Software development promotion effect prediction method based on artificial intelligence

Abstract

The invention relates to the technical field of software promotion effect prediction, and discloses a software development promotion effect prediction method based on artificial intelligence, comprising the following steps: S1, collecting historical data of software promotion activities, the data including but not limited to user growth, marketing investment, user behavior, social media feedback and external environment data; s2, preprocessing the data, including denoising, missing value filling, feature extraction and standardization; and S3, establishing an evolution process of the promotion effect based on the nonlinear dynamic model. Through combination of a quantum approximate optimization algorithm, an optimal control theory, a nonlinear dynamic model, a chaos theory and a dynamic planning method, an efficient optimization popularization effect is realized, a local optimal solution can be avoided, market fluctuation and mutation phenomena can be accurately predicted, a popularization strategy is accurately optimized under multiple constraint conditions, and the popularization effect is maximized.

Description

Technical Field

[0001] The present invention relates to the technical field of software promotion effect prediction, and particularly to a method for predicting software development promotion effect based on artificial intelligence. Background Art

[0002] With the rapid development of the software market and the intensification of competition, software promotion activities have become an important means to increase the user base and market share. To improve the effectiveness of promotion activities, the industry generally adopts data-driven decision-making models to predict and optimize promotion effects. However, the existing technologies mainly focus on traditional rule-based prediction methods, which have certain limitations and cannot meet the requirements of the complex and dynamic market environment in the software promotion process.

[0003] Traditional promotion effect prediction methods mostly rely on statistical models and machine learning algorithms, such as linear regression, decision trees, support vector machines, etc. These methods establish the relationship between promotion effects and various factors through historical data. However, these methods usually assume that the relationship between data is linear, or can only capture simple correlations, ignoring the complex non-linear relationship between promotion effects and the market environment. Especially when faced with large-scale user behavior data, the prediction ability and adaptability of existing methods are insufficient, often resulting in large errors in promotion effects. Therefore, it cannot accurately reflect the interaction and non-linear feedback of multi-dimensional factors in the software promotion process.

[0004] In addition, the optimization methods in the existing technologies usually rely on classical local search algorithms, such as gradient descent method and genetic algorithm, etc. Although these methods can provide effective solutions in simple optimization problems, they are prone to falling into local optimal solutions in multi-dimensional complex promotion effect optimization problems. This is particularly important for the resource allocation problem in promotion activities. The promotion effect not only depends on the action of a single factor, but on the interweaving and interaction of multiple factors (such as advertising investment, promotional activities, user behavior, etc.). It is difficult for traditional methods to find the optimal solution in such a complex solution space. Therefore, the existing technologies often show low efficiency and poor global optimization ability in promotion effect optimization, and cannot effectively improve the overall effect of promotion activities.

[0005] In addition, existing promotion effect prediction models generally lack sufficient consideration of market fluctuations and mutation phenomena. Factors such as the market environment, user behavior, and the dynamics of competitors often exhibit complex non-linear characteristics, and the rapid changes in the market may lead to sudden changes in the promotion effect. For example, sudden changes in the external environment, such as fluctuations in user demand and new strategies of competitors, may have a huge impact on the promotion effect. Most of the existing technologies rely on static modeling and fail to effectively predict and respond to these mutation phenomena. Especially when dealing with sudden market events, traditional models cannot adjust promotion strategies in real time, resulting in unstable promotion effects and waste of resources. Therefore, there are significant deficiencies in the flexibility and adaptability of existing technologies in dealing with sudden market changes.

[0006] Moreover, the existing technologies usually handle time factors in a relatively simplified manner, ignoring the time delay and discount effect of the promotion effect. In software promotion activities, the inputs and effects at different time points have different values. For example, the current advertising investment may have a greater impact on the recent user growth, while the future advertising investment may be more important for the long-term user growth. The existing technologies lack an effective time discount mechanism and usually regard the return of the promotion effect as instantaneous, failing to fully consider the present value of future effects, resulting in the over-pursuit of short-term effects of promotion activities and the neglect of the optimization of long-term effects.

[0007] Finally, most traditional optimization methods cannot effectively handle multiple constraint conditions. For example, the budget, time, resources, etc. in software promotion activities are often subject to various restrictions. Existing technologies mostly focus on maximizing the optimization effect but ignore the impact of resource constraints. Promotion activities are often restricted by multiple resources such as budget, time, and personnel. Traditional methods fail to effectively combine these constraint conditions for optimization, resulting in unrealistic implementation of promotion strategies and low resource utilization efficiency. Summary of the Invention

[0008] In view of the deficiencies of the existing technologies, the present invention provides an artificial intelligence-based software development promotion effect prediction method, which solves the problems that existing software promotion effect prediction methods cannot accurately handle non-linear relationships, market mutations, and resource constraints.

[0009] To achieve the above objectives, the present invention is realized through the following technical solutions: An artificial intelligence-based software development promotion effect prediction method includes the following steps:

[0010] S1. Collect historical data of software promotion activities, and the data includes but is not limited to user growth, marketing investment, user behavior, social media feedback, and external environment data;

[0011] S2. Preprocess the data, including denoising, filling in missing values, feature extraction, and standardization;

[0012] S3. Establish the evolution process of the promotion effect based on a non-linear dynamics model;

[0013] S4. Introduce chaos theory, model the complex behavior of the promotion effect through chaotic systems such as the Lorenz equation, simulate the sensitivity of the system to initial conditions, and identify potential stable intervals and mutation phenomena;

[0014] S5. Solve the optimization problem of the promotion effect based on the quantum approximate optimization algorithm, perform parallel computing on the multi-dimensional promotion effect through a quantum circuit, and obtain the global optimal solution;

[0015] S6. Analyze the critical phenomena in the promotion process based on the self-organized criticality theory, simulate the mutation of the promotion effect caused by market fluctuations or other external factors, and predict the behavior of the system when it enters the critical point;

[0016] S7. Apply the optimal control theory, solve the optimal control strategy through dynamic programming to maximize the promotion effect, and optimize the strategy of the promotion activity in real time.

[0017] Preferably, the S3 step specifically includes the following steps:

[0018] S3.1. Establish a state equation based on the multi-dimensional characteristics of software promotion to describe the dynamic change process of the promotion effect;

[0019] S3.2. Consider the influence of external control factors on the promotion effect and introduce them as control variables into the system;

[0020] S3.3. Introduce a random perturbation term to simulate external uncertainty factors in the promotion effect, such as market fluctuations and the randomness of user behavior.

[0021] Preferably, the external control factors in the S3.2 step include marketing budget, advertising investment, promotion activity intensity, user feedback, seasonal factors, and the promotion activities of competitors.

[0022] Preferably, the S4 step specifically includes the following steps:

[0023] S4.1. Use the Lorenz equation to model the change process of the promotion effect, where there is a high degree of non-linear coupling between multiple dimensions of the promotion effect;

[0024] S4.2. Solve the Lorenz equation by numerical methods to identify the possible chaotic behavior of the system and its influence on the promotion effect;

[0025] S4.3. Evaluate the sensitivity of the promotion effect under different initial conditions through chaos analysis and simulate the evolution of the promotion effect in different market environments.

[0026] Preferably, in the S5 step, the quantum approximate optimization algorithm is used to solve the promotion effect optimization problem, and the objective function of the optimization problem is:

[0027]

[0028] where is the optimization objective function, representing the total loss in the optimization process, f(x) is the promotion effect prediction error, and g(x) is the constraint condition, representing the resource limitation of the system or other actual constraints.

[0029] Preferably, in the S6 step, the steps of analyzing the critical phenomenon in the promotion process based on the self-organized criticality theory are as follows:

[0030] S6.1. Establish a dynamic model of the promotion effect and identify the key factors affecting the promotion effect;

[0031] S6.2. By simulating the critical behavior of the system, identify the critical point in the promotion process, and avoid the system from entering the critical state by adjusting the promotion strategy;

[0032] S6.3. According to market fluctuations or other external factors, predict the mutation phenomenon of the promotion effect and adjust the promotion strategy in real time.

[0033] Preferably, in the S7 step, the optimal control theory optimizes the promotion strategy through the following steps:

[0034] S7.1. Set the optimization objective of the promotion effect, define the cost function and consider the time discount factor;

[0035] S7.2. Use the dynamic programming method to solve the optimal control strategy and minimize the cost of the promotion effect according to the objective function;

[0036] S7.3. Adjust the various parameters in the promotion activity according to the optimal control strategy to maximize the promotion effect.

[0037] Preferably, the cost function in the S7.1 step is:

[0038]

[0039] where is the cost function at each moment, describing the relationship between the promotion effect and the control strategy, ρ is the discount factor, T is the optimization time window, x(t) is the state vector of the promotion effect, table, u(t) is the external control signal or control variable, and dt is the time increment in the time integration.

[0040] The present invention provides a method for predicting the promotion effect of software development based on artificial intelligence. It has the following beneficial effects:

[0041] 1. By adopting the technical solution of combining the quantum approximate optimization algorithm with the optimal control theory, the present invention achieves the technical effect of efficiently solving complex multi-dimensional promotion effect optimization problems. In traditional promotion optimization technologies, classical algorithms often rely on local search methods and are prone to the problem of falling into local optimal solutions. In contrast, the present invention introduces the quantum approximate optimization algorithm, which combines the superposition state of quantum bits and the characteristics of quantum entanglement, can perform parallel computing in the multi-dimensional solution space, significantly improves the optimization efficiency, and avoids the limitations of local optimal solutions. The introduction of the quantum approximate optimization algorithm not only greatly shortens the time required for optimization, but also enhances the adaptability to complex non-linear problems, enabling the promotion effect optimization to be more accurately connected with the actual market environment, making the strategy optimization of promotion activities more flexible and dynamic, and capable of responding to market changes in real time.

[0042] 2. By combining the non-linear dynamics model with the chaos theory and analyzing the critical phenomena in the promotion process, the present invention achieves the technical effect of accurately predicting the mutation points and market fluctuations. Most of the existing promotion effect models fail to fully consider market fluctuations and the mutation phenomena of the system, and such changes often have an important impact on the promotion effect. By introducing the self-organized criticality theory, the present invention can simulate the critical points in the promotion process and effectively identify the occurrence of sudden market fluctuations. By modeling the promotion effect through the chaos theory and the Lorenz equation, the present invention realizes the accurate grasp of the non-linearity and sensitive dependence of the system, enabling the promotion activities to adjust strategies in a timely manner when market conditions change. Compared with traditional methods, the present invention can capture the precursors of unexpected events in real time during the promotion activities, avoiding the large fluctuations in the promotion effect caused by untimely emergency responses.

[0043] 3. By applying the dynamic programming method to optimize various control variables in the promotion activities, the present invention achieves the technical effect of maximizing the promotion effect under the constraint of limited resources. Traditional promotion strategy optimization usually relies on empirical judgment and manually adjusts promotion input and strategies, lacking systematicness and accuracy. In contrast, the present invention optimizes various decisions in the promotion strategy through the optimal control theory in combination with dynamic programming. Under the constraint conditions, the dynamic programming method can automatically deduce the optimal control strategy at each time point, adjust various variables in the promotion activities in real time to ensure the maximization of the promotion effect. In practical applications, the present invention can handle various constraint conditions and complex market environments, accurately optimize resource allocation, improve the efficiency and effectiveness of promotion activities, avoid resource waste in traditional methods, and achieve the optimal allocation of resources and the maximization of promotion effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a flowchart of the method of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0045] Next, in combination with the accompanying drawings of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0046] Please refer to the attached Figure 1 , the embodiments of the present invention provide an artificial intelligence-based software development promotion effect prediction method, including the following steps:

[0047] S1. Collect historical data of software promotion activities, and the data includes but is not limited to user growth, marketing investment, user behavior, social media feedback, and external environment data;

[0048] In this embodiment, the data collection process includes but is not limited to the following types of data:

[0049] User growth data: These data are usually composed of measurement values such as user registration, installation volume, or active user numbers in promotion activities. User growth data can reflect the effect of promotion activities and the change of market acceptance. Specifically, user growth data can be recorded in the form of time series, such as the number of new users per day or per week. The collection of this data can provide a quantitative basis for subsequent analysis of promotion effects and market dynamics.

[0050] Marketing investment data: Marketing investment usually includes expenditures on advertising costs, promotion activities, cooperative marketing, etc. Specifically, the classification of advertising costs includes but is not limited to online advertising costs, television advertising costs, print advertising costs, etc. Promotion cost data can help analyze the relationship between investment and effect and evaluate the input-output ratio of marketing.

[0051] User behavior data: These data usually involve the behavior of users during the use of software, specifically including the operation behavior of users in the application (such as click-through rate, stay time, bounce rate, etc.), purchase behavior (such as subscription, payment, etc.), and social behavior (such as comments, sharing, etc.). By analyzing these behavior data, the matching degree between user needs and promotion strategies can be understood more precisely.

[0052] Social media feedback data: With the popularization of social platforms, user feedback on social media (such as user comments, number of likes, number of shares, sentiment analysis results, etc.) has become an important reference for measuring promotion effects. Social media feedback data can reflect the attitude and emotional fluctuations of users towards the software and evaluate the immediate response of market trends and promotion effects.

[0053] External environment data: External environment data includes factors such as macroeconomics, market competition, and seasonal changes. These external factors have a significant impact on the software promotion effect, especially in a dynamic market environment. For example, during certain periods, user behavior may change due to holidays or major events, thus affecting the promotion effect. Therefore, external environment data also needs to be included in the scope of data collection.

[0054] In this embodiment, data collection usually adopts technologies such as automated scripts, API interface calls, and data crawlers to collect this data from multiple data sources. For example, user growth data can be obtained through the API interfaces of platforms such as app stores and website traffic analysis tools (such as Google Analytics), marketing investment data can be obtained through interface docking with advertising platforms (such as Google Ads, Facebook Ads), and user behavior data can be obtained through in-app behavior analysis tools (such as Mixpanel, Firebase).

[0055] As an option, when collecting social media feedback data, information such as posts, comments, likes, and shares published by users can be obtained through the open API interfaces of social platforms (such as Twitter API, Facebook Graph API), and at the same time, sentiment analysis technology is used to classify the sentiment tendencies of comments (such as positive, negative, neutral).

[0056] Data collection can be obtained through market research reports, public databases, industry trend analysis, etc. These data usually need to be collected manually or by purchasing industry data reports. Specific external environment data may include global economic indices, consumer confidence indices, market activities of competitors, etc. These data provide macro market change information for the model, enabling the model to predict the software promotion effect in a larger context.

[0057] In a possible implementation, all collected data will undergo data integration and cleaning to ensure data consistency and validity. First, align and standardize data from different sources to ensure that all data has the same timestamp and unit. For example, when collecting user growth and advertising investment data, it is necessary to ensure that the time range is consistent, and the time periods of different data sources may need to be unified. Then, use data cleaning technologies such as removing duplicate data, filling in missing values, and removing outliers to ensure the high quality of the data.

[0058] Specifically, when filling in missing values, multiple methods are adopted, and a suitable filling method is selected according to the type of missing data. For example, for time series data, linear interpolation or regression methods based on historical data are often used to fill in missing values; while for categorical data, pattern recognition methods may be used to fill in missing data.

[0059] As an option, if it is difficult to obtain some important external environmental data, it can be considered to infer through existing public data sources or generate estimated values through simulation. For sentiment analysis of social media feedback data, natural language processing techniques (such as sentiment dictionaries, machine learning methods, etc.) are used to analyze the sentiment tendency of comments and quantify user feedback.

[0060] In some embodiments, all collected historical data will be stored in a database, usually a relational database (such as MySQL, PostgreSQL) or a non-relational database (such as MongoDB). Through appropriate database indexing and query optimization, these large amounts of data can be efficiently stored and retrieved. To further improve data processing capabilities, the system may use big data technologies (such as Hadoop, Spark) for distributed data storage and processing to support high-concurrency data access and processing requirements.

[0061] In another implementation, by combining real-time data stream processing and batch processing technologies, data is collected in real time and processed through a stream processing framework (such as Apache Kafka, Apache Flink). At the same time, to improve analysis efficiency and response speed, the data can be stored in a big data warehouse (such as Amazon Redshift, Google BigQuery) for cross-data source query and analysis.

[0062] During the data collection process, the collected user behavior data x(t) and advertising investment data u(t) will form the input of the promotion effect model. Assume that the goal of the model is to minimize the error The error function can be defined as:

[0063]

[0064] where: x i (t) represents the i-th promotion effect indicator (such as user growth rate, installation rate, etc.) at time t; is the promotion effect indicator predicted through promotion activities and marketing investments; n is the number of dimensions of the promotion effect indicators.

[0065] In a further implementation process, to ensure data processing efficiency, this formula can be used to perform regression analysis on the historical data of promotion effects, and the promotion strategy can be optimized through a fitting model to make Minimize the error.

[0066] S2. Preprocess the data, including denoising, filling in missing values, feature extraction, and standardization.

[0067] Generally, data preprocessing includes steps such as denoising, filling in missing values, feature extraction, and standardization. In the previous steps, we have collected a large amount of historical data on promotion activities, including user growth, marketing investment, user behavior, social media feedback, and external environment data, etc. However, the original data may have problems such as missing values, outliers, or noise. Therefore, appropriate processing means must be used to ensure the validity and accuracy of the data.

[0068] In this embodiment, the main tasks of the data preprocessing process are:

[0069] Remove irrelevant data and outliers;

[0070] Fill in missing data;

[0071] Extract features that are important for model training;

[0072] Standardize all data so that the model can be trained on a unified scale.

[0073] In a possible implementation, first clean the original data to remove those invalid or redundant data items. For example, delete columns without practical significance, such as invalid registration information of users, expired promotion activity data, etc. For the detection of outliers, statistical methods (such as Z-score) or machine learning-based outlier detection algorithms (such as IsolationForest) can be used to identify and remove them.

[0074] As an option, in the case of missing values in the data, several different filling methods can be selected. For example, the mean filling method or the regression filling method can be used to handle missing values of continuous variables, while for missing values of categorical variables, the mode filling method can be used, or the missing values can be inferred based on other known data. Specifically, for time series data, linear interpolation or interpolation methods based on historical data can be used to fill in the missing time point data to ensure the continuity of the data in time.

[0075] Specifically, for large-scale user behavior data, we may use the autoencoder method in deep learning to fill in and denoise the data. The autoencoder is an unsupervised learning method that can learn features from the original data through a neural network and reconstruct the input data to remove the noise in the data. By training the autoencoder model, missing or noisy data can be effectively completed while maintaining the data structure.

[0076] In a possible implementation, in the feature extraction stage, we may use statistical analysis, principal component analysis (PCA), or automated feature engineering tools to extract the most representative features from the original data. For example, valuable features such as activity, retention rate, and conversion rate are extracted from user behavior data, or metrics such as advertising effectiveness and promotion intensity are extracted from marketing investment data. The purpose of feature extraction is to transform high-dimensional raw data into a low-dimensional representation that can effectively train the model.

[0077] As an option, data standardization is an essential step in data preprocessing. Since the measurement units and value ranges of different features may vary significantly, standardization can unify each feature to the same scale, enabling the machine learning model to better learn the patterns in the data. The methods of standardization include min-max normalization and Z-score normalization.

[0078] By performing standardization through the above methods, the data of different features can be mapped to a unified scale, enabling the subsequent machine learning model to learn more efficiently.

[0079] S3. Establish the evolution process of the promotion effect based on the nonlinear dynamics model;

[0080] In predicting the promotion effect, it is crucial to establish an accurate nonlinear dynamics model. This model can reflect the long-term impact of multiple factors on the software promotion effect and simulate the evolution process of the promotion effect. By using nonlinear differential equations, we can accurately capture the dynamic changes and complex system behaviors in the software promotion activities. This model not only considers the influence of external factors but also reveals the complex interactions between different factors, providing support for the subsequent optimization of promotion strategies.

[0081] Generally, when establishing a nonlinear dynamics model, we need to regard the promotion effect as a multi-dimensional state variable that changes over time. Specifically, the promotion effect is not only affected by the current market environment, user behavior, and promotion investment but also by the feedback of historical states. By introducing multi-dimensional state variables and nonlinear feedback functions, we can construct a mathematical model that can reflect these complex interactions.

[0082] In this embodiment, we assume that the change in the promotion effect is jointly driven by multiple factors, and the interactions between these factors are nonlinear. To establish this model, we first need to consider the multi-dimensional characteristics of the promotion effect, such as user growth rate, installation rate, user activity, etc. Through these characteristics, we can establish the state equation of the promotion effect and describe the evolution process of these characteristics over time through nonlinear differential equations.

[0083] The promotion effect \(x(t)\) is regarded as a multi-dimensional vector, which includes multiple promotion effect indicators (such as user growth, activity, retention rate, etc.). To describe the change process of the promotion effect, we introduce a non-linear differential equation in the following form:

[0084]

[0085] where \(x(t)\) is the state vector of the promotion effect, representing the multi-dimensional indicators of the promotion effect at time \(t\), usually including user growth, activity, installation rate, etc. Specifically, where \(n\) is the number of dimensions of the indicators, and \(u(t)\) is the external control factor, representing the inputs in marketing activities, such as advertising expenses, promotion activity intensity, etc. Usually, where \(m\) is the number of control factors, and \(f\) i (x(t), u(t), t) is a non-linear feedback function, representing the non-linear relationship between the promotion effect and the external control factors. Each \(f\) i describes how the promotion effect state \(x(t)\) and the control factor \(u(t)\) jointly act at time \(t\) to affect the evolution of the promotion effect. \(\alpha\) i is the feedback coefficient, representing the contribution degree of each factor to the promotion effect, usually the weight obtained through data learning. \(\epsilon(t)\) is the external perturbation term, representing the impact of market fluctuations or other external uncertainty factors on the promotion effect. \(\epsilon(t)\) can be regarded as random noise, usually assumed to be a white noise process.

[0086] In a possible implementation, the above differential equation is solved by numerical methods. Common numerical solution methods include the Euler method, the Runge-Kutta method, etc. These methods can gradually calculate the change process of the promotion effect over time under given initial conditions. Specifically, choosing an appropriate numerical method can ensure that the dynamic evolution of the promotion effect can be accurately simulated and can handle multi-dimensional state variables and complex non-linear relationships.

[0087] As an option, in practical applications, we can use machine learning methods to learn the form of the non-linear feedback function \(f\) i (x(t), u(t), t). For example, using methods such as regression analysis, neural networks, or support vector machines to automatically learn the complex relationship between the promotion effect and the control factors from historical data. Through the machine learning model, the non-linear feedback function can automatically adjust its parameters under different market environments and promotion activity conditions to adapt to new promotion strategies.

[0088] Specifically, when using a neural network model, the input of the neural network can be historical data of the promotion effect and current control factors, and the output of the network is the predicted promotion effect. By training the neural network to minimize the prediction error, it can learn an appropriate non-linear function f i (x(t), u(t), t).

[0089] In this embodiment, we approximately solve the above differential equation by a numerical solution method to obtain the change of the promotion effect over time. As the model is continuously solved, the state x(t) of the promotion effect will gradually evolve, providing a basis for subsequent optimization and adjustment of the promotion strategy.

[0090] As an option, to improve the accuracy and efficiency of the solution, in addition to traditional numerical methods, we can also use reinforcement learning algorithms to optimize the model. Through reinforcement learning, we can let the system automatically adjust the control variable u(t) during the training process to achieve the optimal promotion effect in future time steps. This method can dynamically optimize according to environmental changes, further enhancing the effect of the promotion strategy.

[0091] S4. Introduce chaos theory, model the complex behavior of the promotion effect through chaotic systems such as the Lorenz equation, simulate the sensitivity of the system to initial conditions, and identify potential stable intervals and mutation phenomena;

[0092] In this embodiment, introducing chaos theory mainly involves using classical chaotic system models such as the Lorenz equation to model the evolution of the promotion effect. The Lorenz equation was originally used to describe chaotic phenomena in meteorology, but its unique sensitivity and non-linear characteristics are also very suitable for simulating non-linear phenomena such as complex market dynamics and user behavior fluctuations in software promotion.

[0093] In practical applications, we combine the promotion effect model with the equations of the Lorenz system to accurately model the complex dynamics of the promotion effect. This method can reveal the chaotic characteristics of the system, enabling us to foresee potential fluctuations in the promotion effect in an unstable market environment and then optimize the promotion strategy.

[0094] Specifically, the chaos model can reflect the complexity of software promotion activities under the combined action of multi-dimensional factors by simulating the feedback and interaction of multiple promotion effects. We use the following Lorenz equation to describe the evolution of the promotion effect:

[0095]

[0096] Where: x represents the first state variable of the system. In the context of a promotion campaign, x may represent a measure of a certain promotion effect, such as user growth rate, advertising effect, market acceptance, etc. y represents the second state variable of the system. In the promotion campaign, y may represent another related measure, such as user activity, interaction frequency, number of active users, etc. z represents the third state variable of the system, which may represent certain factors in the market environment, such as the competitive situation, the impact of external factors on the promotion, etc. σ represents a constant, which represents the intensity of the interaction between x and y in the system. It determines the feedback rate between x and y. A larger σ will lead to rapid changes in x and y in the system. ρ represents a constant, which represents the intensity of the interaction between x and z in the system. It controls the impact of x on the change of z and is usually used to reflect the role of external promotion activities on market conditions or the competitive situation. β represents a constant, which represents the decay rate of z. A larger β will lead to a rapid decline or change in the market environment factor z and is usually used to describe the stability of market competition or the external environment. t represents the time variable, which represents the change of the promotion activity or the market environment over time.

[0097] As an option, the parameters σ, ρ, and β of the chaotic equation can be optimized and adjusted through historical data. The selection of these parameters is crucial for accurately reflecting the dynamic evolution of the promotion effect. Through data fitting methods, such as the least squares method or the maximum likelihood estimation method, the optimal values of these parameters can be estimated from the actual data.

[0098] Specifically, we solve the above differential equation through numerical methods to obtain the change of the promotion effect over time. Due to the high sensitivity of the chaotic system to the initial conditions, even a small parameter adjustment may lead to completely different prediction results. Therefore, the model can reveal the stability and possible mutation points of the promotion activity under different market environments.

[0099] In a possible implementation, we use common numerical solutions, such as the Euler method or the Runge - Kutta method, to approximately solve the chaotic equation. Through these methods, we can obtain the evolution trajectories of the promotion effects x, y, and z in the future time period, and then evaluate the potential effects of the promotion strategy.

[0100] As an option, to improve the solution efficiency, in addition to traditional numerical methods, we can also adopt a reinforcement learning algorithm based on machine learning to optimize parameter estimation. Through reinforcement learning, the system can continuously adjust the parameters σ, ρ, and β in multiple trials, so as to dynamically adapt to market changes and optimize the prediction of the promotion effect.

[0101] S5. Solve the optimization problem of the promotion effect based on the quantum approximate optimization algorithm, perform parallel computing on the multi - dimensional promotion effect through a quantum circuit, and obtain the global optimal solution;

[0102] In this embodiment, the goal of optimizing the promotion effect is first defined, that is, to minimize the prediction error of the promotion effect, and necessary constraint conditions are added on this basis. The Quantum Approximate Optimization Algorithm (QAOA) is used to solve this optimization problem. QAOA can avoid the problem of traditional optimization methods falling into local optimal solutions through parallel computing of quantum circuits.

[0103] The core of QAOA is to minimize a well-defined loss function by optimizing the control parameters of the quantum circuit where x represents the control variables in the promotion activities (such as advertising investment, promotion intensity, etc.). During the optimization process, the quantum circuit uses the superposition state and quantum entanglement characteristics of qubits, and performs transformations on qubits through quantum gate operations (such as Hadamard gate, CNOT gate, etc.) to minimize the objective function.

[0104] To perform the optimization, the objective function needs to be defined first. The goal of the promotion effect optimization problem is to minimize the prediction error, and the optimization objective function is defined as:

[0105]

[0106] where:

[0107] is the objective loss function, representing the optimization goal of the promotion effect. A common optimization goal is to minimize the promotion effect prediction error f(x) and satisfy the constraint condition g(x).

[0108] f(x) is the prediction error function, which measures the difference between the model prediction result and the actual data. It can be expressed using the Mean Square Error (MSE) as:

[0109]

[0110] where, represents the predicted value of the model, y i represents the actual observed value, and n is the total number of data points.

[0111] g(x) is the constraint condition function, which describes the conditions that must be satisfied during the optimization process. For example, under the budget constraint, the sum of the advertising investment u 1 and the promotion cost u 2 cannot exceed the budget ceiling B:

[0112] g(x) = u 1 + u 2 - B ≤ 0

[0113] where, u 1 and u 2 are control variables, and B is the total budget.

[0114] x is a control variable, representing various decisions or input strategies in the promotion activity. For example, advertising investment, intensity of promotional activities, social media strategies, etc.

[0115] In a possible implementation, the solution process of the quantum approximate optimization algorithm depends on the superposition state and quantum entanglement characteristics of qubits. The superposition state of qubits enables the quantum circuit to explore multiple solution spaces simultaneously, thus improving the optimization efficiency. Each qubit continuously changes through the action of quantum gates (such as Hadamard gates, CNOT gates, etc.), and the result is fed back through quantum measurement, thereby gradually optimizing the objective function.

[0116] To precisely optimize the control variable x, the qubits in the quantum circuit will perform parallel calculations among multiple possible solutions. This parallelism enables QAOA to handle large-scale and high-dimensional optimization problems, avoiding the dilemma that traditional algorithms may fall into local optimal solutions.

[0117] In this embodiment, the optimization process of the quantum circuit includes multiple stages. First, by initializing the qubits, a preliminary quantum state is constructed. Then, the qubits are transformed through quantum gate operations (such as Hadamard gates, CNOT gates, etc.) to generate different superposition states. The measurement result of each qubit provides feedback for the optimization problem, thereby adjusting the parameters of the quantum circuit according to the current measurement result.

[0118] The parallelism of qubits enables QAOA to search simultaneously among multiple solution spaces. The state of each solution in the quantum circuit is updated, and the optimization direction is fed back through the measurement result. Through multiple iterations, the quantum circuit continuously adjusts the optimization path and finally obtains an optimal control variable x * (t), and the promotion effect corresponding to this variable can maximize or minimize a predetermined objective function.

[0119] As an option, to further improve the optimization efficiency, QAOA can be combined with classical optimization algorithms. For example, quantum computing can be used for global search to find the approximate range of the optimization space, while classical optimization algorithms (such as gradient descent method) can be used for further refined adjustment to ensure the accuracy of the final solution.

[0120] S6. Analyze the critical phenomena in the promotion process based on the self-organized criticality theory, simulate the sudden changes in promotion effects caused by market fluctuations or other external factors, and predict the behavior of the system when it enters the critical point;

[0121] In this embodiment, we introduce the theory of self-organized criticality (SOC). The SOC theory holds that after a certain period of development, many complex systems will naturally reach a critical state, in which the system is prone to sudden changes. This theory has been widely applied in fields such as physics, economics, and biology to describe the sudden changes of systems under specific conditions.

[0122] In the context of a promotion campaign, the evolution of the promotion effect may be affected by market environment, competitor behavior, and other external factors. When these factors reach a certain critical point, the promotion effect may change rapidly, and this change usually has a profound impact on the adjustment of strategies.

[0123] Specifically, in this embodiment, we introduce the SOC theory model to analyze the mutation phenomena in the promotion campaign. These mutation phenomena may manifest as a rapid surge in user growth, a sharp decline in activity, a drastic fluctuation in market share, etc. Through the SOC theory, we can identify these critical phenomena, timely adjust the promotion strategy, and avoid uncontrollable negative effects after the system enters the critical state.

[0124] One of the characteristics of a self-organized criticality system is that the critical behavior of the system often occurs spontaneously without external intervention. Specifically, under the influence of certain internal mechanisms and external environments, the system gradually accumulates to the critical point and then undergoes sudden changes. To simulate this critical phenomenon, we use a self-organized criticality model in the following form:

[0125]

[0126] Where: represents the rate of change of x(t) at time t, that is, how x(t) changes with the increase of time. By taking the derivative, we can understand the growth rate or decline rate of the promotion effect at a certain moment. For example, if x(t) represents the number of user growth, then represents the speed of user growth, α represents the decay factor, which controls the natural decay or deceleration process of the promotion effect, β represents the external excitation intensity, represents the strengthening effect of marketing strategies (such as advertising investment, promotion activities, etc.) on the promotion effect, and f i (x) represents the feedback function, which represents the interaction in the promotion effect or the feedback of the market, and is usually non-linear.

[0127] As an option, the decay factor α and the external excitation intensity β in this model can be optimized through historical data. By performing regression analysis on the effects of historical promotion campaigns, these parameters can be accurately estimated, enabling the model to more accurately predict the occurrence of critical phenomena.

[0128] In a possible implementation, we can also introduce a multiple feedback mechanism to consider the coupling between multiple factors. The promotion effect is not only affected by a single factor, but is the result of the combined action of multiple factors. For example, various factors such as market demand, competitive environment, user behavior, advertising investment, etc. will be intertwined and jointly determine the change of the promotion effect. Therefore, the feedback function f i (x) in the SOC model can be designed as a multi-dimensional function to reflect the interaction between different factors.

[0129] To obtain the dynamic evolution process of the promotion effect and predict possible critical phenomena, we need to numerically solve the above SOC model. Common numerical solution methods include the Euler method and the Runge-Kutta method, etc. These methods can gradually calculate the state change of the promotion effect under given initial conditions and system parameters.

[0130] In some embodiments, numerical simulation can be combined with real-time data feedback to enable the model to be dynamically adjusted. For example, by monitoring the changes in the market environment and user behavior in real time and combining historical data, α and β are dynamically adjusted. In this way, the model can predict and respond to possible sudden changes in real time during the promotion activity.

[0131] S7. Apply the optimal control theory, solve the optimal control strategy through dynamic programming to maximize the promotion effect, and optimize the strategy of the promotion activity in real time.

[0132] In this embodiment, we introduce the optimal control theory and use dynamic programming to find the best promotion strategy. The core of the optimal control theory is: under given initial conditions and constraint conditions, maximize the total benefit of the system by optimizing decisions. In the optimization of the promotion effect, the decision is how to adjust the input of various promotion activities (such as advertising expenses, promotion activity intensity, etc.), and the benefit is the final promotion effect (such as user growth, activity, etc.). Through these decisions, the promotion activity can maximize the effect and achieve the best result within the limited resources and time window.

[0133] Specifically, we first define an objective function J, which is used to measure the relationship between the promotion effect and the promotion activity. The optimization goal is to maximize the return of the promotion effect while controlling the cost of the promotion activity. The objective function usually includes the immediate return of the promotion effect and the cost of resource input, and reflects the present value of future effects through a time discount factor. The definition of the objective function is as follows:

[0134]

[0135] Among them, The cost function for each moment describes the relationship between the promotion effect and the control strategy. ρ is the discount factor, T is the optimized time window, x(t) is the state vector of the promotion effect, and u(t) is the external control signal or control variable. dt is the time increment in the time integration.

[0136] In a possible implementation, to solve for the optimal control strategy, we adopt the Hamilton-Jacobi-Bellman equation (HJB equation) in dynamic programming. Through the HJB equation, we can gradually solve for the optimal control strategy u * (t) and, based on the state changes at each time point, adjust the investment in the promotion activities in real time. The HJB equation is usually expressed as:

[0137]

[0138] Where: represents the partial derivative of the value function with respect to time, indicating how the future return of the promotion activity state changes over time at a certain moment t. It reflects how the maximum expected benefit of the remaining promotion activities changes over time. u represents the control variable, indicating the promotion strategy or decision at a certain moment (e.g., advertising investment, promotion intensity, social media promotion, etc.). The optimal control strategy is to select the most appropriate control variable at each time point. represents the immediate cost function, which describes the relationship between the promotion effect and the control strategy at the current time t. It measures the benefit and cost brought by the current investment, usually including advertising expenses, promotion effects, and revenues, etc. f(x(t), u(t)) represents the state transition function, which is the dynamic equation describing how the promotion effect changes over time. It describes how the current promotion effect state x(t) changes over time under the influence of the control strategy u(t). For example, it can be an equation describing market growth, showing how the promotion activities affect user growth. represents the partial derivative of the value function with respect to the promotion effect state x(t), indicating the impact of the change in the promotion effect state on the optimal decision. This term describes the degree to which the change in the current state affects the future return.

[0139] As an option, to solve the HJB equation, common numerical methods include the value iteration method and the policy iteration method. The value iteration method approximates the optimal control strategy by gradually updating the value function V(t, x). The policy iteration method first assumes an initial policy and adjusts it iteratively until the policy converges to the optimal solution. These numerical methods can dynamically adjust the optimal control strategy according to the current promotion effect and market environment.

[0140] Specifically, real-time data can be combined to dynamically adjust the parameters in the optimization process. By monitoring the market environment, user behavior, and the effectiveness of promotion activities in real time, we can dynamically update the state x(t) of the promotion effectiveness, and then adjust the control variable u(t) to ensure the continuous optimization and maximization of the promotion activities.

[0141] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for predicting software development and promotion effects based on artificial intelligence, characterized in that: The following steps are involved: S1. Collect historical data of software promotion activities, including but not limited to user growth, marketing investment, user behavior, social media feedback and external environment data; S2, preprocessing the data, including denoising, filling missing values, feature extraction and standardization; S3, the evolution process of the promotion effect is established based on the nonlinear dynamics model; S4. Introduce chaos theory to model the complex behavior of the promotion effect through chaotic systems such as the Lorentz equation, simulate the sensitivity of the system to the initial conditions, and identify potential stable intervals and mutation phenomena; S5. Solve the optimization problem of promotion effect based on quantum approximate optimization algorithm, perform parallel calculation on multi-dimensional promotion effect through quantum circuit, and obtain the global optimal solution; S6. Analyze the critical phenomena in the promotion process based on the theory of self-organized criticality, simulate the sudden changes in promotion effects caused by market fluctuations or other external factors, and predict the behavior of the system when it reaches the critical point; S7. Apply optimal control theory and solve the optimal control strategy through dynamic programming to maximize the promotion effect and optimize the promotion activity strategy in real time.

2. The method for predicting software development and promotion effect based on artificial intelligence according to claim 1, characterized in that: The S3 step specifically includes the following steps: S3.

1. Establish a state equation based on the multi-dimensional characteristics of software promotion to describe the dynamic change process of promotion effect; S3.

2. Consider the impact of external control factors on promotion effects and introduce them into the system as control variables; S3.

3. Introduce random disturbance terms to simulate external uncertainties in promotion effects, market fluctuations, and the randomness of user behavior.

3. The method for predicting software development and promotion effect based on artificial intelligence according to claim 1, characterized in that: The external control factors in step S3.2 include marketing budget, advertising investment, promotion intensity, user feedback, seasonal factors and competitor promotion activities.

4. The method for predicting software development and promotion effect based on artificial intelligence according to claim 1, characterized in that: The S4 step specifically includes the following steps: S4.

1. Use the Lorentz equation to model the evolution of the promotion effect, where there is a high degree of nonlinear coupling between the multiple dimensions of the promotion effect; S4.

2. Solve the Lorentz equations by numerical methods to identify possible chaotic behaviors of the system and their impact on the generalization effect; S4.

3. Use chaos analysis to evaluate the sensitivity of promotion effects under different initial conditions and simulate the evolution of promotion effects under different market environments.

5. The method for predicting software development and promotion effect based on artificial intelligence according to claim 1, characterized in that: In step S5, the quantum approximate optimization algorithm is used to solve the optimization problem of promotion effect, and the objective function of the optimization problem is: in, is the optimization objective function, represents the total loss in the optimization process, f(x) is the prediction error of the promotion effect, and g(x) is the constraint condition, which represents the resource limitation or other practical constraints of the system.

6. The method for predicting software development and promotion effect based on artificial intelligence according to claim 1, characterized in that: The critical phenomenon steps in the S6 step based on the self-organized criticality theory analysis and promotion process are as follows: S6.

1. Establish a dynamic model of promotion effect and identify the key factors affecting promotion effect; S6.

2. Identify critical points in the promotion process by simulating the critical behavior of the system, and avoid the system from entering a critical state by adjusting the promotion strategy; S6.

3. Predict sudden changes in promotion effects based on market fluctuations or other external factors, and adjust promotion strategies in real time.

7. The method for predicting software development and promotion effect based on artificial intelligence according to claim 1, characterized in that: In the step S7, the optimal control theory optimizes the promotion strategy through the following steps: S7.

1. Set the optimization goal of promotion effect, define the cost function and consider the time discount factor; S7.2, use dynamic programming method to solve the optimal control strategy and minimize the cost of promotion effect according to the objective function; S7.

3. Adjust various parameters in the promotion activities according to the optimal control strategy to maximize the promotion effect.

8. The method for predicting software development and promotion effect based on artificial intelligence according to claim 7, characterized in that: The cost function in step S7.1 is: Among them, L(x(t),u(t)) is the cost function at each moment, describing the relationship between the promotion effect and the control strategy, ρ is the discount factor, T is the optimization time window, x(t) is the state vector of the promotion effect, u(t) is the external control signal or control variable, and dt is the time increment in the time integral.

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