Zero-dependency CSS architecture generation method and system and storage medium

Abstract

The invention relates to a zero-dependence CSS architecture generation method and system and a storage medium, and the method comprises the steps: L1, collecting the data information of an automatic driving project development framework, constructing a data node matrix of the automatic driving project development framework, and obtaining the data information of the data node matrix of the automatic driving project development framework; and L2, on the basis of the data information of the matrix of the data nodes of the automatic driving project development framework, constructing a CSS atom class generation model of a multilayer neural network based on attenuation factors, and representing the generation of CSS atom classes to obtain the data information of the matrix of the CSS atom classes of the automatic driving project development framework. According to the method, the problems that the dependency relationship between the projects becomes complex and the maintenance difficulty is increased are solved, the project development efficiency is improved, and the operation cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of generating a CSS architecture, and in particular to a method, system and storage medium for generating a zero-dependency CSS architecture. Background Art

[0002] Although Tailwind CSS has gained wide popularity in modern front-end development, it also has some disadvantages. First, beginners may find the use of a large number of utility classes in HTML cluttered. Compared with the traditional CSS method, Tailwind's class names are clear in function, but too many class combinations may make the markup complicated and affect readability.

[0003] Secondly, since Tailwind emphasizes the use of utility classes, developers may face the problem of style duplication in their projects. Without clear planning, multiple developers may create similar classes, resulting in code redundancy. In addition, although custom classes can be created through directives, this may confuse some developers because it goes against the core concept of Tailwind.

[0004] The learning curve of Tailwind CSS can also be steep, especially for developers who are accustomed to traditional CSS and frameworks. It takes time and effort to understand Tailwind's configuration, theme customization, and responsive design methods. In addition, for large projects, the initial setup and configuration may seem cumbersome.

[0005] In terms of performance, although Tailwind supports on-demand CSS generation, the generated files may still be large in the early stages of development, especially if they are not optimized. If PurgeCSS or corresponding tools are not used properly, the final CSS file may cause longer loading time.

[0006] Finally, although Tailwind has a powerful plugin system, this may also make the framework ecosystem complicated. Developers need to constantly update and manage these plugins to ensure compatibility with the main framework. In addition, the use of plugins may complicate the dependencies between projects, increasing the difficulty of maintenance.

[0007] In summary, Tailwind CSS faces certain challenges in readability, learning curve, style duplication, performance, and ecosystem management. Summary of the invention

[0008] In view of the above problems, the present invention provides a method, system and storage medium for generating a zero-dependency CSS architecture, which not only solves the problem that the dependency relationship between projects becomes complicated and the maintenance difficulty is increased, but also improves the development efficiency of the project and reduces the operating cost.

[0009] In order to achieve the above-mentioned purpose and other related purposes, the technical solution provided by the present invention is as follows:

[0010] A method for generating a zero-dependency CSS architecture, the method comprising:

[0011] L1. Collect data information of the autonomous driving project development framework, construct a data node matrix of the autonomous driving project development framework, and obtain data information of the matrix of data nodes of the autonomous driving project development framework;

[0012] L2. Based on the data information of the matrix of the data nodes of the autonomous driving project development framework, a CSS atomic class generation model of a multi-layer neural network based on an attenuation factor is constructed to characterize the generation of the CSS atomic class, and obtain the data information of the matrix of the CSS atomic class of the autonomous driving project development framework;

[0013] L3. Based on the data information of the matrix of the CSS atomic class of the autonomous driving project development framework, the matrix of the CSS atomic class is optimized using an Archimedean optimization algorithm based on a dynamic learning factor to obtain the data information of the matrix of the CSS atomic class of the autonomous driving project development framework after optimization;

[0014] L4. Based on the data information of the matrix of the CSS atomic class of the optimized autonomous driving project development framework, a generation model of the CSS architecture is constructed, the generated files of the CSS architecture are characterized, and the data information of the generated files of the CSS architecture is obtained.

[0015] Furthermore, in step L2, the step of constructing a CSS atomic class generation model based on a multi-layer neural network of an attenuation factor to characterize the generation of CSS atomic classes includes:

[0016] L21. Based on the data information of the matrix of the data nodes of the autonomous driving project development framework, a feature extraction function Q of the autonomous driving project development framework is established.

[0017]

[0018] Among them, x is the data information of the matrix of the data nodes of the autonomous driving project development framework, α1, α2 and α3 are the feature extraction factors of the autonomous driving project development framework, and feature extraction is performed on the data of the autonomous driving project development framework to obtain the data information of the feature matrix of the data nodes of the autonomous driving project development framework;

[0019] L22. Input the data information of the feature matrix of the data node of the autonomous driving project development framework into the CSS atomic class generation model of the multi-layer neural network based on the attenuation factor for training and learning, and determine the CSS atomic class generation function W of the neural network,

[0020]

[0021] Among them, y is the data information of the feature matrix of the data node of the autonomous driving project development framework, β1, β2 and β3 are the attenuation factors for the generation of CSS atomic classes, and the trained CSS atomic class generation model based on the attenuation factor multi-layer neural network is obtained;

[0022] L23. Based on the trained CSS atomic class generation model based on the attenuation factor-based multi-layer neural network, the data information of the matrix of the data nodes of the autonomous driving project development framework is input, the generation of the CSS atomic class is characterized, and the data information of the matrix of the CSS atomic class of the autonomous driving project development framework is obtained.

[0023] Furthermore, the attenuation factors β1, β2 and β3 generated by the CSS atomic class are,

[0024]

[0025] Among them, y is the data information of the feature matrix of the data node of the autonomous driving project development framework.

[0026] Furthermore, the constraint function f of the feature extraction factors α1, α2 and α3 of the autonomous driving project development framework is,

[0027]

[0028] The value range of the constraint function f is (0,1).

[0029] Furthermore, in step L3, the optimization of the matrix of the CSS atomic class using the Archimedean optimization algorithm based on the dynamic learning factor includes:

[0030] L31. Based on the data information of the matrix of the CSS atomic class of the autonomous driving project development framework, the population is initialized, the population parameters are determined, and the data information of the initialized population is obtained;

[0031] L32. Based on the data information of the initialized population, establish the fitness function R of the population individuals,

[0032]

[0033] Among them, z is the data information of the initialized population, δ1, δ2 and δ3 are the fitness adjustment factors of the population individuals, and the fitness values ​​of the population individuals are calculated to obtain the data information of the fitness values ​​of the population individuals;

[0034] L33. Based on the data information of the fitness values ​​of the individuals in the population, establish the optimization function P of the population,

[0035]

[0036] Among them, r is the data information of the fitness value of the population individual, γ1, γ2 and γ3 are the dynamic learning factors of the population individual, and the matrix of the CSS atomic class is optimized to obtain the data information of the matrix of the CSS atomic class of the optimized autonomous driving project development framework.

[0037] Furthermore, the constraints of the dynamic learning factors γ1, γ2 and γ3 of the individuals in the population are:

[0038]

[0039] Furthermore, in step L4, the building of the generation model of the CSS architecture and characterizing the generated files of the CSS architecture include:

[0040] L41. Based on the data information of the matrix of the CSS atomic class of the optimized autonomous driving project development framework, a regularization function S of the CSS atomic class of the autonomous driving project development framework is established.

[0041]

[0042] Among them, h is the data information of the matrix of the CSS atomic class of the autonomous driving project development framework after optimization, η1, η2 and η3 are the regularization parameters of the autonomous driving project development framework, and the matrix of the CSS atomic class is regularized to obtain the data information of the regularized matrix of the CSS atomic class of the autonomous driving project development framework;

[0043] L42. Based on the data information of the regularization matrix of the CSS atomic class of the autonomous driving project development framework, a generation function F of the CSS architecture is established.

[0044]

[0045] Among them, q is the data information of the regularization matrix of the CSS atomic class of the autonomous driving project development framework, and λ1, λ2, and λ3 are the model parameters generated by the CSS architecture;

[0046] L43. Based on the generation function F of the CSS architecture, the generated file of the CSS architecture is characterized to obtain data information of the generated file of the CSS architecture.

[0047] Furthermore, the constraint function g of the regularization parameters η1, η2 and η3 of the autonomous driving project development framework is,

[0048]

[0049] Among them, the value range of the constraint function g is (2,3).

[0050] In order to achieve the above-mentioned object and other related objects, the present invention also provides a system for generating a zero-dependency CSS architecture, including a computer device, which is programmed or configured to execute the steps of any one of the methods for generating a zero-dependency CSS architecture.

[0051] In order to achieve the above-mentioned object and other related objects, the present invention also provides a computer-readable storage medium, on which is stored a computer program programmed or configured to execute any one of the methods for generating a zero-dependency CSS architecture.

[0052] The present invention has the following positive effects:

[0053] 1. The present invention constructs a CSS atomic class generation model based on a multi-layer neural network with an attenuation factor to characterize the generation of CSS atomic classes, and optimizes the matrix of CSS atomic classes by combining an Archimedean optimization algorithm based on a dynamic learning factor. This not only solves the problem that the dependency relationship between projects becomes complicated and the maintenance difficulty is increased, but also improves the development efficiency of the project and reduces the operating cost.

[0054] 2. The present invention constructs a generation model of the CSS architecture and characterizes the generated files of the CSS architecture. It can not only generate adaptive CSS architectures according to the architectures of different development projects to carry out project research and development and improve project development efficiency, but also generate smaller CSS files, increase loading speed, and reduce the learning curve, so that developers can get started more quickly. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a schematic diagram of the method flow of the present invention;

[0056] Figure 2 A schematic diagram of a process for constructing a CSS atomic class generation model based on a multi-layer neural network of attenuation factors according to the present invention;

[0057] Figure 3 It is a flow chart of the Archimedean optimization algorithm based on dynamic learning factor of the present invention;

[0058] Figure 4 A schematic diagram of the process of constructing a generation model of a CSS architecture according to the present invention. DETAILED DESCRIPTION

[0059] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0060] Example 1: Figure 1 As shown, a method for generating a zero-dependency CSS architecture includes:

[0061] L1. Collect data information of the autonomous driving project development framework, construct a data node matrix of the autonomous driving project development framework, and obtain data information of the matrix of data nodes of the autonomous driving project development framework;

[0062] L2. Based on the data information of the matrix of the data nodes of the autonomous driving project development framework, a CSS atomic class generation model of a multi-layer neural network based on an attenuation factor is constructed to characterize the generation of the CSS atomic class, and obtain the data information of the matrix of the CSS atomic class of the autonomous driving project development framework;

[0063] L3. Based on the data information of the matrix of the CSS atomic class of the autonomous driving project development framework, the matrix of the CSS atomic class is optimized using an Archimedean optimization algorithm based on a dynamic learning factor to obtain the data information of the matrix of the CSS atomic class of the autonomous driving project development framework after optimization;

[0064] L4. Based on the data information of the matrix of the CSS atomic class of the optimized autonomous driving project development framework, a generation model of the CSS architecture is constructed, the generated files of the CSS architecture are characterized, and the data information of the generated files of the CSS architecture is obtained.

[0065] In this embodiment, if Figure 2 As shown, in step L2, the construction of a CSS atomic class generation model based on a multi-layer neural network of an attenuation factor to characterize the generation of CSS atomic classes includes:

[0066] L21. Based on the data information of the matrix of the data nodes of the autonomous driving project development framework, a feature extraction function Q of the autonomous driving project development framework is established.

[0067]

[0068] Among them, x is the data information of the matrix of the data nodes of the autonomous driving project development framework, α1, α2 and α3 are the feature extraction factors of the autonomous driving project development framework, and feature extraction is performed on the data of the autonomous driving project development framework to obtain the data information of the feature matrix of the data nodes of the autonomous driving project development framework;

[0069] L22. Input the data information of the feature matrix of the data node of the autonomous driving project development framework into the CSS atomic class generation model of the multi-layer neural network based on the attenuation factor for training and learning, and determine the CSS atomic class generation function W of the neural network,

[0070]

[0071] Among them, y is the data information of the feature matrix of the data node of the autonomous driving project development framework, β1, β2 and β3 are the attenuation factors for the generation of CSS atomic classes, and the trained CSS atomic class generation model based on the attenuation factor multi-layer neural network is obtained;

[0072] L23. Based on the trained CSS atomic class generation model based on the attenuation factor-based multi-layer neural network, the data information of the matrix of the data nodes of the autonomous driving project development framework is input, the generation of the CSS atomic class is characterized, and the data information of the matrix of the CSS atomic class of the autonomous driving project development framework is obtained.

[0073] In this embodiment, the attenuation factors β1, β2 and β3 generated by the CSS atom class are,

[0074]

[0075] Among them, y is the data information of the feature matrix of the data node of the autonomous driving project development framework.

[0076] In this embodiment, the constraint function f of the feature extraction factors α1, α2 and α3 of the autonomous driving project development framework is,

[0077]

[0078] The value range of the constraint function f is (0,1).

[0079] In this embodiment, if Figure 3 As shown, in step L3, the optimization of the matrix of the CSS atomic class by using the Archimedean optimization algorithm based on the dynamic learning factor includes:

[0080] L31. Based on the data information of the matrix of the CSS atomic class of the autonomous driving project development framework, the population is initialized, the population parameters are determined, and the data information of the initialized population is obtained;

[0081] L32. Based on the data information of the initialized population, establish the fitness function R of the population individuals,

[0082]

[0083] Among them, z is the data information of the initialized population, δ1, δ2 and δ3 are the fitness adjustment factors of the population individuals, and the fitness values ​​of the population individuals are calculated to obtain the data information of the fitness values ​​of the population individuals;

[0084] L33. Based on the data information of the fitness values ​​of the individuals in the population, establish the optimization function P of the population,

[0085]

[0086] Among them, r is the data information of the fitness value of the population individual, γ1, γ2 and γ3 are the dynamic learning factors of the population individual, and the matrix of the CSS atomic class is optimized to obtain the data information of the matrix of the CSS atomic class of the optimized autonomous driving project development framework.

[0087] In this embodiment, the constraints of the dynamic learning factors γ1, γ2 and γ3 of the individuals in the population are:

[0088]

[0089] Embodiment 2: Based on the method for generating a zero-dependency CSS architecture in Embodiment 1, the present invention is further illustrated and described below.

[0090] like Figure 1 As shown, a method for generating a zero-dependency CSS architecture includes:

[0091] L1. Collect data information of the autonomous driving project development framework, construct a data node matrix of the autonomous driving project development framework, and obtain data information of the matrix of data nodes of the autonomous driving project development framework;

[0092] L2. Based on the data information of the matrix of the data nodes of the autonomous driving project development framework, a CSS atomic class generation model of a multi-layer neural network based on an attenuation factor is constructed to characterize the generation of the CSS atomic class, and obtain the data information of the matrix of the CSS atomic class of the autonomous driving project development framework;

[0093] L3. Based on the data information of the matrix of the CSS atomic class of the autonomous driving project development framework, the matrix of the CSS atomic class is optimized using an Archimedean optimization algorithm based on a dynamic learning factor to obtain the data information of the matrix of the CSS atomic class of the autonomous driving project development framework after optimization;

[0094] L4. Based on the data information of the matrix of the CSS atomic class of the optimized autonomous driving project development framework, a generation model of the CSS architecture is constructed, the generated files of the CSS architecture are characterized, and the data information of the generated files of the CSS architecture is obtained.

[0095] In this embodiment, if Figure 4 As shown, in step L4, the construction of the generation model of the CSS architecture and the characterization of the generated files of the CSS architecture include:

[0096] L41. Based on the data information of the matrix of the CSS atomic class of the optimized autonomous driving project development framework, a regularization function S of the CSS atomic class of the autonomous driving project development framework is established.

[0097]

[0098] Among them, h is the data information of the matrix of the CSS atomic class of the autonomous driving project development framework after optimization, η1, η2 and η3 are the regularization parameters of the autonomous driving project development framework, and the matrix of the CSS atomic class is regularized to obtain the data information of the regularized matrix of the CSS atomic class of the autonomous driving project development framework;

[0099] L42. Based on the data information of the regularization matrix of the CSS atomic class of the autonomous driving project development framework, a generation function F of the CSS architecture is established.

[0100]

[0101] Among them, q is the data information of the regularization matrix of the CSS atomic class of the autonomous driving project development framework, and λ1, λ2, and λ3 are the model parameters generated by the CSS architecture;

[0102] L43. Based on the generation function F of the CSS architecture, the generated file of the CSS architecture is characterized to obtain data information of the generated file of the CSS architecture.

[0103] In this embodiment, the constraint function g of the regularization parameters η1, η2 and η3 of the autonomous driving project development framework is,

[0104]

[0105] Among them, the value range of the constraint function g is (2,3).

[0106] In this embodiment, the present invention provides a system for generating a zero-dependency CSS architecture, including a computer device, wherein the computer device is programmed or configured to execute the steps of any one of the methods for generating a zero-dependency CSS architecture.

[0107] In this embodiment, the present invention provides a computer-readable storage medium, on which is stored a computer program programmed or configured to execute any one of the methods for generating a zero-dependency CSS architecture.

[0108] Any reference to memory, storage, database or other media used in the embodiments provided in the present application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM) and memory bus dynamic RAM (RDRAM).

[0109] In summary, the present invention not only solves the problem that the dependencies between projects become complicated and the maintenance difficulty is increased, but also improves the development efficiency of the project and reduces the operating cost.

[0110] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A method for generating a zero-dependency CSS architecture, characterized in that: The method comprises: L1. Collect data information of the autonomous driving project development framework, construct a data node matrix of the autonomous driving project development framework, and obtain data information of the matrix of data nodes of the autonomous driving project development framework; L2. Based on the data information of the matrix of the data nodes of the autonomous driving project development framework, a CSS atomic class generation model of a multi-layer neural network based on an attenuation factor is constructed to characterize the generation of the CSS atomic class, and obtain the data information of the matrix of the CSS atomic class of the autonomous driving project development framework; L3. Based on the data information of the matrix of the CSS atomic class of the autonomous driving project development framework, the matrix of the CSS atomic class is optimized using an Archimedean optimization algorithm based on a dynamic learning factor to obtain the data information of the matrix of the CSS atomic class of the autonomous driving project development framework after optimization; L4. Based on the data information of the matrix of the CSS atomic class of the optimized autonomous driving project development framework, a generation model of the CSS architecture is constructed, the generated files of the CSS architecture are characterized, and the data information of the generated files of the CSS architecture is obtained.

2. The method for generating a zero-dependency CSS architecture according to claim 1, characterized in that: In step L2, the step of constructing a CSS atomic class generation model based on a multi-layer neural network of an attenuation factor and characterizing the generation of CSS atomic classes includes: L21. Based on the data information of the matrix of the data nodes of the autonomous driving project development framework, a feature extraction function Q of the autonomous driving project development framework is established. Among them, x is the data information of the matrix of the data nodes of the autonomous driving project development framework, α1, α2 and α3 are the feature extraction factors of the autonomous driving project development framework, and feature extraction is performed on the data of the autonomous driving project development framework to obtain the data information of the feature matrix of the data nodes of the autonomous driving project development framework; L22. Input the data information of the feature matrix of the data node of the autonomous driving project development framework into the CSS atomic class generation model of the multi-layer neural network based on the attenuation factor for training and learning, and determine the CSS atomic class generation function W of the neural network, Among them, y is the data information of the feature matrix of the data node of the autonomous driving project development framework, β1, β2 and β3 are the attenuation factors for the generation of CSS atomic classes, and the trained CSS atomic class generation model based on the attenuation factor multi-layer neural network is obtained; L23. Based on the trained CSS atomic class generation model based on the attenuation factor-based multi-layer neural network, the data information of the matrix of the data nodes of the autonomous driving project development framework is input, the generation of the CSS atomic class is characterized, and the data information of the matrix of the CSS atomic class of the autonomous driving project development framework is obtained.

3. The method for generating a zero-dependency CSS architecture according to claim 2, characterized in that: The attenuation factors β1, β2 and β3 generated by the CSS atomic class are, Among them, y is the data information of the feature matrix of the data node of the autonomous driving project development framework.

4. The method for generating a zero-dependency CSS architecture according to claim 2, characterized in that: The constraint function f of the feature extraction factors α1, α2 and α3 of the autonomous driving project development framework is, The value range of the constraint function f is (0,1).

5. The method for generating a zero-dependency CSS architecture according to claim 1, characterized in that: In step L3, the optimization of the matrix of the CSS atomic class by using the Archimedean optimization algorithm based on the dynamic learning factor includes: L31. Based on the data information of the matrix of the CSS atomic class of the autonomous driving project development framework, the population is initialized, the population parameters are determined, and the data information of the initialized population is obtained; L32. Based on the data information of the initialized population, establish the fitness function R of the population individuals, Among them, z is the data information of the initialized population, δ1, δ2 and δ3 are the fitness adjustment factors of the population individuals, and the fitness values ​​of the population individuals are calculated to obtain the data information of the fitness values ​​of the population individuals; L33. Based on the data information of the fitness values ​​of the individuals in the population, establish the optimization function P of the population, Among them, r is the data information of the fitness value of the population individual, γ1, γ2 and γ3 are the dynamic learning factors of the population individual, and the matrix of the CSS atomic class is optimized to obtain the data information of the matrix of the CSS atomic class of the optimized autonomous driving project development framework.

6. The method for generating a zero-dependency CSS architecture according to claim 5, characterized in that: The constraints of the dynamic learning factors γ1, γ2 and γ3 of the individuals in the population are:

7. The method for generating a zero-dependency CSS architecture according to claim 1, characterized in that: In step L4, the building of the generation model of the CSS architecture and characterizing the generated files of the CSS architecture include: L41. Based on the data information of the matrix of the CSS atomic class of the optimized autonomous driving project development framework, a regularization function S of the CSS atomic class of the autonomous driving project development framework is established. Among them, h is the data information of the matrix of the CSS atomic class of the autonomous driving project development framework after optimization, η1, η2 and η3 are the regularization parameters of the autonomous driving project development framework, and the matrix of the CSS atomic class is regularized to obtain the data information of the regularized matrix of the CSS atomic class of the autonomous driving project development framework; L42. Based on the data information of the regularization matrix of the CSS atomic class of the autonomous driving project development framework, a generation function F of the CSS architecture is established. Among them, q is the data information of the regularization matrix of the CSS atomic class of the autonomous driving project development framework, and λ1, λ2, and λ3 are the model parameters generated by the CSS architecture; L43. Based on the generation function F of the CSS architecture, the generated file of the CSS architecture is characterized to obtain data information of the generated file of the CSS architecture.

8. The method for generating a zero-dependency CSS architecture according to claim 7, characterized in that: The constraint function g of the regularization parameters η1, η2 and η3 of the autonomous driving project development framework is, Among them, the value range of the constraint function g is (2,3).

9. A zero-dependency CSS architecture generation system, comprising a computer device, characterized in that: The computer device is programmed or configured to execute the steps of the method for generating a zero-dependency CSS architecture as recited in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program programmed or configured to execute the method for generating a zero-dependency CSS architecture as described in any one of claims 1 to 8.

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