An AI-based software intelligent development system

Abstract

This invention discloses an AI-based intelligent software development system, belonging to the field of software development technology. It aggregates user feedback data to obtain potential functional requirements, analyzes and determines supplementary conditions for each potential functional requirement, analyzes the corresponding potential functional requirements based on the supplementary conditions, obtains target functional requirements, supplements conditions based on each target functional requirement, and adjusts the preset user development scope. The system reviews development requirements based on the user development scope, obtaining corresponding review results. When the review result is approved, users are allowed to develop functions according to the requirements, obtaining user application functions. When the review result is unapproved, it determines whether to generate requirement feedback data based on user needs. This effectively solves the core pain points of traditional laundry back-end management systems, such as fixed functions, insufficient intelligence, and lack of personalized services, and is more adaptable to changes in user needs.

Description

Technical Field

[0001] This invention belongs to the field of software development technology, specifically an AI-based intelligent software development system. Background Technology

[0002] In the traditional laundry industry's back-end management scenarios, existing systems generally suffer from rigid functions, low levels of intelligence, and difficulty in meeting personalized service needs. Current mainstream laundry back-end management systems are mostly designed around basic business processes, such as order receipt, service classification, and price calculation. Their core logic relies on manual operation and matching with preset rules, lacking dynamic adjustment capabilities. For example, when faced with special fabrics (such as silk and wool) or stubborn stains (such as oil stains and red wine stains), the system can only provide standardized washing options and cannot automatically recommend optimal process parameters based on garment material, stain type, and customer preferences. This forces service personnel to rely on experience for manual handling, which is not only inefficient but also prone to fluctuations in service quality due to operational differences. Meanwhile, consumer demand for laundry services is upgrading from "basic cleaning" to "personalization, transparency, and convenience." Surveys show that over 65% of users want the system to track order status in real time (such as washing progress and delivery location) and to customize service requests (such as "expedited processing" and "use of hypoallergenic detergent") via mobile devices. However, existing systems lack intelligent interaction and dynamic decision-making capabilities, failing to support users' proactive participation in service customization, resulting in delayed response times and limited improvement in customer satisfaction.

[0003] In order to solve the above problems, this invention provides an AI-based intelligent software development system. Summary of the Invention

[0004] To address the problems of the above solutions, this invention provides an AI-based intelligent software development system.

[0005] The objective of this invention can be achieved through the following technical solutions: An AI-based intelligent software development system, comprising a platform and a user side; Furthermore, the platform establishes communication connections with each user terminal.

[0006] The platform includes a basic module, a requirements analysis module, and... The basic modules are used to develop various basic functions.

[0007] The requirement analysis module is used to perform requirement analysis and processing, acquire requirement feedback data from each user in real time, summarize the requirement feedback data from each user to obtain each potential functional requirement, analyze and determine the supplementary condition data for each potential functional requirement, analyze the corresponding potential functional requirement based on each supplementary condition data to obtain each target functional requirement, supplement the conditions based on each target functional requirement, adjust the preset user development scope, and send the user development scope to each user terminal.

[0008] Furthermore, based on the supplementary data, the corresponding potential functional requirements are analyzed, including: Based on the supplementary data, each potential functional requirement is classified to obtain the category of each requirement; Each requirement category is assessed to obtain corresponding assessment results, which include assessment pass and assessment fail. The requirements that pass the requirements assessment will be categorized and presented to the platform, which will then select the corresponding potential functional requirements as target functional requirements.

[0009] Furthermore, based on the supplementary data, the potential functional requirements are categorized, including: Intersection identification is performed on the supplementary condition data of each potential functional requirement to obtain various intersection conditions. An intersection condition list is formed based on these conditions, and the intersection condition list is arranged and combined to form several simulated sequences. Classification simulations are performed based on each simulated sequence to obtain the simulated classification results corresponding to each simulated sequence. Based on the results of each simulation classification, priority analysis is performed on the simulation sequences. The simulation sequence with the highest priority is marked as the target sequence, and each simulation classification corresponding to the target sequence is marked as the demand classification.

[0010] Furthermore, classification simulations are performed based on the simulated sequences, including: Step SA1: Mark the intersection condition that ranks first in the simulation sequence as the baseline condition, classify all potential functional requirements including the baseline condition into one category to obtain a simulation classification; remove the intersection conditions corresponding to the simulation classification from the simulation sequence; Step SA2: Repeat step SA1 until there are no intersection conditions in the simulated sequences, and form the simulated classification result corresponding to the simulated sequence based on the obtained simulated classifications.

[0011] Furthermore, priority analysis is performed on the simulated sequences based on the results of each simulated classification, including: Demand assessment is performed on each simulated category corresponding to the simulated classification results to obtain the demand assessment results for each simulated category. The proportion of users corresponding to each simulated category whose demand assessment results are qualified is identified and marked as the priority value of the corresponding simulated sequence; The simulation sequences are sorted by priority from highest to lowest priority value.

[0012] Furthermore, a needs assessment is conducted for simulated classification, including: Count the total number of users corresponding to all potential functional requirements and mark it as the total number of users who provided feedback; identify the number of users corresponding to each potential functional requirement and mark it as the number of users who requested the feature. Based on the intersection conditions corresponding to the simulated classification, the supplementary cost is estimated, and each potential functional requirement corresponding to the simulated classification is identified. Each potential functional requirement, the number of users with the requirement, the total number of users who provided feedback, and the supplementary cost are integrated into the requirement assessment data. The requirement assessment data is analyzed through a preset requirement assessment model to obtain the corresponding requirement assessment results.

[0013] Furthermore, the expression for the demand assessment model is: ; In the formula: s is the input data, representing the requirement assessment data; s is abnormal data, indicating that the requirement assessment data does not meet the supplementary optimization requirements; the output data is the requirement assessment value XP(s), which is 1 or 0. When the demand assessment value is 1, the demand assessment result is unqualified. When the demand assessment value is 0, the demand assessment result is qualified.

[0014] The user terminal includes an AI development module; The AI ​​development module is used to develop functions according to development needs, obtain user development needs, review development needs according to user development scope, and obtain corresponding review results, including approval and rejection. When the review result is approved, users are allowed to develop functions according to their development needs and obtain the corresponding user application functions. When the review result is "approved", determine whether to generate feedback data based on user needs. If feedback data is generated, send it to the platform.

[0015] Compared with the prior art, the beneficial effects of the present invention are: The AI-based intelligent software development system provided by this invention effectively solves the core pain points of traditional laundry back-end management systems, such as fixed functions, insufficient intelligence, and lack of personalized services, by deeply integrating machine learning algorithms and low-code development technology; and it can better adapt to changes in user needs. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a block diagram illustrating the principle of the present invention. Detailed Implementation

[0018] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0019] like Figure 1 As shown, an AI-based intelligent software development system includes a platform and a user terminal. The platform establishes communication connections with each user terminal.

[0020] The platform includes a basic module and a requirements analysis module; The basic module is used by platform staff to develop various basic functions for the washing machine, such as various washing modes, timer function, reminder function, recording function, revenue function, pricing function, membership function, and other basic functions required by the platform; it is developed based on existing development technologies.

[0021] The requirement analysis module is used to acquire user feedback data in real time, including development requirements, functional usage requirements, and other data that can indicate user needs. It summarizes the user feedback data to obtain potential functional requirements and identifies them based on the feedback data. It analyzes the hardware and software conditions required for users to develop each potential functional requirement using AI, compares them with existing conditions, and obtains supplementary condition data for each potential functional requirement. If the current conditions are met, the supplementary condition data is zero, and is determined based on actual development needs. For example, if a user wants to remotely control a washing machine to select a washing mode, the washing machine needs to be able to perform the corresponding function after the user develops it using AI. Based on this, supplementary condition data is determined to indicate how adjustments need to be made to achieve the above goal. The module analyzes each potential functional requirement based on the supplementary condition data to obtain target functional requirements, supplements the conditions for each target functional requirement, and adjusts the preset user development scope. The user development scope indicates the range within which users are allowed to develop based on AI; that is, users can only develop within the user development scope.

[0022] In one embodiment, the preset user development scope is determined based on the current actual conditions, that is, the development scope that the current conditions allow and can achieve is marked as the user development scope; moreover, the platform can also narrow down the above development scope according to needs to form the user development scope.

[0023] In one embodiment, for potential functional requirements where supplementary condition data is absent, the platform provider manually determines the requirement. That is, the condition is met, but because other platform management requirements cannot be met, it needs to be manually set and does not participate in the analysis and determination of subsequent target functional requirements.

[0024] In one embodiment, the analysis of corresponding potential functional requirements based on various supplementary condition data includes: Each potential functional requirement is categorized based on the supplementary condition data, resulting in different requirement categories. The categorization is further based on whether the same supplementary condition can simultaneously satisfy the corresponding potential functional requirement. If so, they are grouped into one category. For example, potential functional requirements A and B may have supplementary condition data with conditions 1 and 2, and conditions 2 and 3, respectively. Since conditions 1, 2, and 3 alone can fulfill the corresponding potential functional requirement, this means that the potential functional requirement can be fulfilled through two supplementary conditions, 1 and 2. Because potential functional requirements A and B share condition 2, they meet the classification requirements and are grouped into one category. Subsequent assignment of other potential functional requirements to this category also requires condition 2, as this classification is based on condition 2. If condition B is 1, 2, and 3, it is classified based on conditions 1 and 2. If condition C is 1, then condition 1 can be used as the basis for forming an ABC classification. This process continues, using the above method to perform intelligent classification based on existing technology. Each requirement category is assessed to obtain corresponding assessment results, which include assessment pass and assessment fail. The requirements that pass the requirements assessment will be categorized and presented to the platform, which will then select the corresponding potential functional requirements as target functional requirements.

[0025] In one embodiment, the potential functional requirements are categorized based on various supplementary condition data, including: Intersection identification is performed on the supplementary condition data for each potential functional requirement to obtain various intersection conditions. If there is no intersection, then there are no intersection conditions. Intersection identification is performed using a single condition or combination of conditions that can realize the corresponding potential functional requirement as the element. For example, if condition element A consists of three single conditions 1, 2, and 3, the intersection is identified based on A, not on condition 1. If the two supplementary condition data are ABC and BCD, then the intersection conditions are BC, B, and C, thus determining the various intersection conditions. The various intersection conditions are counted to form an intersection condition list. Based on the intersection condition list, several simulated sequences are formed. For example, if the intersection condition list has three intersection conditions ABC, the simulated sequences obtained by permutation and combination are ABC, ACB, BAC, BCA, CAB, and CBA. Since the classification results corresponding to different orders may be different, permutation and combination are performed according to existing mathematical methods. Classification simulations are performed based on each simulated sequence to obtain the simulated classification results corresponding to each simulated sequence. Based on the results of each simulation classification, priority analysis is performed on the simulation sequences. The simulation sequence with the highest priority is marked as the target sequence, and each simulation classification corresponding to the target sequence is marked as the demand classification.

[0026] In one embodiment, classification simulation based on a simulated sequence includes: Step SA1: Mark the intersection condition that ranks first in the simulation sequence as the baseline condition, classify each potential functional requirement including the baseline condition into one category, and obtain a simulation classification; remove the intersection condition corresponding to the simulation classification from the simulation sequence; for example, if the intersection condition and order of the simulation sequence are ABC, after classifying with A as the baseline condition, remove A, and it becomes BC, and B will be ranked first next time. Step SA2: Repeat step SA1 until there are no intersection conditions in the simulated sequences, and form the simulated classification result corresponding to the simulated sequence based on the obtained simulated classifications.

[0027] In one embodiment, prioritization analysis of the simulated sequences is performed based on the results of each simulated classification, including: Demand assessment is performed on each simulated category corresponding to the simulated classification results to obtain the demand assessment results for each simulated category. The proportion of users corresponding to each simulated category who pass the demand assessment is identified and marked as the priority value of the simulated sequence; this proportion refers to the ratio between the sum of all users corresponding to the corresponding simulated category and the total number of feedback users; The simulation sequences are sorted by priority from highest to lowest priority value.

[0028] In one embodiment, priority can also be assigned based on parameters such as the expected supplementary effects and costs of each qualified simulation category.

[0029] In one embodiment, a requirements assessment for simulated classification includes: Count the total number of users corresponding to all potential functional requirements and mark it as the total number of users who have provided feedback; identify the number of users corresponding to each potential functional requirement and mark it as the number of users with the requirement, which refers to the number of users with that potential functional requirement. Based on the intersection conditions corresponding to the simulated classification, the supplementary cost is estimated, and each potential functional requirement corresponding to the simulated classification is identified. Each potential functional requirement, the number of users with the requirement, the total number of users who provided feedback, and the supplementary cost are integrated into the requirement assessment data. The requirement assessment data is analyzed through a preset requirement assessment model to obtain the corresponding requirement assessment results; that is, to assess whether it meets the supplementary optimization requirements.

[0030] In one embodiment, the demand assessment model can be built based on machine learning, deep learning algorithms, etc., and trained by the platform provider using a labeled training set.

[0031] In one embodiment, the expression for the demand assessment model is: ; In the formula: s is the input data, representing the demand assessment data; s is the abnormal data, indicating that the demand assessment data does not meet the supplementary optimization requirements; the output data is the demand assessment value XP(s), which is 1 or 0; the training is performed using the training set set by the platform. When the demand assessment value is 1, the demand assessment result is unqualified. When the demand assessment value is 0, the demand assessment result is qualified.

[0032] For example, the training set can be labeled as follows, and the requirements assessment model can be set up as follows.

[0033] Calculate the ratio between the sum of the number of users requesting each potential functional requirement and the total number of users providing feedback, and label it as the requirement feedback value; The evaluation value is calculated based on a preset formula, which is: Assessment value = Demand feedback value ÷ Replenishment cost; The evaluation is considered successful if the assessed value is greater than the preset value, otherwise it is considered unsuccessful.

[0034] In one embodiment, a demand assessment is performed on each demand category, following the same method used for assessing the demand of the simulated categories as described above. Since the demand assessment results for the simulated categories are already available, they can be obtained directly.

[0035] The user terminal includes an AI development module; The AI ​​development module is used to develop functions according to development needs, obtain user development needs, review development needs through the user's development scope, determine whether the development needs are within the user's development scope, and obtain the corresponding review results, including approval and disapproval. When the review result is approved, users are allowed to develop functions according to their development needs and obtain the corresponding user application functions. When the review result is "approved", determine whether to generate feedback data based on user needs. If feedback data is generated, send it to the platform.

[0036] In one embodiment, user development requirements can be submitted by the user throughout the entire development process. For example, a summary can be submitted before development, and development requirements can be identified based on the development results after development. That is, users can develop based on AI in advance. If the requirements are not met, the application cannot be used, but it can be saved. When it falls within the user's development scope, development can be allowed. Specifically, development can be carried out based on existing AI-based software development technologies, or it can be carried out by connecting to existing development platforms.

[0037] In one embodiment, development requirements are reviewed based on the user's development scope, based on existing judgment methods, or based on intelligent models built using machine learning, etc.

[0038] The above formulas are all numerical calculations after removing dimensions. The formulas are obtained by software simulation based on a large amount of data and are closest to the real situation. The preset parameters and preset thresholds in the formulas are set by those skilled in the art according to the actual situation or obtained by simulation based on a large amount of data.

[0039] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. An AI-based intelligent software development system, characterized in that, Including both the platform side and the user side; The platform includes a basic module and a requirements analysis module; the user terminal includes an AI development module. The basic modules are used to develop various basic functions; The requirement analysis module is used to perform requirement analysis and processing, acquire requirement feedback data from each user in real time, summarize the requirement feedback data from each user to obtain each potential functional requirement, analyze and determine the supplementary condition data for each potential functional requirement, analyze the corresponding potential functional requirement based on each supplementary condition data to obtain each target functional requirement, supplement the conditions based on each target functional requirement, adjust the preset user development scope, and send the user development scope to each user terminal. The AI ​​development module is used to develop functions according to development needs, obtain user development needs, review development needs according to user development scope, and obtain corresponding review results, including approval and rejection. When the review result is approved, users are allowed to develop functions according to their development needs and obtain the corresponding user application functions. When the review result is "approved", determine whether to generate feedback data based on user needs. If feedback data is generated, send it to the platform.

2. The AI-based intelligent software development system according to claim 1, characterized in that, The platform establishes communication connections with each user terminal.

3. The AI-based intelligent software development system according to claim 1, characterized in that, Based on the supplementary data, the corresponding potential functional requirements are analyzed, including: Based on the supplementary data, each potential functional requirement is classified to obtain the category of each requirement; Each requirement category is assessed to obtain corresponding assessment results, which include assessment pass and assessment fail. The requirements that pass the requirements assessment will be categorized and presented to the platform, which will then select the corresponding potential functional requirements as target functional requirements.

4. The AI-based intelligent software development system according to claim 3, characterized in that, Based on the supplementary data, the potential functional requirements are categorized, including: Intersection identification is performed on the supplementary condition data of each potential functional requirement to obtain various intersection conditions. An intersection condition list is formed based on these conditions, and the intersection condition list is arranged and combined to form several simulated sequences. Classification simulations are performed based on each simulated sequence to obtain the simulated classification results corresponding to each simulated sequence. Based on the results of each simulation classification, priority analysis is performed on the simulation sequences. The simulation sequence with the highest priority is marked as the target sequence, and each simulation classification corresponding to the target sequence is marked as the demand classification.

5. The AI-based intelligent software development system according to claim 4, characterized in that, Classification simulations based on simulated sequences include: Step SA1: Mark the intersection condition that ranks first in the simulation sequence as the baseline condition, classify all potential functional requirements including the baseline condition into one category to obtain a simulation classification; remove the intersection conditions corresponding to the simulation classification from the simulation sequence; Step SA2: Repeat step SA1 until there are no intersection conditions in the simulated sequences, and form the simulated classification result corresponding to the simulated sequence based on the obtained simulated classifications.

6. The AI-based intelligent software development system according to claim 4, characterized in that, Prioritization analysis of the simulated sequences is performed based on the results of each simulated classification, including: Demand assessment is performed on each simulated category corresponding to the simulated classification results to obtain the demand assessment results for each simulated category. The proportion of users corresponding to each simulated category whose demand assessment results are qualified is identified and marked as the priority value of the corresponding simulated sequence; The simulation sequences are sorted by priority from highest to lowest priority value.

7. The AI-based intelligent software development system according to claim 6, characterized in that, A needs assessment for simulated classification is conducted, including: Count the total number of users corresponding to all potential functional requirements and mark it as the total number of users who provided feedback; identify the number of users corresponding to each potential functional requirement and mark it as the number of users who requested the feature. Based on the intersection conditions corresponding to the simulated classification, the supplementary cost is estimated, and each potential functional requirement corresponding to the simulated classification is identified. Each potential functional requirement, the number of users with the requirement, the total number of users who provided feedback, and the supplementary cost are integrated into the requirement assessment data. The requirement assessment data is analyzed through a preset requirement assessment model to obtain the corresponding requirement assessment results.

8. The AI-based intelligent software development system according to claim 7, characterized in that, The expression for the demand assessment model is: ； In the formula: s is the input data, representing the requirement assessment data; s is abnormal data, indicating that the requirement assessment data does not meet the supplementary optimization requirements; the output data is the requirement assessment value XP(s), which is 1 or 0. When the demand assessment value is 1, the demand assessment result is unqualified. When the demand assessment value is 0, the demand assessment result is qualified.

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