A method for constructing a software development model adaptive to multiple hardware platforms

Abstract

The application discloses a kind of construction methods of software development model suitable for multiple hardware platforms, comprising: obtaining the attribute information of multiple hardware platforms, constructing development component according to attribute information, and generating component database;Obtain software development requirements, determine target components in the component database according to the software development requirements;The target components are combined, and a software development model is constructed according to the combination result;Run the software development model to obtain running data;According to running data, the software development model is corrected to obtain a modified software development model.It is convenient to adapt to multiple hardware platforms, avoids repeated software development, software testing and subsequent support work, saves manpower and financial resources, effectively utilizes existing components for combination, accurately grasps the combination process, ensures the accuracy of the established software development model, and can improve software development efficiency.

Description

Technical Field

[0001] This invention relates to the field of software development technology, and in particular to a method for constructing a software development model that is compatible with multiple hardware platforms. Background Technology

[0002] With the continuous development of network and software technologies, increasingly higher demands are being placed on software development. Currently, the software development field is constrained by different hardware platforms (processor types, memory, etc.) and the design philosophies of various hardware product providers. Whenever a hardware platform is updated or a hardware product provider changes, the software needs to be redeveloped accordingly. When a product has multiple competing hardware manufacturers, the software needs to be repeatedly developed in multiple versions based on each manufacturer's hardware design to achieve the same functionality. This leads to repetitive software development, testing, and subsequent support work, resulting in a significant waste of human and financial resources, low software development efficiency, and the inability to rationally utilize existing components, as well as instances of unreasonable component combinations. Summary of the Invention

[0003] This invention aims to at least partially solve one of the technical problems in the aforementioned technologies. Therefore, the purpose of this invention is to propose a method for constructing a software development model adaptable to multiple hardware platforms. This method facilitates adaptation to multiple hardware platforms, avoids repetitive software development, testing, and subsequent support work, saves human and financial resources, effectively utilizes existing components for combination, accurately controls the combination process, ensures the accuracy of the established software development model, and can improve software development efficiency.

[0004] To achieve the above objectives, embodiments of the present invention propose a method for constructing a software development model adaptable to multiple hardware platforms, comprising:

[0005] Obtain attribute information from multiple hardware platforms, construct development components based on the attribute information, and generate a component database;

[0006] Obtain software development requirements, and determine target components in the component database based on the software development requirements;

[0007] The target components are combined, and a software development model is constructed based on the combination result;

[0008] The software development model is run to obtain running data; the running data is evaluated to determine the parameters to be corrected; the type of the parameters to be corrected is obtained; when the type of the parameters to be corrected is determined to be a first type, the weight coefficients of each target component in constructing the software development model are obtained; adjustment parameters are determined according to the parameters to be corrected, the weight coefficients are adjusted according to the adjustment parameters, and the corrected software development model is obtained according to the adjustment result; when the type of the parameters to be corrected is determined to be a second type, new target components are determined according to the parameters to be corrected, and the new target components are combined into the software development model to obtain the corrected software development model.

[0009] According to some embodiments of the present invention, obtaining attribute information of multiple hardware platforms, constructing development components based on the attribute information, and generating a component database includes:

[0010] Obtain attribute information for multiple hardware platforms; the attribute information includes processor architecture, clock speed, number of cores, memory size, storage type, peripheral interfaces, operating system support, and power consumption;

[0011] The data source is obtained based on the attribute information, the data source is preprocessed, and the data used to build the development component is determined and marked as target data; wherein, the data source includes the underlying driver data source, the middleware data source, and the application framework data source;

[0012] Obtain the attribute information of the target data;

[0013] Cluster analysis is performed on the target data based on the attribute information to obtain several classification sets;

[0014] Determine the rank information of each component element in the same classification set, and group components of the same rank into a data group;

[0015] Development components are constructed based on the data set, and the development components are stored according to category and level to obtain a component database.

[0016] According to some embodiments of the present invention, the step of combining the target components and constructing a software development model based on the combination result includes:

[0017] Establish the operational relationships between the various target components;

[0018] The combination order is determined based on the aforementioned operational relationship;

[0019] In the process of combining the target components according to the combination order, the combination node of the first target component and the second target component is determined. The code of the first target component and the code of the second target component are merged according to the combination node to obtain the first merged component. Based on the same method, the first merged component is merged with the third target component to obtain the second merged component. The target components are combined in sequence, and a software development model is constructed based on the combination result.

[0020] According to some embodiments of the present invention, the method further includes: encrypting the modified software development model based on the RSA algorithm to obtain an encrypted software development model and storing it.

[0021] According to some embodiments of the present invention, the process of assembling the target components further includes:

[0022] The duration of each combination between target components is counted, and it is determined whether it is greater than a preset duration. If the combination process is determined to be longer than the preset duration, the combination process is optimized to obtain the optimization method and store it.

[0023] According to some embodiments of the present invention, the preprocessing includes data cleaning.

[0024] According to some embodiments of the present invention, data cleaning of the data source includes:

[0025] The data source is divided into several data blocks based on the business type;

[0026] The data blocks are input into a pre-trained feature extraction model, which outputs the feature values ​​of the data blocks in several spatial dimensions.

[0027] Calculate the average of the feature values ​​of several data blocks in the same spatial dimension;

[0028] The eigenvalues ​​of the data block in each spatial dimension are compared with the average value of the corresponding spatial dimension, and the covariance matrix of the data block is determined based on the comparison results.

[0029] The covariance matrix of the data block is analyzed to determine the target data block. The target data block is then subjected to dimensionality reduction to obtain the hash value of the target data block.

[0030] The target data blocks with hash values ​​greater than the preset hash value are removed to obtain cleaned data.

[0031] According to some embodiments of the present invention, it further includes:

[0032] During the process of assembling the target components, data generated during the assembly process is collected as first data; the first data includes assembly data and attribute data related to the assembly data.

[0033] The attribute data is parsed to obtain the parsing results;

[0034] Based on the user-preset data processing rules and the parsing results, the edge-type attribute data in the first data is deleted to obtain the second data and it is stored.

[0035] According to some embodiments of the present invention, determining target components in the component database based on the software development requirements includes:

[0036] Obtain sample software development requirements, perform cluster analysis on the sample software development requirements to obtain several classification sets, train the constructed initial neural network model based on each classification set until the loss function corresponding to the initial neural network model converges, and use the trained initial neural network as the advanced neural network model.

[0037] Cluster centers are determined in each category set, the distances between each cluster center are determined, and an identification tree is built; the identification tree has multiple nodes, and several advanced neural network models are filled into each node, with one advanced neural network model filling each node;

[0038] Calculate the distance between the software development requirements and each cluster center, select the advanced neural network model corresponding to the cluster center with the smallest distance as the target advanced neural network model; input the software development requirements into the target advanced neural network model to obtain several semantic recognition results of the software development requirements;

[0039] The software development requirements are divided into several first sub-data, and each sub-data is filled into the first node of a preset first data matrix.

[0040] Each semantic recognition result is segmented to obtain several second sub-data, which are then filled into the second node of the preset second data matrix.

[0041] Establish the correspondence between the first node and the second node, merge the first sub-data on the first node and the second sub-data on the second node, determine the fusion rate of each fusion node, and calculate the average fusion rate.

[0042] Based on several semantic recognition results, several average fusion rates are obtained. The semantic recognition result corresponding to the highest average fusion rate is taken as the target semantic recognition result.

[0043] The target component is determined in the component database based on the target semantic recognition result.

[0044] This invention proposes a method for constructing a software development model adaptable to multiple hardware platforms. The method involves running the constructed software development model to obtain runtime parameters, and then correcting the model based on these parameters to ensure the accuracy of the final corrected software development model. During the correction process, different correction strategies are adopted based on different types of correction parameters to achieve rapid correction and ensure accuracy. This facilitates adaptation to multiple hardware platforms, avoids repetitive software development, testing, and subsequent support work, and saves human and financial resources. It effectively utilizes existing components for combination, accurately controls the combination process, and ensures the accuracy of the established software development model, thereby improving software development efficiency, reducing the workload of R&D personnel, and shortening working time. It effectively utilizes the attribute information of multiple hardware platforms to construct development components and builds the software development model according to software development requirements. Simultaneously, through evaluation of runtime data and model correction, the software development model is continuously optimized to better adapt to the target hardware platform and meet user needs, not only improving the efficiency and quality of software development but also enhancing the software's flexibility and scalability.

[0045] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0046] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0047] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0048] Figure 1 This is a flowchart of a method for constructing a software development model adapted to multiple hardware platforms according to an embodiment of the present invention;

[0049] Figure 2 This is a flowchart of generating a component database according to an embodiment of the present invention. Detailed Implementation

[0050] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0051] like Figure 1As shown, this embodiment of the invention proposes a method for constructing a software development model adaptable to multiple hardware platforms, including steps S1-S4:

[0052] S1. Obtain attribute information from multiple hardware platforms, construct development components based on the attribute information, and generate a component database;

[0053] S2. Obtain software development requirements, and determine the target component in the component database based on the software development requirements;

[0054] S3. Combine the target components and construct a software development model based on the combination result;

[0055] S4. Run the software development model to obtain running data; evaluate the running data to determine the parameters to be corrected; obtain the type of the parameters to be corrected; when the type of the parameters to be corrected is determined to be a first type, obtain the weight coefficients of each target component in constructing the software development model; determine adjustment parameters according to the parameters to be corrected, adjust the weight coefficients according to the adjustment parameters, and obtain the corrected software development model according to the adjustment results; when the type of the parameters to be corrected is determined to be a second type, determine new target components according to the parameters to be corrected, and combine the new target components into the software development model to obtain the corrected software development model.

[0056] The working principle of the above technical solution is as follows: Acquire attribute information from multiple hardware platforms, design and build development components adapted to them, and organize all the built development components into a database, recording key information such as the function, performance, and compatibility of each component. Software development requirements include the application scenarios, functions, application categories, and levels of the software. Clearly define the specific requirements for software development, including functional requirements, performance requirements, and compatibility requirements. Based on the software development requirements, search and determine the required target components in the component database. Combine the determined target components according to the software architecture and functional requirements to form a preliminary software development model. In the development environment, use the combined target components to build the software development model and conduct preliminary functional and performance tests. Run the software development model on the target hardware platform and collect runtime data, including performance indicators, error logs, and user feedback. Analyze and evaluate the collected runtime data to determine the problems in the model and the parameters that need to be corrected. Based on the evaluation results, the parameters to be corrected are divided into two categories: the first category is parameters that can be corrected by adjusting the weights of existing components (such as performance tuning); the second category is parameters that need to be solved by introducing new components (such as missing functions). The first type represents parameters whose execution process is complete, but whose accuracy is lower than the preset accuracy. For this type of parameter, the weight coefficient of each target component in building the software development model is calculated, and adjustment parameters are determined based on the parameters to be corrected. The weight coefficients are then adjusted to obtain the corrected software development model. The second type represents parameters corresponding to incomplete execution processes in the software development model. For this type of parameter, the target components that need to be added are determined based on the parameters to be corrected. These new components are then combined into the software development model, and necessary adjustments and optimizations are made to obtain the corrected software development model.

[0057] The beneficial effects of the above technical solution are as follows: The constructed software development model is run to obtain runtime parameters, and the model is corrected based on these parameters, ensuring the accuracy of the final corrected software development model. During the correction process, different correction strategies are adopted based on different types of correction parameters, achieving rapid correction and ensuring accuracy. It facilitates adaptation to multiple hardware platforms, avoiding repetitive software development, testing, and subsequent support work, saving human and financial resources. Effectively utilizing existing components for combination, and accurately controlling the combination process, ensures the accuracy of the established software development model, improving software development efficiency, reducing the workload of R&D personnel, and shortening working time. It effectively utilizes the attribute information of multiple hardware platforms to construct development components and builds a software development model according to software development requirements. Simultaneously, through the evaluation of runtime data and model correction, the software development model is continuously optimized to better adapt to the target hardware platform and meet user needs, not only improving the efficiency and quality of software development but also enhancing the software's flexibility and scalability.

[0058] like Figure 2 As shown, according to some embodiments of the present invention, obtaining attribute information of multiple hardware platforms, constructing development components based on the attribute information, and generating a component database includes steps S11-S16:

[0059] S11. Obtain attribute information of multiple hardware platforms; the attribute information includes processor architecture, clock speed, number of cores, memory size, storage type, peripheral interfaces, operating system support, and power consumption;

[0060] S12. Obtain the data source based on the attribute information, preprocess the data source, determine the data used to build the development component, and mark it as target data; wherein, the data source includes the underlying driver data source, the middleware data source, and the application framework data source;

[0061] S13. Obtain the attribute information of the target data;

[0062] S14. Perform cluster analysis on the target data based on the attribute information to obtain several classification sets;

[0063] S15. Determine the level information of each component element in the same classification set, and group the component elements of the same level into a data group.

[0064] S16. Construct development components based on the data set, and store the development components according to category and level to obtain a component database.

[0065] The working principle of the above technical solution: Attribute information: Processor architecture: such as x86, ARM, RICS-V, etc., which determines the software compilation method and execution efficiency. Clock speed: The processor's clock frequency, affecting processing speed. Number of cores: The number of processor cores, affecting parallel processing capabilities. Memory size: The capacity of system memory, affecting software running efficiency and the amount of data that can be loaded. Storage type: such as HDD, SSD, NVMe, etc., affecting data read / write speed and storage capacity. Peripheral interfaces: such as USB, HDMI, Thunderbolt, etc., affecting connection and communication with external devices. Operating system support: The type of operating system supported by the hardware platform, such as Windows, Linux, macOS, etc. Power consumption: The power consumption of the hardware platform, affecting battery life and energy efficiency. Based on the collected attribute information, data for building development components is obtained from multiple data sources. These data sources include: Low-level driver data sources: Providing drivers and interfaces that directly interact with the hardware, ensuring that the software can correctly identify and control the hardware. Middleware data source: Provides a cross-platform, cross-hardware abstraction layer, simplifying the software development process and improving code portability and reusability. Application framework data source: Provides high-level components and tools needed to build applications, such as UI frameworks and database access layers. During preprocessing, the data sources need to be carefully analyzed to determine which data is essential for building development components. This target data should accurately reflect the hardware platform's attribute information and meet the software development requirements. Once the target data is identified, it needs to be tagged for easy identification and use in subsequent development. Tagging can include information such as data type, source, and purpose. By acquiring attribute information from multiple hardware platforms and obtaining and preprocessing data from underlying driver data sources, middleware data sources, and application framework data sources, the target data used to build development components is identified and tagged. This provides a solid foundation for subsequent development work and helps build an efficient, compatible, and easily maintainable software development environment. The attribute information of the target data is obtained, including categories. Cluster analysis is performed on the target data based on the attribute information to obtain several classification sets; the level information of each component in the same classification set is determined, and components of the same level are grouped together as a data group; development components are constructed based on the data groups, and the development components are stored according to category and level to obtain a component database.

[0066] The beneficial effects of the above technical solution are: establishing different categories and identifying different levels of development components within the same category, thereby achieving effective management of development components and facilitating the rapid identification of target components based on development needs in subsequent steps.

[0067] According to some embodiments of the present invention, the step of combining the target components and constructing a software development model based on the combination result includes:

[0068] Establish the operational relationships between the various target components;

[0069] The combination order is determined based on the aforementioned operational relationship;

[0070] In the process of combining the target components according to the combination order, the combination node of the first target component and the second target component is determined. The code of the first target component and the code of the second target component are merged according to the combination node to obtain the first merged component. Based on the same method, the first merged component is merged with the third target component to obtain the second merged component. The target components are combined in sequence, and a software development model is constructed based on the combination result.

[0071] The working principle of the above technical solution is as follows: establish the operational relationship between various target components; determine the combination order according to the operational relationship; during the process of combining the target components according to the combination order, determine the combination node of the first target component and the second target component; merge the code of the first target component and the code of the second target component according to the combination node to obtain the first merged component; merge the first merged component and the third target component according to the same method to obtain the second merged component; combine the target components in sequence; and construct a software development model based on the combination result.

[0072] The beneficial effects of the above technical solution are as follows: Combining the target components according to the combination order facilitates the logical construction of the software development model; merging the target components in sequence enables the orderly management of the software development model and improves software development efficiency.

[0073] According to some embodiments of the present invention, after constructing the software development model based on the combination result, the method further includes:

[0074] The modified software development model is encrypted using the RSA algorithm to obtain an encrypted software development model, which is then stored.

[0075] The beneficial effects of the above technical solution are: improved security of the modified software development model.

[0076] According to some embodiments of the present invention, the process of assembling the target components further includes:

[0077] The duration of each combination between target components is counted, and it is determined whether it is greater than a preset duration. If the combination process is determined to be longer than the preset duration, the combination process is optimized to obtain the optimization method and store it.

[0078] The working principle of the above technical solution is as follows: the duration of each combination between target components is counted, and it is determined whether it is greater than the preset duration. If the combination process is determined to be longer than the preset duration, the combination process is optimized to obtain the optimization method and store it.

[0079] The beneficial effects of the above technical solution are: it facilitates the optimization of combined processes whose duration exceeds the preset duration, obtains the optimization method, and makes it easier to optimize the combined process in the future, thereby improving the efficiency of subsequent software development.

[0080] According to some embodiments of the present invention, the preprocessing includes data cleaning.

[0081] According to some embodiments of the present invention, data cleaning of the data source includes:

[0082] The data source is divided into several data blocks based on the business type;

[0083] The data blocks are input into a pre-trained feature extraction model, which outputs the feature values ​​of the data blocks in several spatial dimensions.

[0084] Calculate the average of the feature values ​​of several data blocks in the same spatial dimension;

[0085] The eigenvalues ​​of the data block in each spatial dimension are compared with the average value of the corresponding spatial dimension, and the covariance matrix of the data block is determined based on the comparison results.

[0086] The covariance matrix of the data block is analyzed to determine the target data block. The target data block is then subjected to dimensionality reduction to obtain the hash value of the target data block.

[0087] The target data blocks with hash values ​​greater than the preset hash value are removed to obtain cleaned data.

[0088] The working principle of the above technical solution is as follows: The data source is segmented based on business type to obtain several data blocks; the several data blocks are respectively input into a pre-trained feature extraction model, and the feature values ​​of the data blocks in several spatial dimensions are output; the average value of the feature values ​​of the several data blocks in the same spatial dimension is calculated; the feature values ​​of the data blocks in each spatial dimension are compared with the average value of the corresponding spatial dimension, and the covariance matrix of the data block is determined according to the comparison results; the covariance matrix of the data blocks is analyzed to determine the target data blocks; the target data blocks are subjected to dimensionality reduction processing to obtain the hash value of the target data blocks; the target data blocks with hash values ​​greater than a preset hash value are removed to obtain cleaned data.

[0089] The beneficial effects of the above technical solution are: dimensionality reduction of the target data block in the data source, which makes it easier to reduce the size of the data, improves the efficiency of data cleaning, and also ensures the accuracy of data cleaning.

[0090] According to some embodiments of the present invention, it further includes:

[0091] During the process of assembling the target components, data generated during the assembly process is collected as first data; the first data includes assembly data and attribute data related to the assembly data.

[0092] The attribute data is parsed to obtain the parsing results;

[0093] Based on the user-preset data processing rules and the parsing results, the edge-type attribute data in the first data is deleted to obtain the second data and it is stored.

[0094] The working principle of the above technical solution is as follows: During the process of assembling the target components, data generated during the assembly process is collected as first data; the first data includes assembly data and attribute data related to the assembly data; the attribute data is parsed to obtain parsing results; according to the user-preset data processing rules and the parsing results, edge-type attribute data in the first data is deleted to obtain second data, which is then stored. The preset data processing rules are the data to be saved according to the user's specified requirements.

[0095] The beneficial effects of the above technical solution are: it helps to reduce the amount of data that users do not need, saves storage space in the memory, and makes the second data more accurate.

[0096] In one embodiment, storing the encryption software development model in a memory includes:

[0097] Calculate the distribution balance of the data stored in the memory and determine whether it is within the preset distribution balance range;

[0098]

[0099] Where S represents the distribution uniformity of the stored data in the memory; N represents the number of storage nodes included in the memory; w i Let be the size of the stored data in the i-th storage node; Q is the total amount of stored data.

[0100] When it is determined that the distribution balance is not within the preset distribution balance range, the data stored in each storage node is sorted from smallest to largest, and the first preset number of storage nodes are selected.

[0101] The encryption software development model is divided equally according to a preset number and stored in the first preset number of storage nodes.

[0102] The working principle and beneficial effects of the above technical solution are as follows: The distribution balance of the stored data in the memory is calculated, and it is determined whether it is within a preset distribution balance range. When the distribution balance is determined to be outside the preset range, the data in each storage node is sorted from smallest to largest, and the first preset number of storage nodes are selected. The encryption software development model is then evenly distributed according to a preset number and stored in the first preset number of storage nodes. Each time data is stored in the memory, the distribution balance is calculated, which facilitates accurate determination of the corresponding storage nodes. Storing data in the corresponding storage nodes helps adjust the distribution balance of the memory, preventing some storage nodes from being overloaded while others remain idle for extended periods, thus improving the memory's lifespan.

[0103] According to some embodiments of the present invention, determining target components in the component database based on the software development requirements includes:

[0104] Obtain sample software development requirements, perform cluster analysis on the sample software development requirements to obtain several classification sets, train the constructed initial neural network model based on each classification set until the loss function corresponding to the initial neural network model converges, and use the trained initial neural network as the advanced neural network model.

[0105] Cluster centers are determined in each category set, the distances between each cluster center are determined, and an identification tree is built; the identification tree has multiple nodes, and several advanced neural network models are filled into each node, with one advanced neural network model filling each node;

[0106] Calculate the distance between the software development requirements and each cluster center, select the advanced neural network model corresponding to the cluster center with the smallest distance as the target advanced neural network model; input the software development requirements into the target advanced neural network model to obtain several semantic recognition results of the software development requirements;

[0107] The software development requirements are divided into several first sub-data, and each sub-data is filled into the first node of a preset first data matrix.

[0108] Each semantic recognition result is segmented to obtain several second sub-data, which are then filled into the second node of the preset second data matrix.

[0109] Establish the correspondence between the first node and the second node, merge the first sub-data on the first node and the second sub-data on the second node, determine the fusion rate of each fusion node, and calculate the average fusion rate.

[0110] Based on several semantic recognition results, several average fusion rates are obtained. The semantic recognition result corresponding to the highest average fusion rate is taken as the target semantic recognition result.

[0111] The target component is determined in the component database based on the target semantic recognition result.

[0112] The working principle of the above technical solution is as follows: Obtain sample software development requirements; perform cluster analysis on the sample software development requirements to obtain several classification sets; train an initial neural network model based on each classification set until the loss function corresponding to the initial neural network model converges; use the trained initial neural network as an advanced neural network model; determine the cluster centers in each classification set, determine the distances between each cluster center, and build a recognition tree; the recognition tree has multiple nodes, and several advanced neural network models are filled into each node, with one advanced neural network model filling each node; calculate the distance between the software development requirements and each cluster center, and select the advanced neural network model corresponding to the cluster center with the smallest distance as the target advanced neural network model; input the software development requirements into the target... In the advanced neural network model, several semantic recognition results of the software development requirements are obtained; the software development requirements are segmented to obtain several first sub-data, which are then filled into the first node of a preset first data matrix; each semantic recognition result is segmented to obtain several second sub-data, which are then filled into the second node of a preset second data matrix; a correspondence between the first node and the second node is established, and the first sub-data on the first node and the second sub-data on the second node are fused to determine the fusion rate of each fusion node and calculate the average fusion rate; based on the several semantic recognition results, several average fusion rates are obtained, and the semantic recognition result corresponding to the largest average fusion rate is taken as the target semantic recognition result; the target component is determined in the component database according to the target semantic recognition result. The matrix structures of the first data matrix and the second data matrix are consistent.

[0113] The beneficial effects of the above technical solution are as follows: Cluster analysis is performed based on multiple sample software development requirements to determine multiple advanced neural network models and establish a recognition tree, facilitating semantic recognition of different types of software development requirements. Several semantic recognition results for the software development requirements are obtained based on the target advanced neural network model. To further improve the accuracy of semantic recognition, the first sub-data on the first node and the second sub-data on the second node are fused. The principle is that the first and second sub-data are based on a consistent principle; the smoother the fusion process, the higher the fusion efficiency, and the more accurate the parsing result. Therefore, based on several semantic recognition results, several average fusion rates are obtained. The average fusion rate represents the overall parsing result; the larger the average fusion rate, the more accurate the corresponding semantic recognition result. The semantic recognition result corresponding to the highest average fusion rate is taken as the target semantic recognition result. The target component is determined in the component database based on the target semantic recognition result, improving the accuracy of semantic analysis of software development requirements and also improving the accuracy of determining the target component.

[0114] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A construction method of adapting a software development model to a multi-hardware platform, characterized by, The method comprises the following steps: Obtaining attribute information of multiple hardware platforms, constructing development components according to the attribute information, and generating a component database; Obtaining software development requirements, determining target components in the component database according to the software development requirements; Combining the target components, and constructing a software development model according to the combination result; Running the software development model to obtain running data; Evaluating the running data to determine a parameter to be corrected, obtaining the type of the parameter to be corrected, determining the weight coefficients of each target component in constructing the software development model when the target component is used to construct the software development model when the type of the parameter to be corrected is determined to be a first type, determining an adjustment parameter according to the parameter to be corrected, adjusting the weight coefficients according to the adjustment parameter, and obtaining a corrected software development model according to the adjustment result; when the type of the parameter to be corrected is determined to be a second type, determining an added target component according to the parameter to be corrected, combining the added target component into the software development model to obtain a corrected software development model; Obtaining attribute information of multiple hardware platforms, constructing development components according to the attribute information, and generating a component database, comprising: Obtaining attribute information of multiple hardware platforms; the attribute information includes processor architecture, main frequency, core number, memory size, storage type, peripheral interface, operating system support, and power consumption; Obtaining a data source according to the attribute information, preprocessing the data source, determining data for constructing development components, and marking the data as target data; wherein the data source includes a bottom driver data source, a middleware data source, and an application framework data source; Obtaining attribute information of the target data; Performing cluster analysis on the target data according to the attribute information to obtain a plurality of classification sets; Determining the level information of each component element in the same classification set, and regarding the component elements of the same level as a data group; Constructing development components according to the data group, and storing the development components according to categories and levels to obtain a component database; The method comprises the following steps: Establishing the running relationship between each target component; Determining the combination order according to the running relationship; In the process of combining the target components according to the combination order, determining the combination node of the first target component and the second target component, merging the code of the first target component and the code of the second target component according to the combination node to obtain a first merged component, and based on the same method, merging the first merged component and the third target component to obtain a second merged component, and sequentially combining the target components to construct a software development model according to the combination result.

2. The construction method of software development model adapting to multiple hardware platforms according to claim 1, characterized in that, Further comprising: Encrypting the corrected software development model based on the RSA algorithm to obtain an encrypted software development model and store it.

3. The construction method of software development model adapting to multiple hardware platforms according to claim 1, characterized in that, In the process of combining the target components, further comprising: Statistically obtaining the time length of each combination between the target components, and determining whether it is greater than a preset time length, determining the combination process whose time length is greater than the preset time length, optimizing the combination process, obtaining an optimization method, and storing it.

4. The construction method of software development model adapting to multiple hardware platforms according to claim 1, characterized in that, The preprocessing comprises data cleaning.

5. The construction method of software development model adapting to multiple hardware platforms according to claim 4, characterized in that, The data source is subjected to data cleaning, comprising: segmenting the data source based on business types to obtain a plurality of data blocks; inputting the plurality of data blocks into a pre-trained feature extraction model respectively to output feature values of the data blocks in a plurality of spatial dimensions; calculating average values of the feature values of the plurality of data blocks in the same spatial dimension; comparing the feature values of the data blocks in each spatial dimension with the average values of the corresponding spatial dimensions respectively to determine a covariance matrix of the data blocks according to the comparison results; analyzing the covariance matrix of the data blocks to determine a target data block, and performing dimensionality reduction processing on the target data block to obtain a hash value of the target data block; eliminating the target data block with a hash value greater than a preset hash value to obtain cleaned data.

6. The construction method of software development model adapting to multiple hardware platforms according to claim 1, wherein, Further comprising: collecting data generated in the process of combining the target components as first data; the first data comprises combination data and attribute data related to the combination data; analyzing the attribute data to obtain an analysis result; deleting attribute data of an edge type in the first data according to a data processing rule preset by a user and the analysis result to obtain second data and store the second data.

7. The construction method of software development model adapting to multiple hardware platforms according to claim 1, wherein, determining a target component in the component database according to the software development requirement, comprising: obtaining sample software development requirements, performing cluster analysis on the sample software development requirements to obtain a plurality of classification sets, training an initial neural network model based on each classification set until a loss function corresponding to the initial neural network model converges, and taking the initial neural network after the training as an advanced neural network model; determining the cluster centers in each classification set respectively, determining the distances between the cluster centers, and establishing an identification tree; corresponding to a plurality of nodes on the identification tree, a plurality of advanced neural network models are filled into each node, and one advanced neural network model is filled into each node; calculating the distances between the software development requirement and the cluster centers, screening out an advanced neural network model corresponding to a cluster center with the smallest distance as a target advanced neural network model; inputting the software development requirement into the target advanced neural network model to obtain a plurality of semantic recognition results of the software development requirement; segmenting the software development requirement to obtain a plurality of first sub-data, and filling the first sub-data into first nodes of a preset first data matrix respectively; segmenting each semantic recognition result to obtain a plurality of second sub-data, and filling the second sub-data into second nodes of a preset second data matrix respectively; establishing a corresponding relationship between the first nodes and the second nodes, fusing the first sub-data on the first nodes with the second sub-data on the second nodes, determining a fusion rate of each fusion node, and calculating an average fusion rate; based on a plurality of semantic recognition results, a plurality of average fusion rates are obtained, and a semantic recognition result corresponding to the largest average fusion rate is taken as a target semantic recognition result; determining a target component in the component database according to the target semantic recognition result.

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