Fast construction method and device for task planning domain software based on feature variability

Abstract

The application provides a task planning field software rapid construction method and device based on feature variability, and relates to the field of software design; the method comprises the following steps: constructing basic function components for realizing different planning; constructing a feature model of the task planning series software, setting the task type as a multiple-choice feature point, setting the mode type as a single-choice feature point, and setting the additional function as an optional feature point; according to the constructed feature model, generating all effective software variants by calculating the cosine similarity of the multiple-choice, single-choice and optional feature point combinations; based on the calling dependency relationship and priority among the components, developing a scheduling algorithm for optimizing the component integration sequence, the scheduling algorithm adopts an attention mechanism to calculate the importance weight of the specific position sequence of each component integration, and constructing a final application program. According to the scheme, the planning software can be efficiently generated, and the component integration sequence can be reasonably scheduled, so that a high-quality software product meeting the individual needs of users can be delivered in a short time.

Description

Technical Field

[0001] This invention belongs to the field of software design, specifically relating to a method and apparatus for rapidly constructing task planning software based on feature variability. Background Technology

[0002] Mission planning is a crucial and widely applied field, encompassing various types such as rescue planning, resupply planning, drone planning, vehicle planning, and navigation planning. Traditional mission planning software systems are typically custom-developed for specific mission types and scenarios, lacking flexibility and scalability.

[0003] As the field of task planning continues to evolve, user demands for planning software are also constantly changing and expanding. Users may require software that supports multiple task planning types simultaneously, or to add new planning methods, optimization algorithms, and auxiliary functions to existing planning functionalities. However, using traditional software development models, it is very difficult to quickly respond to these changing needs.

[0004] In existing technologies, some studies have proposed methods based on software product lines and service-oriented architectures to improve software reusability and flexibility. However, these methods often require extensive domain analysis and modeling, which remains a challenge for rapidly building software systems that meet users' personalized needs. On the other hand, feature modeling techniques are widely used to describe and build highly configurable software systems. By constructing feature models and selecting features as needed, different software instances can be generated relatively easily. However, existing feature models mainly focus on the functional characteristics of software and lack support for modeling and optimizing the software construction process.

[0005] Therefore, there is an urgent need for a new method for rapid software development that can efficiently generate various variations of planning software based on feature models, while rationally scheduling the integration order of components, thereby delivering high-quality software products that meet users' personalized needs in a shorter time. Summary of the Invention

[0006] In view of this, the present invention provides a method for rapid construction of task planning domain software based on feature variability, comprising the following steps:

[0007] Step 1: Construct the basic functional components to implement different plans;

[0008] Step 2: Construct the feature model of the task planning software series, setting task type as a multi-select feature point, method type as a single-select feature point, and additional functions as optional feature points.

[0009] Step 3: Based on the constructed feature model, generate all valid software variants by calculating the cosine similarity of multiple-choice, single-choice, and optional feature point combinations;

[0010] Step 4: Based on the calling dependencies and priorities between components, develop a scheduling algorithm to optimize the component integration order. This scheduling algorithm uses an attention mechanism to calculate the importance weight of the specific position order of each component integration and builds the final application.

[0011] Specifically, in step 1, the basic functional components of different plans include: rescue planning, supply planning, drone planning, vehicle planning, and navigation planning.

[0012] Specifically, in step 3, all valid software variants are calculated and generated, including:

[0013]

[0014] Where P represents the combined feature vector, which is a vector generated based on the combination of multiple-choice, single-choice, and optional feature points; C represents the feature model vector, which is a pre-constructed feature model vector representing all variable features of the task planning software series; w P w C These are the weighting coefficients of the combined feature vector P and the feature model vector C in each dimension, used to adjust the importance of features in different dimensions; they are usually set to non-negative values, with larger values ​​indicating greater importance of that dimension; P i C i These represent the values ​​of the combined feature vector P and the feature model vector C in the i-th dimension, respectively; they can be 0 or 1, where 1 indicates that the feature exists and 0 indicates that it does not exist.

[0015] N P N C These are the normalization factors for vectors P and C, respectively. The purpose is to normalize the vectors to a similar scale and avoid the influence of length differences on similarity. The value is taken as the L2 norm of the vector.

[0016] Specifically, combined features with cosine similarity values ​​higher than a preset threshold are considered valid variants.

[0017] Specifically, an attention mechanism is used to calculate the importance weights of the specific order in which each component is integrated, and the final application is constructed, including:

[0018]

[0019] Where, α i,k W represents the attention weight for the k-th component at the i-th integration position; l U l ,Γ l For the model's learnable parameters; F i,kThis represents the feature vector of the k-th component at the i-th integration position; the integration order of each component is determined based on the attention weights.

[0020] In particular, the eigenvector F i,k This includes various attribute information describing the component.

[0021] Specifically, the attribute information includes component type, function, call dependencies, and priority.

[0022] This invention also proposes a rapid construction device for task planning domain software based on feature variability, comprising:

[0023] The basic functional component building module is used to build basic functional components to implement different plans;

[0024] The feature model building module is used to build feature models for the task planning software series, setting task type as multi-select feature points, method type as single-select feature points, and additional functions as optional feature points.

[0025] The software variant generation module is used to generate all valid software variants based on the constructed feature model by calculating the cosine similarity of multiple-choice, single-choice, and optional feature point combinations.

[0026] The final application building block is used to develop a scheduling algorithm to optimize the component integration order based on the call dependencies and priorities between components. This scheduling algorithm uses an attention mechanism to calculate the importance weight of the specific position order of each component integration and builds the final application.

[0027] The method provided by this invention has the following beneficial technical effects:

[0028] 1. Quickly build task planning software systems that meet users' personalized needs. By building a task planning software feature model based on feature variability, users can flexibly select the required task types, planning methods and additional functions according to actual needs, and quickly generate customized software variants, which greatly shortens the software development cycle.

[0029] 2. Improve the reusability and scalability of the software by encapsulating different planning functions into reusable components. New software variants can be generated by combining feature models. If new planning requirements arise in the future, only the corresponding functional components need to be developed and the feature models updated, without affecting the reuse of existing components.

[0030] 3. The software build process was optimized by designing a scheduling algorithm based on component call dependencies and priorities, and using an attention mechanism to calculate the weight of the integration order of each component, ensuring that the final application has high performance and reliability.

[0031] 4. Reduce software development and maintenance costs. By adopting a feature-based construction method, repetitive analysis, modeling, and coding work is avoided, significantly saving manpower and time costs. Furthermore, the maintenance cost of deriving new variants based on existing components is also lower.

[0032] 5. Enhance the flexibility and customizability of software systems. Feature models make the functional and non-functional characteristics of software explicit, allowing users to freely combine them according to their needs, achieving highly flexible customization and meeting the application needs of different fields and scenarios. Attached Figure Description

[0033] Figure 1 This is a schematic diagram of the process for rapidly constructing task planning domain software based on feature variability proposed in this invention;

[0034] Figure 2 This is a schematic diagram of the rapid construction device for task planning domain software based on feature variability proposed in this invention. Detailed Implementation

[0035] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0036] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0037] This invention provides a method for rapid construction of task planning domain software based on feature variability, such as... Figure 1 As shown, it includes the following steps:

[0038] Step 1: Construct basic functional components for implementing different plans; the basic functional components for implementing different plans in Step 1 include: rescue planning, supply planning, drone planning, vehicle planning and navigation planning.

[0039] Rescue planning includes planning rescue routes and allocating manpower and resources based on information such as the accident location, casualties, and on-site environment. It integrates the collaborative operation processes of various rescue forces (firefighting, medical, etc.) and considers emergency plans such as traffic control and evacuation. Supply planning includes planning the types and quantities of supplies based on disaster relief needs, considering storage, transportation, and distribution routes and schedules, or establishing a tracking and dispatch mechanism for supplies. Drone planning includes planning drone take-off and landing points, routes, and altitudes based on operational needs; integrating drones for reconnaissance, surveillance, and search, as well as handling data transmission, command control, and power management. Transportation planning includes planning vehicle dispatch based on the starting point, destination, and number of evacuees; arranging boarding points, boarding order, and transportation routes; considering vehicle types and passenger capacity. Navigation planning includes integrating Global Positioning System (GPS), Geographic Information System (GIS), etc.; providing accurate navigation services for various rescue vehicles and personnel and dynamically planning detour routes based on real-time traffic conditions.

[0040] Step 2: Construct a feature model for the task planning software series, setting task type as a multi-select feature point, mode type as a single-select feature point, and additional functions as optional feature points. The feature model includes: root feature: task planning software series; task type using multi-select feature points, including: rescue planning, supply planning, drone planning, vehicle planning, and navigation planning; mode type using single-select feature points, including: constraint-based solving, heuristic algorithm-based, machine learning-based, and rule-based reasoning; additional functions using optional feature points, including: decision support, risk assessment, resource management, information sharing, 2D / 3D visualization, real-time monitoring, and report generation.

[0041] Step 3: Based on the constructed feature model, generate all valid software variants by calculating the cosine similarity of multiple-choice, single-choice, and optional feature point combinations; Step 3, specifically, includes calculating and generating all valid software variants:

[0042]

[0043] Where P represents the combined feature vector, which is a vector generated based on the combination of multiple-choice, single-choice, and optional feature points; C represents the feature model vector, which is a pre-constructed feature model vector representing all variable features of the task planning software series; w P w C These are the weighting coefficients of the combined feature vector P and the feature model vector C in each dimension, used to adjust the importance of features in different dimensions; they are usually set to non-negative values, with larger values ​​indicating greater importance of that dimension; P i C iThese represent the values ​​of the combined feature vector P and the feature model vector C in the i-th dimension, respectively; they can be 0 or 1, where 1 indicates that the feature exists and 0 indicates that it does not exist.

[0044] N P N C These are the normalization factors for vectors P and C, respectively, aiming to normalize the vectors to a similar scale and avoid the influence of length differences on similarity. The value is taken as the L2 norm of the vector. Combination features with cosine similarity values ​​higher than a preset threshold are considered valid variants.

[0045] Step 4: Based on the call dependencies and priorities between components, develop a scheduling algorithm to optimize the component integration order. This algorithm uses an attention mechanism to calculate the importance weights of the specific order in which each component is integrated, and then constructs the final application. Specifically, this involves using an attention mechanism to calculate the importance weights of the specific order in which each component is integrated and constructing the final application.

[0046]

[0047] Where, α i,k W represents the attention weight for the k-th component at the i-th integration position; l U l ,Γ l For the model's learnable parameters; F i,k This represents the feature vector of the k-th component at the i-th integration position; the integration order of each component is determined based on the attention weights. Feature vector F i,k It includes various attribute information describing the component, including component type, function, call dependencies, and priority.

[0048] This invention also discloses a rapid construction device for task planning domain software based on feature variability, such as... Figure 2 As shown, it includes:

[0049] The basic functional component building module is used to build basic functional components to realize different plans; the basic functional components for different plans in the basic functional component building module include: rescue planning, supply planning, drone planning, vehicle planning and navigation planning.

[0050] Rescue planning includes planning rescue routes and allocating manpower and resources based on information such as the accident location, casualties, and on-site environment. It integrates the collaborative operation processes of various rescue forces (firefighting, medical, etc.) and considers emergency plans such as traffic control and evacuation. Supply planning includes planning the types and quantities of supplies based on disaster relief needs, considering storage, transportation, and distribution routes and schedules, or establishing a tracking and dispatch mechanism for supplies. Drone planning includes planning drone take-off and landing points, routes, and altitudes based on operational needs; integrating drones for reconnaissance, surveillance, and search, as well as handling data transmission, command control, and power management. Transportation planning includes planning vehicle dispatch based on the starting point, destination, and number of evacuees; arranging boarding points, boarding order, and transportation routes; considering vehicle types and passenger capacity. Navigation planning includes integrating Global Positioning System (GPS), Geographic Information System (GIS), etc.; providing accurate navigation services for various rescue vehicles and personnel and dynamically planning detour routes based on real-time traffic conditions.

[0051] The feature model construction module is used to construct the feature model of the task planning software series. Task types are set as multi-select feature points, method types as single-select feature points, and additional functions as optional feature points. The feature model includes: root feature: task planning software series; task types using multi-select feature points, including: rescue planning, supply planning, drone planning, vehicle planning, and navigation planning; method types using single-select feature points, including: constraint-based solving, heuristic algorithm-based, machine learning-based, and rule-based reasoning; additional functions using optional feature points, including: decision support, risk assessment, resource management, information sharing, 2D / 3D visualization, real-time monitoring, and report generation.

[0052] The software variant generation module is used to generate all valid software variants based on the constructed feature model by calculating the cosine similarity of multiple-choice, single-choice, and optional feature point combinations. Specifically, the calculation and generation of all valid software variants in the software variant generation module includes:

[0053]

[0054] Where P represents the combined feature vector, which is a vector generated based on the combination of multiple-choice, single-choice, and optional feature points; C represents the feature model vector, which is a pre-constructed feature model vector representing all variable features of the task planning software series; wP, w C These are the weighting coefficients of the combined feature vector P and the feature model vector C in each dimension, used to adjust the importance of features in different dimensions; they are usually set to non-negative values, with larger values ​​indicating greater importance of that dimension; P i C iThese represent the values ​​of the combined feature vector P and the feature model vector C in the i-th dimension, respectively; they can be 0 or 1, where 1 indicates that the feature exists and 0 indicates that it does not exist.

[0055] N P N C These are the normalization factors for vectors P and C, respectively, aiming to normalize the vectors to a similar scale and avoid the influence of length differences on similarity. The value is taken as the L2 norm of the vector. Combination features with cosine similarity values ​​higher than a preset threshold are considered valid variants.

[0056] The final application building block is used to develop a scheduling algorithm to optimize the component integration order based on the call dependencies and priorities between components. This scheduling algorithm uses an attention mechanism to calculate the importance weight of the specific position order of each component integration and builds the final application.

[0057] An attention mechanism is used to calculate the importance weights of the specific order in which each component is integrated, and the final application is built, specifically including:

[0058]

[0059] Where, α i,k W represents the attention weight for the k-th component at the i-th integration position; l U l ,Γ l For the model's learnable parameters; F i,k This represents the feature vector of the k-th component at the i-th integration position; the integration order of each component is determined based on the attention weights. Feature vector F i,k It includes various attribute information describing the component, including component type, function, call dependencies, and priority.

[0060] In summary, the above are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0061] It will be apparent to those skilled in the art that the embodiments of the present invention are not limited to the details of the exemplary embodiments described above, and that the embodiments of the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the embodiments of the present invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the embodiments of the present invention is defined by the appended claims rather than the foregoing description. Therefore, all variations falling within the meaning and scope of equivalents of the claims are intended to be encompassed within the embodiments of the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims. Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units, modules, or devices recited in the system, apparatus, or terminal claims may also be implemented by the same unit, module, or device through software or hardware. The terms "first," "second," etc., are used to indicate names and do not indicate any particular order.

[0062] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the embodiments of the present invention and are not intended to limit them. Although the embodiments of the present invention have been described in detail with reference to the above preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions to the technical solutions of the embodiments of the present invention should not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

[0063] In summary, the above are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0064] It will be apparent to those skilled in the art that the embodiments of the present invention are not limited to the details of the exemplary embodiments described above, and that the embodiments of the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the embodiments of the present invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the embodiments of the present invention is defined by the appended claims rather than the foregoing description. Therefore, all variations falling within the meaning and scope of equivalents of the claims are intended to be encompassed within the embodiments of the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims. Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units, modules, or devices recited in the system, apparatus, or terminal claims may also be implemented by the same unit, module, or device through software or hardware. The terms "first," "second," etc., are used to indicate names and do not indicate any particular order.

[0065] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the embodiments of the present invention and are not intended to limit them. Although the embodiments of the present invention have been described in detail with reference to the above preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions to the technical solutions of the embodiments of the present invention should not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for rapid construction of task planning domain software based on feature variability, characterized in that, Includes the following steps: Step 1: Construct the basic functional components to implement different plans; The basic functional components for different planning methods include: rescue planning, supply planning, drone planning, vehicle planning, and navigation planning. Step 2: Construct the feature model of the task planning software series, and set the task type as a multi-select feature point, the method type as a single-select feature point, and the additional functions as optional feature points. Step 3: Based on the constructed feature model, generate all valid software variants by calculating the cosine similarity of multiple-choice, single-choice, and optional feature point combinations; in Step 3, generating all valid software variants specifically includes: Where P represents the combined feature vector, which is a vector generated based on the combination of multiple-choice, single-choice, and optional feature points; C represents the feature model vector, which is a pre-constructed feature model vector representing all variable features of the task planning software series; w P w C These are the weighting coefficients of the combined feature vector P and the feature model vector C in each dimension, used to adjust the importance of features in different dimensions. They are set to non-negative values, with larger values ​​indicating greater importance of that dimension; P i C i These represent the values ​​of the combined feature vector P and the feature model vector C in the i-th dimension, respectively; they can be 0 or 1, where 1 indicates the feature exists and 0 indicates it does not exist; N P N C These are the normalization factors for vectors P and C, respectively. The purpose is to normalize the vectors to a similar scale and avoid the influence of length differences on similarity. The value is taken as the L2 norm of the vector. Step 4: Based on the calling dependencies and priorities between components, develop a scheduling algorithm to optimize the component integration order. The scheduling algorithm uses an attention mechanism to calculate the importance weight of the specific position order of each component integration and builds the final application.

2. The method for rapid construction of task planning domain software based on feature variability according to claim 1, characterized in that: Features with a cosine similarity value higher than a preset threshold are considered valid variants.

3. The method for rapid construction of task planning domain software based on feature variability according to claim 2, characterized in that: An attention mechanism is used to calculate the importance weights of the specific order in which each component is integrated, and the final application is built, specifically including: Where, α i,k W represents the attention weight for the k-th component at the i-th integration position; l U l ,Γ l For the model's learnable parameters; F i,k This represents the feature vector of the k-th component at the i-th integration position; the integration order of each component is determined based on the attention weights.

4. The method for rapid construction of task planning domain software based on feature variability according to claim 3, characterized in that: eigenvector F i,k This includes various attribute information describing the component.

5. The method for rapid construction of task planning domain software based on feature variability according to claim 4, characterized in that: The attribute information includes component type, function, call dependencies, and priority.

6. A rapid construction device for task planning domain software based on feature variability, characterized in that, include: The basic functional component building module is used to build basic functional components to implement different plans; The basic functional components for different planning methods include: rescue planning, supply planning, drone planning, vehicle planning, and navigation planning. The feature model building module is used to build feature models for the task planning software series, setting task type as multi-select feature points, method type as single-select feature points, and additional functions as optional feature points. The software variant generation module is used to generate all valid software variants based on the constructed feature model by calculating the cosine similarity of multiple-choice, single-choice, and optional feature point combinations; the calculation and generation of all valid software variants specifically includes: Where P represents the combined feature vector, which is a vector generated based on the combination of multiple-choice, single-choice, and optional feature points; C represents the feature model vector, which is a pre-constructed feature model vector representing all variable features of the task planning software series; w P w C These are the weighting coefficients of the combined feature vector P and the feature model vector C in each dimension, used to adjust the importance of features in different dimensions. They are set to non-negative values, with larger values ​​indicating greater importance of that dimension; P i C i These represent the values ​​of the combined feature vector P and the feature model vector C in the i-th dimension, respectively; they can be 0 or 1, where 1 indicates the feature exists and 0 indicates it does not exist; N P N C These are the normalization factors for vectors P and C, respectively. The purpose is to normalize the vectors to a similar scale and avoid the influence of length differences on similarity. The value is taken as the L2 norm of the vector. The final application building block is used to develop a scheduling algorithm to optimize the component integration order based on the call dependencies and priorities between components. This scheduling algorithm uses an attention mechanism to calculate the importance weight of the specific position order of each component integration and builds the final application.

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