk index is obtained, comparing the step execution risk coefficient with a distinguishing threshold, wherein the distinguishing threshold comprises a first risk distinguishing threshold and a second risk distinguishing threshold, if the step execution risk coefficient is larger than or equal to the second risk distinguishing threshold, the step execution risk coefficient indicates that the calculation pressure which is caused by the step execution of the parameter is far and the calculation performance is not matched, the probability of software breakdown is easily increased greatly, an advanced risk signal is generated, and an early warning prompt is sent; if the risk coefficient of step execution is smaller than the risk distinguishing threshold value II and larger than or equal to the risk distinguishing threshold value I, the risk coefficient is indicated to be easy to cause software blocking or operation time extension, a moderate risk signal is generated, and a three-dimensional file is stored before the step execution; if the risk coefficient of step execution is smaller than the first risk distinguishing threshold value, the operation of the step execution is pressureless, the probability of faults is greatly reduced, and a low-level risk signal is generated.
In the invention, the model constructed by each step is subjected to low-power light weight in the process of constructing the three-dimensional model, so that the low-power virtual model is generated, the calculation complexity is reduced, the calculation efficiency is improved, and the consumption of calculation resources can be reduced while the calculation model complexity is met, particularly for a large-scale complex model; the method comprises the steps of collecting a dimension complex index, editing the complex index and an exceeding performance floating index of a low virtual model, comprehensively calculating the parameters to obtain a step execution risk index, predicting the running risk before executing the step in advance, comparing the step execution risk index with corresponding risk distinguishing thresholds I and II respectively and independently, generating different-level risk signals according to comparison results, further giving an operator clear execution risk prompt, ensuring that each execution step is in a reasonable calculation resource range, reducing the time of project processing, and ensuring smooth execution of modeling work.
The operation process of the performance acquisition unit comprises the following steps:
step 51), recording the original performance scores and corresponding frequencies of use for a plurality of factors of the computer, including: CPU reference performance score, memory, hard disk read-write speed, 3D performance score of display card, energy efficiency grade of computer;
step 52), calculating a performance attenuation coefficient as follows:
wherein PD is a performance attenuation coefficient used for measuring and calculating materialsDegree of reduction of source, rps j Is the raw performance score, wf, of each factor j Is a weight factor of each factor for adjusting the relative importance thereof, uf j Is the frequency of use, af, of each factor j Is the aging factor of each factor, i.e., the rate of performance change over time, j is the number of factors, j=1, 2 … … m, m represents the total number of factors, and m is a positive integer.
The performance attenuation coefficient represents the degradation degree of the hardware performance for constructing the three-dimensional model, and when the performance attenuation coefficient is larger, the degradation degree of the performance of the computer is represented to be higher, which means that the performance of the computer is severely degraded, and more maintenance, upgrading or repairing measures may be required to improve the performance. Meaning that the computer may be slower or less stable in performing the task.
When the coefficient of performance decay is smaller, it means that the degree of degradation of the computer performance is lower. This means that the performance state of the computer is relatively good and still can perform tasks efficiently. Indicating that the performance of the computer is still stable and no urgent maintenance or upgrades are required.
After the performance attenuation coefficient is obtained, comparing the performance attenuation coefficient with a performance attenuation threshold, if the performance attenuation coefficient is larger than or equal to the performance attenuation threshold, indicating that the performance has been obviously reduced, and generating a trigger signal; otherwise, no trigger signal is generated.
The operation process of the dynamic and static switching unit comprises the following steps:
after the trigger signal is obtained, the first and second risk distinguishing thresholds are adjusted through the performance attenuation coefficient, and the calculation formula is as follows:
wherein (RT 1, RT 2) represents the original risk classification threshold of one, two, TZ [ RT (1), RT (2)]Representing the adjusted risk classification threshold value of one and two, PD and PD' are respectively the performance attenuation coefficient and the performance attenuation threshold value, and eta isAnd eta is greater than 0.
And replacing the original risk classification threshold value I and the original risk classification threshold value II by the adjusted risk classification threshold value I and the adjusted risk classification threshold value II.
According to the method, the original performance frequency spectrum and the corresponding use frequency of the factors of the computer are recorded, the performance attenuation coefficient is obtained through calculation, the performance attenuation coefficient is used for evaluating and evaluating the hardware performance attenuation degree of the three-dimensional model, the performance attenuation coefficient is compared with the performance attenuation threshold value and used for judging whether a trigger signal is generated, after the trigger signal is obtained, the first risk classification threshold value and the second risk classification threshold value are actively interfered and adjusted in time based on the performance attenuation coefficient, and further the risk gate of the three-dimensional model of the framework can be adjusted based on the performance condition of the hardware, the reliability and the efficiency of the three-dimensional model construction task are improved, and risk misjudgment is avoided.
After each step is executed, the manufactured three-dimensional model is sent to a model rendering module for rendering the three-dimensional model, and the three-dimensional model is output after rendering.
The above formulas are all formulas with dimensionality removed and numerical calculation, the formulas are formulas with the latest real situation obtained by software simulation through collecting a large amount of data, and preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with the embodiments of the present application are all or partially produced. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more sets of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution.
It will be clear to those skilled in the art that, for convenience and brevity of description, the specific working procedures of the systems, apparatuses and units described above may refer to the corresponding procedures in the foregoing embodiments, and are not repeated here.
In the several embodiments provided in this application, it should be understood that the disclosed systems and apparatuses may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or a part of the technical solution, in the form of a software product stored in a storage medium, including several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Finally: the foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (10)
1. A three-dimensional model generation system, comprising the steps of: the system comprises a model building module and a model rendering module, wherein the model building module comprises a parameter simulation unit, a parameter collection unit, a data analysis unit, a performance acquisition unit and a dynamic and static switching unit;
the parameter simulation unit is used for simulating the low-power virtual model after the user inputs the parameters and sending the low-power virtual model to the parameter collection unit;
the parameter collection unit is used for collecting construction parameters and resource parameters of the low-power simulation model and sending the construction parameters and the resource parameters to the data analysis unit;
the data analysis unit builds an analysis model through the building parameters and the resource parameters, generates step execution risk coefficients, compares the step execution risk coefficients with the distinguishing threshold values, and generates different operation risk levels;
the performance acquisition unit is used for acquiring original performance scores and corresponding use frequencies of a plurality of factors of the computer, generating performance attenuation coefficients according to the original performance scores and the corresponding use frequencies of the plurality of factors, judging whether to generate a trigger signal according to the performance attenuation coefficients, and transmitting the trigger to the dynamic and static switching unit under the condition of acquiring the trigger signal;
the dynamic and static switching unit is used for adjusting the distinguishing threshold value, generating an adjusting signal and sending the adjusting signal to the data analysis unit.
2. A three-dimensional model generation system according to claim 1, wherein:
the operation process of the parameter simulation unit comprises the following steps:
step 11), a user inputs parameters required by the current step of constructing the three-dimensional model on model software, including the size, shape, quantity, material properties and the like of the model;
step 12), the model software analyzes and verifies the parameters to ensure that the parameters are valid and meet the requirements of model construction;
step 13), generating a low-power virtual model according to parameters provided by a user, wherein the model is built according to the user requirement, but is reduced and simplified by fixed times according to the input parameters of the user, and the low-power virtual model does not output and display and is focused on the building and calculation of the model.
3. A three-dimensional model generation system according to claim 2, characterized in that:
the construction parameters comprise a dimension complexity index and an edit complexity index, and the resource parameters comprise a exceeding performance floating index.
4. A three-dimensional model generation system according to claim 3, characterized in that:
the acquisition logic of the dimension complex index is as follows:
step 21), counting the number of triangles, the number of vertexes, the number of components and the nesting depth of the components of the low-power virtual model;
step 22), calculating a dimension complexity index, wherein the calculation formula is as follows:
where mci, tn, vn, cn, cnd is the dimension complexity index, the number of triangles, the number of vertices, the number of components, and the component nesting depth, respectively.
5. The three-dimensional model generation system of claim 4, wherein:
the acquisition logic for editing the complex index is:
step 31), counting the model volume, the material quantity, the texture resolution and the editing step number of the low-power virtual model;
step 32), calculating a dimension complex index, wherein the calculation formula is as follows:
wherein ci, mv, mc, tr, esc is the model volume, the material number, the texture resolution, and the number of editing steps, respectively.
6. The three-dimensional model generation system of claim 5, wherein:
the acquisition logic of the exceeding performance floating index is as follows:
step 41), recording frame rate changes when constructing the low-power virtual model, and collecting a plurality of frame rate data at the same interval;
step 42), calculating an out-of-standard performance change index, wherein the calculation formula is as follows:
in the formula, fh i Representing the frame rate of the ith frame, th representing the frame rate difference threshold, Δt representing the time required to complete the construction of the low-power virtual model, if |fh i -fh i+1 I is greater than th, return I fh i -fh i+1 Otherwise, returning to 0, i represents the sequence number of the recorded frame rate, i=1, 2 … … n, n represents the total number of acquired frame rates, and n is a positive integer.
7. The three-dimensional model generation system of claim 6, wherein:
the operation process of the data analysis unit comprises the following steps:
the step execution risk coefficient is obtained by comprehensively analyzing and calculating the dimension complex index, the editing complex index and the exceeding performance floating index, and the calculation formula is as follows:
wherein Ri is a step execution risk coefficient, C is a scaling factor between the real model and the low-power virtual model, the scaling factor is used for representing a scale value between the real model and the low-power virtual model, and mci, ci, fi is a dimension complexity index, an edit complexity index and a sum, respectivelyExceeding performance float index, k 1 、k 2 、k 3 The preset proportional coefficients of the dimension complex index, the editing complex index and the exceeding performance floating index are respectively set;
after the step execution risk index is obtained, comparing the step execution risk coefficient with a distinguishing threshold, wherein the distinguishing threshold comprises a first risk distinguishing threshold and a second risk distinguishing threshold, if the step execution risk coefficient is larger than or equal to the first risk distinguishing threshold, generating a high-level risk signal, and sending out an early warning prompt; if the risk coefficient of the step execution is smaller than the risk distinguishing threshold value II and larger than or equal to the risk distinguishing threshold value I, generating a moderate risk signal, and storing the three-dimensional file before the step execution; and if the risk coefficient of step execution is smaller than the first risk discrimination threshold, generating a low-level risk signal.
8. The three-dimensional model generation system of claim 7, wherein:
the operation process of the performance acquisition unit comprises the following steps:
step 51), recording the original performance scores and corresponding frequencies of use for a plurality of factors of the computer, including: CPU reference performance score, memory, hard disk read-write speed, 3D performance score of display card, energy efficiency grade of computer;
step 52), calculating a performance attenuation coefficient as follows:
wherein PD is a performance decay coefficient used for measuring the reduction degree of calculation resources and Rps j Is the raw performance score, wf, of each factor j Is a weight factor of each factor for adjusting the relative importance thereof, uf j Is the frequency of use, af, of each factor j Is the aging factor of each factor, i.e., the rate of performance change over time, j is the number of factors, j=1, 2 … … m, m represents the total number of factors, and m is a positive integer.
9. The three-dimensional model generation system of claim 8, wherein:
after the performance attenuation coefficient is obtained, comparing the performance attenuation coefficient with a performance attenuation threshold, and generating a trigger signal if the performance attenuation coefficient is greater than or equal to the performance attenuation threshold; otherwise, no trigger signal is generated;
the operation process of the dynamic and static switching unit comprises the following steps:
after the trigger signal is obtained, the first and second risk distinguishing thresholds are adjusted through the performance attenuation coefficient, and the calculation formula is as follows:
wherein (RT 1, RT 2) represents the original risk classification threshold of one, two, TZ [ RT (1), RT (2)]Representing the adjusted risk classification threshold value of one and two, PD and PD' are respectively the performance attenuation coefficient and the performance attenuation threshold value, and eta isAnd η is greater than 0;
and replacing the original risk classification threshold value I and the original risk classification threshold value II by the adjusted risk classification threshold value I and the adjusted risk classification threshold value II.
10. A three-dimensional model generation system according to claim 9, wherein:
after each step is executed, the manufactured three-dimensional model is sent to a model rendering module for rendering the three-dimensional model, and the three-dimensional model is output after rendering.
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