CN117687554B - Scale element flexible configuration system and method based on visual simulation scoring - Google Patents
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Abstract
The invention discloses a flexible scale element configuration system and a flexible scale element configuration method based on visual simulation scoring, which relate to the technical field of scale element configuration, wherein each scale element configuration is simulated and scored by using a visual simulation technology, the system comprises the steps of simulating the use of scale elements in a virtual environment, updating guide factors in a configuration set according to the score of each scale element configuration, thereby increasing the level of the guide factors related to the scale element configuration with higher score, searching according to the level of the guide factors and heuristic information, updating the scale element configuration, carrying out iterative processing, and outputting the currently optimal scale element configuration after reaching preset iterative times. According to the configuration system, each scale element configuration is subjected to simulation scoring based on a visual simulation technology, then the optimal scale element configuration is continuously and iteratively recommended automatically, the automatic recommendation enables the configuration process to be more efficient, and a user does not need to manually try a large number of combinations through the automatic recommendation of the optimal configuration, so that time and cost are saved.
Description
Technical Field
The invention relates to the technical field of scale element configuration, in particular to a scale element flexible configuration system and method based on visual simulation scoring.
Background
Scale element flexible configuration system is a software system for customizing and configuring scale elements, generally referred to as a measuring tool, for measuring length, angle or other physical quantities, which may be in various forms, such as scales, gauges, angle gauges, etc., as digitization technology has evolved, many measuring tools have become more intelligent, possibly in combination with digitization technology, allowing a user to configure on a digital interface and integrate with other digital tools.
The existing scale element configuration system generally only provides the configuration function of the scale elements for users, namely, the users manually generate a plurality of scale element configurations in the configuration system according to experience or expert knowledge and then select the scale element configurations for other users, and the configuration method has the following defects:
1. The user manually generates a plurality of scale element configurations in the configuration system according to experience or expert knowledge, and a great deal of time is required to be spent, so that the use cost of the configuration system is increased;
2. When other users need to acquire the scale element configuration from the configuration system, because a large number of scale element configurations are stored in the configuration system, if other users select to acquire the scale element configuration randomly, the scale element configuration effect acquired randomly may be poor or even cannot be used, and if other users select the scale element configuration by themselves, a large amount of time is required for other users to acquire, and time and cost are increased.
Disclosure of Invention
The invention aims to provide a scale element flexible configuration system and a scale element flexible configuration method based on visual simulation scoring, which are used for solving the defects in the background technology.
In order to achieve the above object, the present invention provides the following technical solutions: a flexible configuration method for scale elements based on visual simulation scoring, the configuration method comprising the steps of:
S1: randomly generating a set of initial scale element configurations, generating a configuration set based on the initial scale element configurations;
S2: simulating the use of scale elements in a virtual environment using visual simulation techniques, performing a simulation scoring on each scale element configuration;
s3: updating the guide factors in the configuration set according to the scores of each scale element configuration, and adjusting the level of the guide factors related to the scale element configuration;
S4: searching according to the guide factor level and heuristic information, updating the scale element configuration, introducing a local search mechanism, and mutating the updated scale element configuration in a search space;
S5: repeating the steps S2, S3 and S4 for iteration processing, and outputting the current optimal staff element configuration after the preset iteration times are reached;
S6: and evaluating the optimal scale element configuration, and sending an evaluation result to an administrator.
In a preferred embodiment, in step S2, the performing of the analogue scoring of each scale element configuration comprises the steps of:
S2.1: defining a virtual environment, and modeling the geometric shape, the size and the scale of the scale element configuration into the virtual environment by using a three-dimensional modeling tool or library;
S2.2: simulating physical characteristics of the scale element configuration, simulating an interaction process of a user and the scale element configuration, generating an image in a virtual environment by using a rendering technology, analyzing a visual effect of the scale element configuration in the image, and simulating experience of the user using a scale in the virtual environment;
S2.3: and according to various parameters of the scale element configuration observed in the simulation, performing performance grading on each scale element configuration, and outputting performance grading.
In a preferred embodiment, in step S3, adjusting the scale element configuration dependent guide factor level comprises the steps of:
S3.1: configuring each scale element, standardizing the score of each scale element into a relative value, and updating the guide factor of each scale element configuration;
S3.2: the level of the adjustment guide factor is configured for each scale element, and the expression is calculated as: dz New type ＝(1-ρ)*dz Old one +ρ PF; where ρ is a parameter for controlling the update rate, and the value range of ρ is [0,1], dz New type is the guide factor level after the scale element configuration update, dz Old one is the guide factor level before the scale element configuration update, and PF is the scale element configuration score.
In a preferred embodiment, in step S3.2, the relative value is obtained by dividing the score of each scale element configuration by the highest score of all scale element configurations;
normalized guide factor levels are obtained by dividing each guide factor level by the sum of all guide factor levels expressed as:
Where i=1, 2, 3,..n, n denotes the number of scale element configurations in the configuration set, dz i denotes the guide factor level of the i-th scale element configuration, dz New type denotes the guide factor level after the scale element configuration update, and dz Normalization denotes the guide factor level after normalization.
In a preferred embodiment, in step S5, outputting the currently optimal scale element configuration comprises the steps of:
s5.1: presetting iteration times, and repeatedly executing S2, S3 and S4 in each iteration;
S5.2: in each iteration, recording the current optimal scale element configuration according to the visual simulation score;
s5.3: comparing visual simulation scores of each round of iterative optimal scale element configuration, and taking the highest score scale element configuration as a global optimal scale element configuration;
s5.4: and outputting the globally optimal scale element configuration after the preset iteration times are reached.
In a preferred embodiment, in step S2.3, scoring the performance of each scale element configuration comprises the steps of:
S2.3.1: according to various parameters of the scale element configuration observed in the simulation, the various parameters comprise glossiness, freedom of movement, scale definition and error rate of the scale element configuration;
s2.3.2: after normalizing the glossiness, the movement freedom, the scale definition and the error rate, comprehensively calculating to obtain a configuration coefficient xs p;
S2.3.3: the larger the value of the configuration coefficient xs p, the higher the performance score of the scale element configuration;
S2.3.4: if the value of the configuration coefficient xs p of the scale element configuration is less than the quality threshold, the value of the configuration coefficient xs p is deleted from the configuration system.
In a preferred embodiment, in step S4: updating the scale element configuration and mutating the updated scale element configuration in the search space comprises the steps of:
s4.1: calculating heuristic information for each scale element configuration for guiding a search, selecting a scale element configuration in a search space using a steering factor;
S4.2: after the scale element configuration is selected, a local search mechanism is introduced to locally optimize the scale element configuration, and a local search operator is introduced to adjust the currently selected scale element configuration to improve the performance.
In a preferred embodiment, in step S4.1, selecting a scale element configuration in the search space using the steering factor comprises the steps of:
S4.1.1: initializing a local slope for each scale element configuration, calculating the local slope of each scale element configuration based on local slope rules;
s4.1.2: the local slope of the scale element configuration is standardized, and the combined score of each scale element configuration is calculated by combining the guide factors and the standardized local slope, wherein the expression is as follows: z Score of ＝(1-σ)*dz Normalization +σ JBX; wherein Z Score of is the composite score of the scale element configuration, sigma is a parameter for balancing the normalized guide factor level and the normalized local slope, the value is between 0 and 1, dz Normalization is the normalized guide factor level, and JBX is the normalized local slope;
S4.1.3: based on the composite score, a scale element configuration is selected in the search space.
In a preferred embodiment, in step S4.2, the introduction of a local search operator to adjust the currently selected scale element configuration to improve performance comprises the steps of:
s4.2.1: initializing a state of a local search operator for a currently selected scale element configuration;
S4.2.2: the local search operator is applied to adjust the currently selected scale element configuration, and the update rule of gradient descent is expressed as: Where pz New type is the adjusted new scale element configuration, pz currently, the method is that is the current scale element configuration,/> Configuring a gradient of parameters for an objective function relative to a current scale element, τ being a learning rate;
S4.2.3: evaluating the adjusted new scale element configuration by using an objective function, and comparing the performance of the adjusted new scale element configuration with the performance of the current scale element configuration;
S4.2.4: if the configuration performance of the new regulated scale element is better than that of the current scale element, the new regulated scale element is configured to be used as a follow-up;
S4.2.5: if the adjusted new scale element configuration performance is inferior to the current scale element configuration performance, the current scale element configuration is kept as the subsequent use.
The invention also provides a scale element flexible configuration system based on visual simulation scoring, which comprises a configuration generating module, a simulation scoring module, an element updating module, a configuration updating module, an iteration module and an evaluation module;
configuration generation module: randomly generating a set of initial scale element configurations, generating a configuration set based on the initial scale element configurations;
and (5) a simulation scoring module: simulating the use of scale elements in a virtual environment using visual simulation techniques, performing a simulation scoring on each scale element configuration;
Element updating module: updating the guide factors in the configuration set according to the score of each scale element configuration, adjusting the level of the guide factors related to the scale element configuration, introducing a local search mechanism, and mutating the updated scale element configuration in a search space;
And (3) a configuration updating module: searching according to the guide factor level and heuristic information, updating the scale element configuration, and introducing a local search mechanism to mutate the scale element configuration in a search space;
And (3) an iteration module: repeating the steps of the simulation scoring module, the element updating module and the configuration updating module to carry out iterative processing, and outputting the current optimal scale element configuration after the preset iteration times are reached;
and an evaluation module: and evaluating the optimal scale element configuration, and sending an evaluation result to an administrator.
In the technical scheme, the invention has the technical effects and advantages that:
The invention randomly generates a group of initial scale element configurations in a configuration system, carries out simulation scoring on each scale element configuration by using a visual simulation technology, comprises the steps of simulating the use of scale elements in a virtual environment, updating guide factors in a configuration set according to the score of each scale element configuration, thereby increasing the level of the guide factors related to the scale element configuration with higher score, searching according to the level of the guide factors and heuristic information, updating the scale element configuration, carrying out iteration processing, and outputting the currently optimal scale element configuration after reaching the preset iteration times. According to the configuration system, after the scale element configurations are randomly generated, each scale element configuration is subjected to simulation scoring based on a visual simulation technology, and then the optimal scale element configuration is continuously and iteratively and automatically recommended, so that the configuration process is more efficient, the optimal configuration is automatically recommended, a user does not need to manually try a large number of combinations, and time and cost are saved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments described in the present application, and other drawings may be obtained according to these drawings for a person having ordinary skill in the art.
FIG. 1 is a flow chart of the method of the present invention.
FIG. 2 is a block diagram of a system according to the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. 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: referring to fig. 1, the flexible configuration method for scale elements based on visual simulation scoring according to the present embodiment includes the following steps:
S1: randomly generating a set of initial scale element configurations, generating a configuration set based on the initial scale element configurations, specifically:
Determining parameters and variables of the scale element configuration, which may include the size, shape, material, scale type, etc. of the scale, defining a reasonable range for each configuration parameter, e.g. the length of the scale may vary within a certain range, the density of the scale may vary within another range, randomly generating a set of initial parameter values within the determined parameter range, which will create an initial scale element configuration;
Determining the size of a configuration set to be generated, which may be a predetermined fixed number or a number dynamically adjusted according to algorithm requirements, and outputting a final configuration set, wherein the final configuration set comprises a plurality of scale element configurations which are randomly generated;
To better illustrate how an initial set of scale element configurations may be randomly generated, examples are as follows:
Let us assume that we want to design a scale element, the parameters including the length, shape, material and scale type of the scale;
parameter and variable definitions:
Scale length L: varying over a range of [10,50] cm;
ruler shape S: may be a ruler, a curved ruler, etc., where represented by a binary variable, such as s= {0,1}, where 0 represents the ruler and 1 represents the curved ruler;
ruler material M: may be plastic, metal, etc., represented here by binary variables, such as m= {0,1}, where 0 represents plastic and 1 represents metal;
scale type T: may be a digital scale, a linear scale, etc., where a binary variable is represented, such as t= {0,1}, where 0 represents a digital scale and 1 represents a linear scale;
Parameter range definition:
l epsilon [10,50] cm;
S.epsilon.0, 1 (ruler, curved ruler);
M.epsilon.0, 1 (plastic, metal);
t ε {0,1} (numerical scale, linear scale);
Randomly generating initial parameter values:
Each parameter is randomly generated within its defined range, for example: randomly generating L, S, M, T values;
Configuration set size:
A fixed number is predetermined, for example, the selection of 100 different initial scale element configurations;
Outputting a final configuration set:
The output comprises a set of randomly generated arrangements of a plurality of scale elements. Each configuration consists of its parameter values forming a specific scale element, for example, to randomly generate one of the following initial configurations:
1) L=30 cm, s=0 (ruler), m=0 (plastic), t=1 (linear scale);
2) L=30 cm, s=0 (curved ruler), m=0 (metal), t=1 (numerical scale);
3) L=30 cm, s=0 (ruler), m=0 (plastic), t=1 (numerical scale);
Thus, by determining parameters, defining ranges, randomly generating initial parameter values, a configuration set can be created that contains a plurality of initial scale element configurations.
S2: each scale element configuration is simulated using visual simulation techniques, including simulating the use of scale elements in a virtual environment, and evaluating scale element configuration performance according to predetermined scoring criteria.
S3: according to the score of each scale element configuration, updating the guide factors in the configuration set, the scale element configuration with higher score will be more likely to be selected, thereby increasing the guide factor level associated with the scale element configuration with higher score, specifically:
For each scale element configuration, normalizing its score to a relative value, which can be achieved by dividing the score of each configuration by the highest score of all configurations, such that the relative score of each configuration will range between 0 and 1, updating the steering factor for each scale element configuration, the steering factor indicating a tendency to select a scale element configuration, a higher relative score should correspond to a higher steering factor in relation to the relative score, and for each scale element configuration increasing its level of steering factor, calculating the expression: dz New type ＝(1-ρ)*dz Old one +ρ PF; wherein ρ is a parameter for controlling the update rate, the value range of ρ is [0,1], dz New type is the guiding factor level after the scale element configuration update, dz Old one is the guiding factor level before the scale element configuration update, and PF is the score of the scale element configuration;
Ensuring that the sum of all guide factor levels is 1 so that they can be considered as a probability distribution can be achieved by dividing each guide factor level by their sum, expressed as:
Where i=1, 2, 3,..n, n represents the number of scale element configurations in the configuration set, dz i represents the guide factor level of the i-th scale element configuration, dz New type represents the guide factor level after the scale element configuration update, and dz Normalization represents the guide factor level after normalization;
for each scale element configuration, its score is normalized to a relative value, which can be achieved by dividing the score of each configuration by the highest score of all configurations, specifically:
For each scale element configuration, there is a corresponding score, which may be based on a plurality of metrics, such as visual effect, user experience, etc., traversing the scores of all scale element configurations, finding the highest score, which will be the basis for normalization, dividing its score by the highest score in all configurations, which will generate a relative value representing the relative performance of each configuration on the respective metrics, expressed as: relative score = score of configuration/highest score of all configurations, which ensures that the relative score is between 0 and 1, where 1 represents the highest score and 0 represents the lowest score, the same normalization process is performed for each scale element configuration to obtain the relative score, by comparing the relative scores, the best performing configuration can be identified, which helps to determine which configurations perform better on a particular metric, supporting decision making and improvement.
S4: searching is carried out according to the level of the guide factors and heuristic information, the configuration of the scale element is updated, a local search mechanism is introduced to increase the local optimization capacity of the configuration system, and the method comprises the steps of adopting a local search operator or carrying out a certain range of variation in a search space, and specifically comprises the following steps:
Heuristic information is computed for each scale element configuration, which may be additional information about a point in the configuration space, such as gradients, local slopes, etc., used to guide the search, with the scale element configuration being selected in the search space using the steering factors, the higher steering factor configurations being more likely to be selected, thereby increasing their impact in the search.
After the scale element configuration is selected, a local search mechanism is introduced to locally optimize the scale element configuration, local search operators, such as gradient descent, simulated annealing and the like, are introduced to finely tune the currently selected scale element configuration to try to improve performance, the local search operators are selected according to the nature of a specific problem, and the local search operators are ensured to work cooperatively with the global search.
Calculating heuristic information for each scale element configuration for guiding a search, selecting a scale element configuration in a search space using a steering factor, comprising the steps of:
Initializing a local slope for each scale element configuration, which may be zero, or some initial value according to prior knowledge of the problem and initial configuration settings, calculating the local slope for each scale element configuration based on local slope rules, normalizing the local slopes to ensure consistency in comparison between different local slopes, which may be normalized to a specific range to ensure that their weights are balanced when the guide factor is selected, calculating a composite score for each scale element configuration in combination with the guide factor and normalized local slope, expressed as: z Score of ＝(1-σ)*dz Normalization +σ JBX; wherein Z Score of is the composite score of the scale element configuration, σ is a parameter that balances the normalized guide factor level and the normalized local slope, the value is between 0 and 1, dz Normalization is the normalized guide factor level, JBX is the normalized local slope, and the scale element configuration with higher composite score is more likely to be selected according to the composite score in the search space;
calculating the local slope of each scale element configuration based on the local slope rule comprises the steps of:
Selecting e as the step size of the numerical difference, e=1×10 -6, for each scale element configuration, assuming the current scale element configuration is dz i, calculating the value of the objective function at the current configuration dz i, i.e. f (dz i), for each parameter of the current scale element configuration, generating a trimmed a scale element configuration by increasing or decreasing the step size e And B scale element configurationThe expression is:
wherein p j represents a unit vector in the j-th parameter direction, and each of the target functions is calculated in the A scale element arrangement/> And B Scale element configuration/>Values of/>, i.e.)And/>For the j-th parameter, calculating the local slope through numerical difference, wherein the expression is as follows: /(I)Representing the change rate of the objective function relative to the j-th parameter, namely the local slope, repeating the steps for each scale element configuration, and calculating the local slope of each scale element configuration;
calculating the value of the objective function at the current configuration dz i, comprising the steps of:
Determining a specific target or objective function to be optimized, which may be a complex function, covering various aspects of the scale element configuration, such as visual effect, user experience, scale accuracy, etc., inputting the parameter values of the currently selected scale element configuration into the objective function, including parameters of scale size, shape, texture, etc., depending on the definition of the objective function, performing simulation or calculation: depending on the nature of the objective function, a corresponding simulation, calculation or rendering operation is performed, which may include simulating scale usage, calculating image quality, evaluating scale accuracy, etc. in a virtual environment, resulting in an objective function value: based on the results of the simulation or calculation, the value of the objective function at the current configuration is calculated, which is a quantized representation of the objective function, showing the merits of the objective under the current configuration, and for a better illustration of this scheme we exemplify the following:
Assuming that our goal is to design a virtual scale element in which the objective function covers both visual effects and user experience, we want to minimize the ambiguity of the scale on the scale and maximize the user's satisfaction with the scale, the goals of both aspects can be expressed by the following calculation formula:
setting Clarity as the definition of the scale, wherein the value range is between 0 and 1, 0 represents blurring, 1 represents very clear, and the aim is to maximize definition;
satisfaction is set as satisfaction of the user, and the value range is between 0 and 1, wherein 0 represents dissatisfaction, 1 represents very satisfaction, and the aim is to maximize the satisfaction of the user;
To comprehensively consider visual effects and user experiences, an objective function is defined, where a 1 and a 2 are weights used to adjust the importance of two objects, expressed as: mb z＝a1*Clarity+a2 x Satisfaction, and a 1+a2 =1.
After the scale element configuration is selected, introducing a local search mechanism to locally optimize the scale element configuration, and introducing a local search operator to adjust and improve the performance of the currently selected scale element configuration, wherein the method comprises the following steps of:
For the currently selected scale element configuration, the state of the local search operator is initialized. This may involve setting parameters such as initial step size, number of iterations of gradient descent, etc., applying a local search operator, e.g. gradient descent, fine tuning the currently selected scale element configuration, the update rule of gradient descent may be expressed as: Where pz New type is the adjusted new scale element configuration, pz currently, the method is that is the current scale element configuration,/> For the gradient of the objective function relative to the current scale element configuration parameters, τ is a learning rate, the objective function is used for evaluating the adjusted new scale element configuration, the performance of the adjusted new scale element configuration is compared with the performance of the current scale element configuration, if the performance of the adjusted new scale element configuration is better than the performance of the current scale element configuration, the adjusted new scale element configuration is used as a follow-up, and if the performance of the adjusted new scale element configuration is worse than the performance of the current scale element configuration, the current scale element configuration is kept as the follow-up;
for the currently selected scale element configuration, the state of the local search operator is initialized. This may involve setting parameters such as initial step size, iteration number of gradient descent, etc., specifically:
An appropriate initial step value is selected, usually according to the characteristics of the problem and the scale of the search space, an empirical value or a problem-based property is used for selection, if an optimization algorithm such as gradient descent is used, the learning rate is an important component of the step, the selection of the initial learning rate may need to be adjusted in the process of optimizing the algorithm, an upper limit of the iteration number of the local search operator is set to avoid infinite loops, the upper limit may be a fixed constant, or adaptive adjustment according to the property of the problem is performed, the introduction of an early stop mechanism is considered, that is, the iteration is ended in advance when a certain condition is met, for example when the objective function converges or changes are small,
Determining when a local search is deemed to have converged may involve an indication of the change in objective function value, gradient magnitude, etc., selecting appropriate convergence conditions based on the nature of the particular problem and algorithm, dynamically adjusting step size or other parameters to accommodate changes in the search process if possible, taking into account the use of adaptive methods, determining the initial point of starting the search, which may be the most optimal point of the previous round of search or other point selected based on the nature of the problem, in actual problems, the selection of parameters may require finding the best combination by experimentation and iterative tuning, cross-validation, grid search, etc., techniques may be used to record the selected parameters and state information for subsequent analysis and tuning prior to performing the local search.
The new scale element configuration after adjustment is evaluated using an objective function, in particular:
Determining a specific target or objective function to be optimized may include minimizing errors, maximizing sharpness, maximizing scale accuracy, etc., depending on the design goals of the scale elements, applying the new scale element configuration to the defined objective function, which may involve steps of simulating user use, rendering images, optical simulation, etc., depending on the nature of the objective function, calculating the value of the objective function at the new configuration, resulting in a digitized evaluation result,
Recording new scale element configurations and calculated objective function values, which may be used in subsequent analyses and comparisons, comparing the newly configured objective function values with previous configurations to learn the direction of improvement or change, which helps to determine whether the design objective has been met, feeding back the objective function values to an optimization algorithm so that the algorithm may make further adjustments based on this evaluation result, which may be part of a gradient descent algorithm, etc., if the optimization algorithm allows, the optimization process may be iteratively performed, the scale element configurations may be continually adjusted until satisfactory performance is achieved, if there are multiple objectives or multiple performance indicators, they may be comprehensively considered to form a multi-objective function, further improving the integrity of the evaluation, flexibly adjusting the weights, forms, etc. of the objective functions according to specific problems and requirements to better reflect the design objective.
S5: repeating the steps S2, S3 and S4 for iteration processing, and outputting the current optimal staff element configuration after the preset iteration times are reached, wherein the staff element configuration specifically comprises the following steps:
The iteration number is preset, which is a main control parameter, in each iteration, the steps described before are executed, including evaluation, updating of a guide factor, searching, introduction of a local search mechanism and the like, in each iteration, the current optimal scale element configuration, usually the scale element configuration with the highest score, is recorded according to the visual simulation score, the visual simulation scores of the optimal scale element configurations in each iteration are compared, the highest scoring scale element configuration is used as the global optimal scale element configuration, and after the preset iteration number is reached, the global optimal scale element configuration is output.
S6: and evaluating the finally obtained scale element configuration, including indexes of matching degree with requirements, performance evaluation and the like, and sending an evaluation result to an administrator.
The application randomly generates a group of initial scale element configurations in a configuration system, carries out simulation scoring on each scale element configuration by using a visual simulation technology, comprises the steps of simulating the use of scale elements in a virtual environment, updating guide factors in a configuration set according to the score of each scale element configuration, thereby increasing the level of the guide factors related to the scale element configuration with higher score, searching according to the level of the guide factors and heuristic information, updating the scale element configuration, carrying out iteration processing, and outputting the currently optimal scale element configuration after reaching the preset iteration times. According to the configuration system, after the scale element configurations are randomly generated, each scale element configuration is subjected to simulation scoring based on a visual simulation technology, and then the optimal scale element configuration is continuously and iteratively and automatically recommended, so that the configuration process is more efficient, the optimal configuration is automatically recommended, a user does not need to manually try a large number of combinations, and time and cost are saved.
Example 2: simulating the use of scale elements in a virtual environment by using a visual simulation technology, and performing simulation scoring on each scale element configuration, wherein the simulation scoring is specifically as follows:
Defining a virtual environment including scenes, illumination, backgrounds and the like, ensuring that an environmental model is real enough to reflect an actual use situation, modeling geometric shapes, sizes, scales and the like of scale element configurations into the virtual environment by using a three-dimensional modeling tool or library, ensuring that the models of the scale element configurations conform to the actual configurations, simulating physical characteristics of the scale element configurations including optical characteristics, reflection, refraction and the like of materials, which are helpful for more realistically presenting the appearance of the scales in the virtual environment, simulating interaction processes of a user and the scale element configurations, which can include the user observing the scales, moving the scales, using the scales and the like, ensuring that user operations in the virtual environment reflect the actual use situation, generating images in the virtual environment by using a rendering technology, evaluating visual effects of the scale element configurations in the images, including definition, color accuracy, definition of the scales and the like, simulating the experience of the scales in the virtual environment by the user, capturing possible problems or advantages of the user experience factors, such as usability, readability and the like of the scale element configurations, taking into consideration of the user experience factors, and outputting performance of each element configuration according to various parameters of the scale element configurations observed in the simulation, and guiding the scale element configurations to obtain a score and a score by selectively scoring the scale element configuration;
Performance scoring of each scale element configuration according to parameters of the scale element configuration observed in the simulation comprises the steps of:
according to various parameters of the scale element configuration observed in the simulation, the various parameters comprise glossiness, freedom of movement, scale definition and error rate of the scale element configuration;
After normalizing the glossiness, the movement freedom, the scale definition and the error rate, comprehensively calculating and obtaining a configuration coefficient xs p, wherein the calculation expression is as follows: Wherein GZ is glossiness, YZ is movement freedom, KQ is scale definition, WC is error rate, alpha, beta, gamma and delta are respectively glossiness, movement freedom, scale definition and ratio coefficient of error rate, and alpha, beta, gamma and delta are all more than 0;
As can be seen from the calculation expression of the configuration coefficient xs p, the larger the value of the configuration coefficient xs p is, the higher the performance score of the configuration of the scale element is, in order to reduce the calculation load of the configuration system, a quality threshold is preset, and if the value of the configuration coefficient xs p configured by the scale element is smaller than the quality threshold, the value of the configuration coefficient xs p is deleted from the configuration system, so that the data processing capacity of the configuration system is reduced;
1) Gloss (Glossiness):
numerical range: between 0 and 1;
the good effect is that: the high glossiness can make the surface look smoother, and the reflection effect is improved;
bad effects: low gloss may cause the surface to appear rough and not smooth enough;
The acquisition mode is as follows: using the optical material property settings provided in the rendering engine or graphics library;
The method comprises the following specific steps:
setting a glossiness parameter in a material property of the scale element;
Rendering the scene using a rendering engine, generating an image;
Acquiring glossiness information of a scale element in an image through an interface provided by an image analysis tool or a rendering engine;
2) Freedom of Movement (free-of-Movement):
the numerical value is as follows: the degree of freedom of the user moving the scale in the virtual environment;
The good effect is that: the greater freedom of movement enables the user to more flexibly adjust and use the scale;
bad effects: the limited movement may cause a user to have difficulty in acquiring a desired viewing angle or position, reducing convenience of use;
the acquisition mode is as follows: simulating the operation of a user in a virtual environment and recording the freedom of movement;
The method comprises the following specific steps:
Placing a scale element in a virtual environment, and starting user interaction simulation;
recording a user's movement operations on the scale element, including translation and rotation;
Analyzing the recorded operation data, calculating a degree of freedom of movement, for example, a distance of translation, an angle of rotation, and the like;
3) Scale-definition (Scale-Clarity):
The numerical value is as follows: definition of the scale;
the good effect is that: the scale with high definition enables a user to accurately read the numerical value of the scale;
bad effects: a blurred scale may cause erroneous readings or annoyance to the user;
the acquisition mode is as follows: simulating rendering of the scale element in the virtual environment, and analyzing definition of scales in the image;
The method comprises the following specific steps:
setting a model and materials of a scale element, including fineness of scale marks;
Rendering a virtual scene to generate an image;
Evaluating the sharpness of the tick marks using an image analysis tool or image processing algorithm, which may take into account the use of image processing techniques to detect edges, calculate contrast, etc.;
4) Error Rate (Error-Rate):
the numerical value is as follows: the proportion of misreading or misoperation when the user uses the scale;
The good effect is that: a low error rate means that the user rarely makes mistakes, and the design of the scale is easy to understand;
Bad effects: a high error rate may mean that there are confusion points or non-intuitive points in the design of the scale elements;
The acquisition mode is as follows: simulating the use of the scale by a user in a virtual environment, and recording an error between the reading of the user and the actual numerical value;
The method comprises the following specific steps:
Defining a measurement standard of the staff gauge, wherein the measurement standard comprises scales and numbers;
Simulating the operation of measuring or reading the scale by a user;
the difference between the user's reading and the actual value is recorded and the error rate is calculated.
Evaluating the finally obtained scale element configuration, including indexes of matching degree with requirements, performance evaluation and the like, and sending an evaluation result to an administrator, wherein the method comprises the following steps of:
Reviewing design and demand documents, determining the design requirements and specifications of scale elements, comparing the finally obtained scale element configuration and design requirements, evaluating the matching degree between the scale elements, recording the matching degree evaluation result, and using quantitative indexes or descriptive evaluation;
defining performance indexes, which may include definition, color accuracy, scale definition and the like, performing simulation scoring on the scale element in a virtual environment to obtain numerical values or qualitative evaluation of the performance indexes, and analyzing performance evaluation results to ensure that the scale element achieves expectations in terms of visual effects and user experience;
Performing virtual user experience test, simulating a scene of a user using a scale in a virtual environment, recording key indexes such as user operation, interaction time, error rate and the like, collecting user feedback and comments, knowing the experience of the user on the use process of the scale element, and evaluating the usability of the scale element in actual use and user satisfaction degree by combining user experience data;
Safety problems possibly related to the scale element in the use process, such as material selection, edge design and the like, are considered for safety evaluation, so that the scale element is ensured not to cause danger under normal use conditions, and the stability and durability of the scale element are considered, so that the scale element is not easy to damage in long-time use;
Summarizing all the evaluation results and data, sorting the evaluation results and the data into an evaluation report, clearly presenting the results of each evaluation aspect in the report, including numerical values, charts, user feedback and the like, providing overall evaluation of the evaluation results, and indicating advantages and improvement spaces of scale element configuration;
The assessment report is generated in the form of an electronic document, the report is sent to an administrator or related stakeholder, the delivery mode of the report is ensured to conform to the communication specification inside the organization, and necessary explanation, suggestion or further improvement plan can be attached when the report is sent.
Example 3: referring to fig. 2, the scale element flexible configuration system based on visual simulation scoring according to the present embodiment includes a configuration generating module, a simulation scoring module, an element updating module, a configuration updating module, an iteration module, and an evaluation module:
Configuration generation module: generating a group of initial scale element configurations at random, generating a configuration set based on the initial scale element configurations, transmitting the initial scale element configurations to the simulation scoring module, and transmitting the configuration set to the element updating module;
And (5) a simulation scoring module: performing simulation scoring on each scale element configuration by using a visual simulation technology, including simulating the use of scale elements in a virtual environment, evaluating the scale element configuration performance according to a preset scoring standard, and transmitting a scoring result to an element updating module;
Element updating module: updating the guide factors in the configuration set according to the score of each scale element configuration, and introducing a local search mechanism to increase the local optimization capacity of the configuration system, wherein the local search operator is adopted or a certain range of variation is carried out in a search space, the scale element configuration with higher score is more likely to be selected, so that the guide factor level related to the scale element configuration with higher score is increased, and the guide factor level of the scale element configuration is sent to a configuration updating module;
and (3) a configuration updating module: searching according to the level of the guide factors and heuristic information, updating the configuration of the scale element, and introducing a local searching mechanism to increase the local optimizing capability of the configuration system, wherein the method comprises the steps of adopting a local searching operator or carrying out variation in a certain range in a searching space;
And (3) an iteration module: repeating the steps of the simulation scoring module, the element updating module and the configuration updating module to carry out iterative processing, outputting the current optimal scale element configuration after the preset iteration times are reached, and sending the optimal scale element configuration to the evaluation module;
And an evaluation module: and evaluating the optimal scale element configuration, including indexes of matching degree with requirements, performance evaluation and the like, and sending an evaluation result to an administrator.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.
It should be understood that the term "and/or" is merely an association relationship describing the associated object, and means that three relationships may exist, for example, a and/or B may mean: there are three cases, a alone, a and B together, and B alone, wherein a, B may be singular or plural. In addition, the character "/" herein generally indicates that the associated object is an "or" relationship, but may also indicate an "and/or" relationship, and may be understood by referring to the context.
It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.
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. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application. It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (2)
1. The flexible configuration method of the scale element based on the visual simulation scoring is characterized by comprising the following steps of: the configuration method comprises the following steps:
S1: randomly generating a set of initial scale element configurations, generating a configuration set based on the initial scale element configurations;
S2: simulating the use of scale elements in a virtual environment using visual simulation techniques, performing a simulation scoring on each scale element configuration;
s3: updating the guide factors in the configuration set according to the scores of each scale element configuration, and adjusting the level of the guide factors related to the scale element configuration;
S4: searching according to the guide factor level and heuristic information, updating the scale element configuration, introducing a local search mechanism, and mutating the updated scale element configuration in a search space;
S5: repeating the steps S2, S3 and S4 for iteration processing, and outputting the current optimal staff element configuration after the preset iteration times are reached;
s6: evaluating the optimal scale element configuration and sending an evaluation result to an administrator;
In step S2, performing analog scoring on each scale element configuration includes the steps of:
S2.1: defining a virtual environment, and modeling the geometric shape, the size and the scale of the scale element configuration into the virtual environment by using a three-dimensional modeling tool or library;
S2.2: simulating physical characteristics of the scale element configuration, simulating an interaction process of a user and the scale element configuration, generating an image in a virtual environment by using a rendering technology, analyzing a visual effect of the scale element configuration in the image, and simulating experience of the user using a scale in the virtual environment;
S2.3: according to various parameters of scale element configuration observed in simulation, performance grading is carried out on each scale element configuration, and then performance grading is output;
In step S3, adjusting the scale element configuration dependent guide factor level comprises the steps of:
S3.1: configuring each scale element, standardizing the score of each scale element into a relative value, and updating the guide factor of each scale element configuration;
s3.2: the level of the adjustment guide factor is configured for each scale element, and the expression is calculated as: ; in the/> Is a parameter controlling the update rate, and/>The value range is，/>Configuring updated guide factor levels for scale elements,/>Configuring scale elements with pre-update guide factor levels,/>Scoring configured for the scale element;
in step S3.1, the relative value is obtained by dividing the score of each scale element configuration by the highest score of all scale element configurations;
normalized guide factor levels are obtained by dividing each guide factor level by the sum of all guide factor levels expressed as:
in the above, the ratio of/> ，/>Representing the number of scale element configurations in a configuration set,/>Guide factor level representing the i-th scale element configuration,/>Configuring updated guide factor levels for scale elements,/>Is the normalized guide factor level;
In step S5, outputting the currently optimal scale element configuration comprises the steps of:
s5.1: presetting iteration times, and repeatedly executing S2, S3 and S4 in each iteration;
S5.2: in each iteration, recording the current optimal scale element configuration according to the visual simulation score;
s5.3: comparing visual simulation scores of each round of iterative optimal scale element configuration, and taking the highest score scale element configuration as a global optimal scale element configuration;
S5.4: outputting the globally optimal scale element configuration after the preset iteration times are reached;
in step S2.3, scoring the performance of each scale element configuration comprises the steps of:
S2.3.1: according to various parameters of the scale element configuration observed in the simulation, the various parameters comprise glossiness, freedom of movement, scale definition and error rate of the scale element configuration;
s2.3.2: normalizing the glossiness, the freedom of movement, the scale definition and the error rate, and comprehensively calculating to obtain configuration coefficients ；
S2.3.3: configuration coefficientThe larger the value, the higher the performance score of the scale element configuration;
s2.3.4: if the scale element is configured with the configuration coefficient The value < quality threshold value, the configuration coefficient/>Deleting the value from the configuration system;
in step S4: updating the scale element configuration and mutating the updated scale element configuration in the search space comprises the steps of:
s4.1: calculating heuristic information for each scale element configuration for guiding a search, selecting a scale element configuration in a search space using a steering factor;
S4.2: after the scale element configuration is selected, introducing a local search mechanism to locally optimize the scale element configuration, and introducing a local search operator to adjust and improve the performance of the currently selected scale element configuration;
in step S4.1, selecting a scale element configuration in the search space using the steering factor comprises the steps of:
S4.1.1: initializing a local slope for each scale element configuration, calculating the local slope of each scale element configuration based on local slope rules;
S4.1.2: the local slope of the scale element configuration is standardized, and the combined score of each scale element configuration is calculated by combining the guide factors and the standardized local slope, wherein the expression is as follows: ; in the/> Complex score assigned to scale element,/>To balance the normalized level of the steering factor with the normalized local slope parameter, a value between 0 and 1 is taken,/>For normalized director level,/>Is the local slope after normalization;
S4.1.3: selecting a scale element configuration in the search space based on the composite score;
In step S4.2, the introduction of a local search operator to adjust the currently selected scale element configuration to improve performance comprises the steps of:
s4.2.1: initializing a state of a local search operator for a currently selected scale element configuration;
S4.2.2: the local search operator is applied to adjust the currently selected scale element configuration, and the update rule of gradient descent is expressed as: in the above, the ratio of/> For the new scale element configuration after adjustment,/>For the current scale element configuration,/>Configuring a gradient of parameters for an objective function relative to a current scale element,/>Is the learning rate;
S4.2.3: evaluating the adjusted new scale element configuration by using an objective function, and comparing the performance of the adjusted new scale element configuration with the performance of the current scale element configuration;
S4.2.4: if the configuration performance of the new regulated scale element is better than that of the current scale element, the new regulated scale element is configured to be used as a follow-up;
S4.2.5: if the adjusted new scale element configuration performance is inferior to the current scale element configuration performance, the current scale element configuration is kept as the subsequent use.
2. A scale element flexible configuration system based on visual simulation scoring for implementing the configuration method of claim 1, characterized in that: the system comprises a configuration generation module, a simulation scoring module, an element updating module, a configuration updating module, an iteration module and an evaluation module;
configuration generation module: randomly generating a set of initial scale element configurations, generating a configuration set based on the initial scale element configurations;
and (5) a simulation scoring module: simulating the use of scale elements in a virtual environment using visual simulation techniques, performing a simulation scoring on each scale element configuration;
Element updating module: updating the guide factors in the configuration set according to the score of each scale element configuration, adjusting the level of the guide factors related to the scale element configuration, introducing a local search mechanism, and mutating the updated scale element configuration in a search space;
And (3) a configuration updating module: searching according to the guide factor level and heuristic information, updating the scale element configuration, and introducing a local search mechanism to mutate the scale element configuration in a search space;
And (3) an iteration module: repeating the steps of the simulation scoring module, the element updating module and the configuration updating module to carry out iterative processing, and outputting the current optimal scale element configuration after the preset iteration times are reached;
and an evaluation module: and evaluating the optimal scale element configuration, and sending an evaluation result to an administrator.
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