Sharing unmanned aerial vehicle market promotion and income analysis method and system

Abstract

The invention discloses a shared unmanned aerial vehicle market promotion and income analysis method and system, and relates to the technical field of shared unmanned aerial vehicle market promotion and income analysis, and the method comprises the steps: collecting the multi-dimensional data of a user in the process of using a shared unmanned aerial vehicle, constructing a detailed user portrait, and generating a personalized precise marketing strategy based on the user portrait. Meanwhile, a multi-dimensional income analysis model is constructed, factors such as the use frequency, the flight time, the task type, the user payment willingness and the operation cost of the unmanned aerial vehicle are comprehensively considered, income prediction and resource allocation optimization are carried out, and in addition, by collecting user feedback information and continuously optimizing a user portrait, a precise marketing strategy and the income analysis model, the income of the unmanned aerial vehicle is predicted. According to the method, the market promotion effect and the operation income of the shared unmanned aerial vehicle can be remarkably improved, and the method has remarkable practicability and innovativeness.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of shared unmanned aerial vehicle market promotion and revenue analysis, and particularly relates to a shared unmanned aerial vehicle market promotion and revenue analysis method and system. BACKGROUND

[0002] In recent years, shared unmanned aerial vehicle technology has developed rapidly and is widely used in logistics distribution, agricultural plant protection, film shooting, emergency rescue and other fields. With the continuous progress of unmanned aerial vehicle technology, its application in wireless communication, path planning, data sharing and other aspects is also increasingly mature. However, the existing technology still has deficiencies in market promotion and revenue analysis, especially in precise marketing and multi-dimensional revenue analysis model construction.

[0003] The existing shared unmanned aerial vehicle market promotion and revenue analysis method mainly relies on simple user data collection and basic statistical analysis, lacking deep data mining and precise user portrait construction. This leads to limited market promotion effect and cannot meet the individual needs of different user groups. At the same time, the existing revenue analysis model usually only considers a single factor, which cannot comprehensively evaluate the revenue of shared unmanned aerial vehicles, making it difficult to achieve dynamic resource allocation and optimization. SUMMARY

[0004] In view of the above existing problems, the present application is proposed.

[0005] Therefore, the present application provides a shared unmanned aerial vehicle market promotion and revenue analysis method to solve the problems of insufficient precise marketing, single revenue analysis model and how to achieve dynamic resource allocation and optimization in the existing shared unmanned aerial vehicle market promotion and revenue analysis method.

[0006] To solve the above technical problems, the present application provides the following technical solutions:

[0007] In a first aspect, the present application provides a shared unmanned aerial vehicle market promotion and revenue analysis method, characterized in that it comprises the following steps:

[0008] Collecting multi-dimensional data of users in the process of using shared unmanned aerial vehicles, including personal information, usage habits, preference settings, historical order records, etc., to construct detailed user portraits;

[0009] Based on the user portraits, using data analysis technology to generate individualized precise marketing strategies, and recommending suitable shared unmanned aerial vehicle services and packages to users;

[0010] According to the collected user data and marketing strategy feedback, constructing a multi-dimensional revenue analysis model, considering various factors such as usage frequency, flight time, task type, user willingness to pay, operation cost, etc.

[0011] Based on the multi-dimensional benefit analysis model, benefit prediction is carried out, and resource allocation is optimized according to the prediction result, and the number of unmanned aerial vehicles and the distribution area are dynamically adjusted;

[0012] After the user uses the shared unmanned aerial vehicle service, the feedback information of the user is collected, including the satisfaction of the service, the improvement suggestion and the like, and the user portrait and the accurate marketing strategy are optimized;

[0013] According to the user feedback and the benefit analysis result, the user portrait, the accurate marketing strategy and the benefit analysis model are continuously optimized, and a complete market promotion and benefit analysis closed loop is formed.

[0014] As a preferred scheme of the market promotion and benefit analysis method of the shared unmanned aerial vehicle, wherein: the multi-dimensional data of the user in the process of using the shared unmanned aerial vehicle is collected, including personal information, use habit, preference setting, historical order record and the like, to construct a detailed user portrait, and the specific steps are,

[0015] Through the reservation platform of the shared unmanned aerial vehicle, the user mobile application and the real-time data collection module of the user in the process of using the unmanned aerial vehicle, the personal information, the use habit, the preference setting and the historical order record of the user are collected;

[0016] By using data mining technology, key features are extracted from the collected data, including the geographical position, the use frequency, the task type preference and the consumption amount of the user;

[0017] Based on the extracted key features, a clustering analysis algorithm is used to divide the users into different user groups, and a user portrait is constructed for each group;

[0018] The constructed user portrait is stored in a user portrait database, so as to be used for subsequent accurate marketing and benefit analysis.

[0019] As a preferred scheme of the market promotion and benefit analysis method of the shared unmanned aerial vehicle, wherein: based on the user portrait, a data analysis technology is used to generate a personalized accurate marketing strategy, and suitable shared unmanned aerial vehicle services and packages are recommended to the user, and the specific steps are,

[0020] For each user group, the features in the user portrait of the group are analyzed to determine the potential demand and preference of the users in the group;

[0021] According to the potential demand and preference of the user group, a personalized shared unmanned aerial vehicle service package is designed, including different unmanned aerial vehicle models, use time, additional services and the like;

[0022] By using a machine learning algorithm, the response probability of the user to different service packages is predicted according to the user portrait and historical marketing feedback data;

[0023] According to the predicted response probability, the service package most likely to be accepted is recommended for each user, and the user is pushed through the user mobile application, email, etc.

[0024] As a preferred solution of the market promotion and income analysis method of the shared unmanned aerial vehicle, wherein: the multi-dimensional income analysis model is constructed according to the collected user data and marketing strategy feedback, and various factors such as the use frequency, flight time, task type, user willingness to pay, and operation cost of the unmanned aerial vehicle are comprehensively considered, and the specific steps are,

[0025] Collecting the use data of the shared unmanned aerial vehicle, including the flight time, flight distance, task type, and unmanned aerial vehicle model of each use;

[0026] Collecting the income-related data, including the user's payment amount, use duration, package type, discount, and the like;

[0027] Based on the collected use data and income data, a multi-dimensional income analysis model is established by using statistical analysis methods, and the model includes multiple dimensions such as the use frequency, flight time, task type, user willingness to pay, and operation cost of the unmanned aerial vehicle;

[0028] The multi-dimensional income analysis model is verified and optimized to ensure that the model can accurately reflect the income of the shared unmanned aerial vehicle.

[0029] As a preferred solution of the market promotion and income analysis method of the shared unmanned aerial vehicle, wherein: based on the multi-dimensional income analysis model, the income is predicted, and the resource allocation is optimized according to the prediction result, and the number and distribution area of the unmanned aerial vehicles are dynamically adjusted, and the specific steps are,

[0030] The multi-dimensional income analysis model is used to predict the future income, and the prediction result includes the income situation of different time periods, different regions, and different task types;

[0031] According to the income prediction result, the direction of resource allocation that needs to be optimized is determined, including the number of unmanned aerial vehicles, the distribution area, the maintenance plan, and the like;

[0032] Optimization algorithms such as genetic algorithm and simulated annealing algorithm are used to optimize the resource allocation, and the optimal resource allocation scheme is obtained;

[0033] The optimized resource allocation scheme is applied to the operation of the shared unmanned aerial vehicle, and the number and distribution area of the unmanned aerial vehicles are dynamically adjusted.

[0034] As a preferred solution of the market promotion and benefit analysis method of the shared unmanned aerial vehicle, wherein: after the user uses the shared unmanned aerial vehicle service, the feedback information of the user is collected, including the satisfaction of the service, the improvement suggestion, etc., and the user portrait and the accurate marketing strategy are optimized, and the specific steps are,

[0035] After the user uses the shared unmanned aerial vehicle service, the feedback information of the user is collected through the user mobile application, online questionnaire, customer service feedback, etc., including the satisfaction of the service, the improvement suggestion, etc.

[0036] The feedback information of the user is analyzed by using the text analysis technology, the overall satisfaction of the user to the service is judged, and the specific steps are,

[0037] According to the emotional analysis result and the specific suggestion of the user feedback, the user portrait is updated and optimized, and the features and preference settings in the user portrait are adjusted.

[0038] According to the updated user portrait, the effectiveness of the accurate marketing strategy is re-evaluated, and the marketing strategy is adjusted and optimized.

[0039] As a preferred solution of the market promotion and benefit analysis method of the shared unmanned aerial vehicle, wherein: according to the user feedback and the benefit analysis result, the user portrait, the accurate marketing strategy and the benefit analysis model are continuously optimized, and a complete market promotion and benefit analysis closed loop is formed, and the specific steps are,

[0040] The user portrait, the accurate marketing strategy and the benefit analysis model are evaluated regularly, and the evaluation content includes the accuracy of the model, the effectiveness of the marketing strategy, the user satisfaction, etc.

[0041] According to the evaluation result, the direction and content that need to be further optimized are determined.

[0042] By using data analysis and machine learning technology, the user portrait, the accurate marketing strategy and the benefit analysis model are continuously optimized, and the model parameters and strategy content are continuously adjusted.

[0043] The optimized user portrait, the accurate marketing strategy and the benefit analysis model are applied to the market promotion and operation of the shared unmanned aerial vehicle, and a continuously improved closed loop system is formed.

[0044] In the second aspect, the present application provides a market promotion and benefit analysis system of a shared unmanned aerial vehicle, which comprises an image module, collects multi-dimensional data of users in the process of using the shared unmanned aerial vehicle, including personal information, use habit, preference setting, historical order record, etc., to construct a detailed user portrait.

[0045] The marketing module generates a personalized precision marketing strategy based on the user portrait and data analysis techniques, and recommends suitable shared drone services and packages to the user;

[0046] The modeling module constructs a multi-dimensional revenue analysis model based on the collected user data and marketing strategy feedback, considering various factors such as the frequency of using drones, flight time, task type, user willingness to pay, and operating costs;

[0047] The prediction module performs revenue prediction based on the multi-dimensional revenue analysis model and optimizes resource allocation according to the prediction results, dynamically adjusting the number of drones deployed and the distribution area;

[0048] The feedback module collects user feedback information after using shared drone services, including satisfaction with the service, suggestions for improvement, and optimizes the user portrait and precision marketing strategy;

[0049] The optimization module continuously optimizes the user portrait, precision marketing strategy, and revenue analysis model based on user feedback and revenue analysis results, forming a complete market promotion and revenue analysis closed loop.

[0050] In a third aspect, the present application provides a computer device comprising a memory and a processor, the memory storing a computer program, wherein the computer program is executed by the processor to implement any step of the market promotion and revenue analysis method of the shared drone according to the first aspect of the present application.

[0051] In a fourth aspect, the present application provides a computer readable storage medium storing a computer program, wherein the computer program is executed by the processor to implement any step of the market promotion and revenue analysis method of the shared drone according to the first aspect of the present application.

[0052] The present application has the beneficial effects that: by constructing a detailed user portrait and a multi-dimensional benefit analysis model, the market promotion effect and operation benefit of the shared unmanned aerial vehicle are significantly improved. First, through deep data mining and user portrait technology, accurate personalized marketing is realized, the diversified needs of different user groups are met, and user satisfaction and market competitiveness are improved. Secondly, the multi-dimensional benefit analysis model comprehensively considers the use frequency, flight time, task type, user willingness to pay and operation cost of the unmanned aerial vehicle, and can more accurately evaluate the benefit of the shared unmanned aerial vehicle, providing strong support for dynamic resource allocation. In addition, based on the resource optimization scheme of benefit prediction, the number and distribution area of the unmanned aerial vehicle are dynamically adjusted, the resource utilization efficiency is improved, and the operation cost is reduced. Finally, through continuous optimization of user portrait, accurate marketing strategy and benefit analysis model, a complete market promotion and benefit analysis closed loop is formed, ensuring the long-term stable operation and continuous improvement of the system. These innovations together make the present application have significant practicality and innovation in the field of market promotion and benefit analysis of shared unmanned aerial vehicles, and can effectively improve the operation efficiency and economic benefit. BRIEF DESCRIPTION OF DRAWINGS

[0053] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creating laborious work.

[0054] Figure 1 The flowchart of the market promotion and benefit analysis method of the shared unmanned aerial vehicle in embodiment 1. DETAILED DESCRIPTION

[0055] In order to make the above-mentioned purposes, features and advantages of the present application more apparent and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings of the specification.

[0056] In the following description, many specific details are set forth in order to provide a thorough understanding of the present application, but the present application can also be implemented in other ways different from those described herein, and those skilled in the art can make similar generalizations without departing from the connotation of the present application, therefore the present application is not limited to the specific embodiments disclosed below.

[0057] Secondly, the "one embodiment" or "embodiment" referred to herein means that the specific features, structures or characteristics can be included in at least one implementation of the present application. "In one embodiment" appearing in different places in the specification does not mean the same embodiment, nor is it an independent or alternative embodiment that excludes other embodiments.

[0058] Embodiment 1, refer to Figure 1 For the first embodiment of the present application, the embodiment provides a method for sharing unmanned aerial vehicle market promotion and benefit analysis, characterized in that it comprises the following steps:

[0059] Collecting multi-dimensional data of users in the process of using shared unmanned aerial vehicles, including personal information, usage habits, preference settings, historical order records, etc., to build detailed user portraits;

[0060] Based on the user portrait, personalized precision marketing strategies are generated using data analysis techniques, and suitable shared unmanned aerial vehicle services and packages are recommended to users;

[0061] According to the collected user data and marketing strategy feedback, a multi-dimensional benefit analysis model is constructed, considering various factors such as the frequency of use of unmanned aerial vehicles, flight time, task type, user willingness to pay, and operating costs;

[0062] Based on the multi-dimensional benefit analysis model, revenue forecasts are made, and resource allocation is optimized according to the forecast results, dynamically adjusting the number of unmanned aerial vehicles and their distribution areas;

[0063] After users use shared unmanned aerial vehicle services, feedback information is collected, including satisfaction with services, suggestions for improvement, etc., and user portraits and precision marketing strategies are optimized;

[0064] According to user feedback and benefit analysis results, continuously optimize user portraits, precision marketing strategies and benefit analysis models to form a complete market promotion and benefit analysis closed loop.

[0065] It should be noted that the multi-dimensional data of users in the process of using shared unmanned aerial vehicles, including personal information, usage habits, preference settings, historical order records, etc., are collected to build detailed user portraits.

[0066] Through the reservation platform of shared unmanned aerial vehicles, user mobile applications and sensors and data collection modules on unmanned aerial vehicles, multi-dimensional data of users in the process of using shared unmanned aerial vehicles are comprehensively collected. These data not only include users' personal information such as age, gender, occupation, etc., but also cover usage habits, such as users' usual time period for using unmanned aerial vehicles, preferred areas for use, length of time for each use, etc. In addition, users' preference settings, such as preferred unmanned aerial vehicle models, additional service preferences, etc., and historical order records, including order frequency, order amount, service type used, etc. are collected. These data will be stored in a cloud database for subsequent in-depth analysis and processing.

[0067] By collecting multi-dimensional data to construct detailed user portraits, a comprehensive understanding of user behavior and preferences is achieved. This not only provides a data foundation for subsequent precision marketing, but also helps operators better understand user needs, thereby optimizing service content and improving user experience. Ultimately, this in-depth user insight helps to improve user satisfaction and loyalty, thereby increasing the frequency of users using shared drones and the amount of consumption.

[0068] Based on the user portrait, personalized precision marketing strategies are generated using data analysis techniques, and suitable shared drone services and packages are recommended to users.

[0069] Using data mining and machine learning algorithms, the constructed user portraits are analyzed to identify the characteristics and preferences of different user groups. For example, through clustering analysis, users are divided into different groups, such as "logistics delivery users" and "aerial photography enthusiasts". For each group, personalized service packages and marketing strategies are designed. For example, for logistics delivery users, efficient logistics solutions and customized drone models are recommended; for aerial photography enthusiasts, professional aerial photography equipment and related service packages are provided. These personalized recommendations and service packages are pushed to users through mobile applications, emails, etc.

[0070] Through personalized precision marketing strategies, the targeting and success rate of marketing activities can be effectively improved. Compared with traditional mass marketing, precision marketing can significantly improve user response rate and conversion rate, thereby improving the effectiveness and efficiency of market promotion. In addition, this personalized service can enhance user brand recognition and loyalty, further promoting user repeat purchase and word-of-mouth.

[0071] Based on the collected user data and marketing strategy feedback, a multi-dimensional revenue analysis model is constructed, considering multiple factors such as drone usage frequency, flight time, task type, user willingness to pay, and operating cost.

[0072] Collect usage data of shared drones, including flight time, flight distance, task type, drone model, etc. for each use, as well as revenue-related data such as user payment amount, usage duration, package type, discount offers, etc. Based on these data, statistical analysis and machine learning methods are used to establish a multi-dimensional revenue analysis model. This model will consider multiple dimensions such as drone usage frequency, flight time, task type, user willingness to pay, and operating cost, through data fitting and model validation to ensure that the model can accurately reflect the revenue of shared drones.

[0073] By constructing a multi-dimensional revenue analysis model, the revenue of shared drones can be evaluated comprehensively and accurately. Compared with traditional single-factor revenue analysis, this multi-dimensional model can more realistically reflect the operating conditions and provide stronger support for operational decision-making. For example, by analyzing different task types and user willingness to pay, operators can optimize service pricing strategies to increase revenue; by analyzing the frequency of use and flight time of drones, operators can optimize drone deployment and maintenance plans to reduce operating costs.

[0074] Based on the multi-dimensional revenue analysis model, revenue prediction is performed, and resource allocation is optimized according to the prediction results to dynamically adjust the number of drones deployed and the distribution area.

[0075] Using the multi-dimensional revenue analysis model, future revenue is predicted, and the prediction results include the revenue of different time periods, different regions, and different task types. Based on the revenue prediction results, the direction of resource allocation optimization is determined, such as the number of drones deployed, the distribution area, and the maintenance plan. Optimization algorithms such as genetic algorithms and simulated annealing algorithms are used to optimize resource allocation to obtain the optimal resource allocation scheme. The optimized resource allocation scheme is applied to the operation of shared drones to dynamically adjust the number of drones deployed and the distribution area.

[0076] Through resource allocation optimization based on revenue prediction, the number of drones deployed and the distribution area can be dynamically adjusted to improve resource utilization efficiency. This dynamic adjustment ensures that there are enough drones available in areas and time periods with high demand, while avoiding excessive deployment in areas with low demand, thereby reducing operating costs and improving overall revenue. In addition, optimized resource allocation can improve operational flexibility and response speed, better adapting to market changes.

[0077] After users use the shared drone service, feedback information is collected, including satisfaction with the service, suggestions for improvement, etc., and user portraits and precise marketing strategies are optimized.

[0078] After users use the shared drone service, feedback information is collected through user mobile applications, online questionnaires, customer service feedback, etc., including satisfaction with the service, suggestions for improvement, etc. Text analysis techniques are used to perform sentiment analysis on user feedback information to determine overall user satisfaction with the service. Based on the sentiment analysis results and specific suggestions from user feedback, user portraits are updated and optimized, and the characteristics and preference settings in the user portraits are adjusted. Based on the updated user portraits, the effectiveness of the precise marketing strategy is re-evaluated, and the marketing strategy is adjusted and optimized.

[0079] By collecting user feedback and conducting sentiment analysis, the real feelings and needs of users can be understood in a timely manner. This optimization mechanism based on user feedback can ensure that user portraits and precise marketing strategies are always consistent with user needs, thereby improving the accuracy and effectiveness of marketing strategies. In addition, timely response to user feedback can also enhance user trust and satisfaction with the brand, further promoting long-term use and loyalty of users.

[0080] Based on user feedback and revenue analysis results, continuously optimize user portraits, precise marketing strategies, and revenue analysis models to form a complete market promotion and revenue analysis closed loop.

[0081] Regularly evaluate user portraits, precise marketing strategies, and revenue analysis models, including model accuracy, marketing strategy effectiveness, and user satisfaction. Based on the evaluation results, determine the direction and content that need to be further optimized. Use data analysis and machine learning techniques to continuously optimize user portraits, precise marketing strategies, and revenue analysis models, and constantly adjust model parameters and strategy content. Apply the optimized user portraits, precise marketing strategies, and revenue analysis models to the market promotion and operation of shared drones to form a continuously improving closed loop system.

[0082] By continuously optimizing user portraits, precise marketing strategies, and revenue analysis models, a complete market promotion and revenue analysis closed loop is formed, ensuring that the system is always in the best operating state. This closed loop optimization mechanism can timely discover and solve problems in operation, continuously improve service quality, and improve market competitiveness. In addition, continuous optimization can adapt to changes in the market and dynamic changes in user needs, ensuring the long-term stable development of shared drone services.

[0083] Specifically, the collection of multi-dimensional data of users during the use of shared drones includes personal information, usage habits, preference settings, and historical order records to construct detailed user portraits. The specific steps are as follows,

[0084] Through the reservation platform of shared drones, user mobile applications, and real-time data collection modules during the use of drones, personal information, usage habits, preference settings, and historical order records of users are collected;

[0085] Using data mining techniques, key features are extracted from the collected data, including user geographic location, usage frequency, task type preference, and consumption amount;

[0086] Based on the extracted key features, clustering analysis algorithms are used to divide users into different user groups, and user portraits are constructed for each group;

[0087] The constructed user portrait is stored in a user portrait database for subsequent precision marketing and revenue analysis.

[0088] It should be noted that through the sharing of the drone reservation platform, the user mobile application, and the real-time data collection module during the use of the drone, the user's personal information, usage habits, preference settings, and historical order records are collected.

[0089] This step involves collecting multi-dimensional data of users through multiple channels. First, the user's personal information during the reservation process, such as name, contact information, address, etc., is recorded using the shared drone reservation platform. Second, the user's usage habits, including usage frequency, usage time, preferred service types, etc., are collected through the user mobile application. In addition, real-time data such as flight path, flight time, task type, etc. during the use of the drone are recorded with the help of sensors on the drone and the real-time data collection module. These data will be stored uniformly in the cloud database for subsequent in-depth analysis.

[0090] Through multi-channel collection of multi-dimensional data of users, a comprehensive understanding of user behavior and preferences is achieved. This provides a rich data foundation for subsequent user portrait construction, making the user portrait more accurate and detailed. Accurate user portrait can help operators better understand user needs, thereby optimizing service content and improving user experience, ultimately achieving the beneficial effect of improving user satisfaction and loyalty.

[0091] Using data mining technology, key features are extracted from the collected data, including user's geographic location, usage frequency, task type preference, and consumption amount.

[0092] After collecting a large amount of user data, data mining technology is used to process and analyze these data. Key features are identified through algorithms, such as user's geographic location information to determine hotspots of user activity, analysis of user's usage frequency to understand user's activity level, identification of task type preference to clarify user's demand for different services, and statistics of consumption amount to assess user's consumption ability. These key features will serve as the basis for subsequent user portrait and precision marketing.

[0093] Through data mining technology, key features are extracted from massive data, which can filter out valuable information for operational decision-making. This not only improves the efficiency of data processing, but also makes the user portrait more focused and accurate. Accurate user portrait can help operators better understand user needs, thereby optimizing service content and improving user experience, ultimately achieving the beneficial effect of improving user satisfaction and loyalty.

[0094] Based on the extracted key features, users are divided into different user groups using clustering analysis algorithms, and a user portrait is constructed for each group.

[0095] Using clustering analysis algorithms, users are divided into different groups based on the extracted key features. For example, users are divided into "urban logistics delivery users", "suburban aerial photography enthusiasts", etc. For each group, a detailed user portrait is constructed based on its characteristics, including basic information, usage habits, preference settings, etc. These user portraits will be stored in the user portrait database for subsequent precision marketing and revenue analysis.

[0096] By clustering analysis algorithm, users are divided into different groups, and a user portrait is constructed for each group, which can realize the fine management of user groups. This fine management helps the operator to develop personalized service and marketing strategies according to the characteristics and needs of different groups, so as to improve the effect and efficiency of market promotion, and finally achieve the beneficial effect of improving user satisfaction and loyalty.

[0097] The constructed user portrait is stored in the user portrait database for subsequent precision marketing and revenue analysis.

[0098] The constructed user portrait will be stored in a special user portrait database. The database uses distributed storage technology to ensure data security and scalability. User portrait data will be updated regularly to reflect the latest behavior and preferences of users. Stored user portrait data will be used as the basis for subsequent precision marketing and revenue analysis, and will be called by marketing systems and revenue analysis systems through data interfaces.

[0099] Storing the constructed user portrait in the user portrait database provides reliable data support for subsequent precision marketing and revenue analysis. This data storage and management method not only improves the usability and security of data, but also enables the operator to quickly respond to market changes, adjust marketing strategies and optimize resource allocation in a timely manner, ultimately achieving the beneficial effect of improving operational efficiency and economic benefits.

[0100] Specifically, based on the user portrait, the data analysis technology is used to generate personalized precision marketing strategies, and suitable shared unmanned aerial vehicle services and packages are recommended to users, and the specific steps are as follows,

[0101] For each user group, analyze the characteristics in the user portrait to determine the potential needs and preferences of the group users;

[0102] According to the potential needs and preferences of the user group, design personalized shared unmanned aerial vehicle service packages, including different unmanned aerial vehicle models, usage time, additional services, etc.

[0103] Using machine learning algorithms, predict the response probability of users to different service packages based on user profiles and historical marketing feedback data.

[0104] According to the predicted response probability, recommend the most likely to be accepted service package for each user, and push it to the user through the user mobile application, email, etc.

[0105] It should be noted that for each user group, analyze the features in the user profile to determine the potential needs and preferences of the group users.

[0106] First, analyze the user profile of each user group in depth. The user profile contains personal information, usage habits, preference settings, and historical order records of the user, etc. Through data mining technology, extract key features such as user's geographic location, usage frequency, task type preference, consumption amount, etc. Based on these features, analyze the potential needs and preferences of each user group. For example, for a user group whose geographic location is concentrated in the city, analyze whether they are more inclined to logistics distribution tasks; for a user group with high usage frequency, analyze whether they have higher requirements for the endurance and flight speed of the drone. Through these analyses, the specific needs and preferences of each user group are clarified, providing a basis for the design of subsequent personalized service packages.

[0107] Through analyzing the features in the user profile, the potential needs and preferences of each user group can be accurately identified. This allows the operator to design service packages with a high degree of personalization and satisfaction. Compared with traditional general service packages, this personalized service based on user profiles can better meet the specific needs of users, thereby improving their recognition and loyalty to the service.

[0108] According to the potential needs and preferences of the user group, design personalized shared drone service packages, including different drone models, usage time, additional services, etc.

[0109] Based on the potential needs and preferences of the user group determined in step 1, design personalized shared drone service packages. For example, for a user group that prefers logistics distribution, design a package that includes a high-efficiency logistics drone model, longer usage time, and additional logistics tracking services; for a user group that prefers aerial photography, design a package that includes a professional aerial photography drone model, flexible usage time, and additional post-production services. These packages will be pushed to users through user mobile applications, emails, etc., ensuring that users can receive service recommendations that meet their needs.

[0110] By designing personalized service packages, the satisfaction and usage frequency of users can be effectively improved. Personalized service packages not only meet the specific needs of users, but also enhance user experience through additional services. This personalized service can significantly improve user loyalty and word-of-mouth, thereby enhancing the market competitiveness of shared drone services.

[0111] Using machine learning algorithms, the response probability of users to different service packages is predicted based on user portraits and historical marketing feedback data.

[0112] Using machine learning algorithms, the response probability of users to different service packages is predicted based on user portraits and historical marketing feedback data. First, collect user feedback data on past recommended service packages, including whether to accept recommendations, satisfaction after use, etc. Then, combine these data with the features in the user portrait to train a machine learning model. The model will learn the relationship between user features and service package response, so as to predict the response probability of new users to different service packages. For example, through a logistic regression model or decision tree model, the probability of a user accepting a certain service package is predicted, and users are ranked according to the probability.

[0113] Predicting the response probability of users to service packages through machine learning algorithms can significantly improve the accuracy and success rate of marketing activities. Compared with traditional marketing methods, this data-driven prediction method can more accurately identify potential high-response users, thereby improving the utilization efficiency of marketing resources and reducing marketing costs.

[0114] According to the predicted response probability, recommend the most likely to be accepted service package for each user, and push it to the user through the user mobile application, email, etc.

[0115] According to the predicted response probability in step 3, recommend the most likely to be accepted service package for each user. The recommendation system will select the most suitable service package for the user according to the user portrait and prediction results, and push it to the user through the user mobile application, email, etc. The push content will include detailed information of the service package, preferential activities and use guide, etc., to ensure that the user can clearly understand the value and advantages of the recommended service. At the same time, the system will track the user's feedback in real time, collect the user's acceptance of the recommended service and use experience, in order to further optimize the recommendation algorithm.

[0116] By accurately recommending service packages, the acceptance rate and usage frequency of users can be significantly improved. This recommendation mechanism based on user portraits and response probabilities not only improves user satisfaction, but also optimizes marketing strategies to improve operational efficiency. Accurate recommendation can ensure that users receive the most suitable service, thereby improving user loyalty and long-term value.

[0117] Specifically, the multi-dimensional revenue analysis model is constructed based on the collected user data and marketing strategy feedback, considering various factors such as the usage frequency of the UAV, flight time, task type, user willingness to pay, and operation cost, and the specific steps are as follows,

[0118] Collect usage data of shared UAVs, including flight time, flight distance, task type, UAV model, etc. for each use;

[0119] Collect revenue-related data, including user payment amount, usage duration, package type, discount offer, etc.

[0120] Based on the collected usage data and revenue data, statistical analysis methods are used to establish a multi-dimensional revenue analysis model, which includes multiple dimensions such as the usage frequency of the UAV, flight time, task type, user willingness to pay, and operation cost.

[0121] The multi-dimensional revenue analysis model is verified and optimized to ensure that the model can accurately reflect the revenue situation of shared UAVs.

[0122] It should be noted that the usage data of shared UAVs is collected, including flight time, flight distance, task type, UAV model, etc. for each use.

[0123] First, through the booking platform of shared UAVs and the user mobile application, the specific information of each use of UAVs is recorded, such as flight time, flight distance, task type, and UAV model. These data will be transmitted to the cloud database in real time through the Internet of Things technology, ensuring the integrity and real-time nature of the data. At the same time, the sensors and data collection modules on the UAVs are used to further collect detailed data during flight, such as flight speed, flight altitude, and environmental conditions. These data will be uniformly stored and managed for subsequent analysis and processing.

[0124] By collecting detailed usage data, a comprehensive understanding of the usage and performance of shared UAVs can be achieved. This not only provides accurate data support for subsequent revenue analysis, but also helps operators to timely discover and solve problems in the use of UAVs, improving service quality and user experience.

[0125] Collect revenue-related data, including user payment amount, usage duration, package type, discount offer, etc.

[0126] After the user completes the use of the shared drone, the data related to the revenue, such as the user's payment amount, use duration, selected package type, and enjoyed discount preferences, are collected through the reservation platform and payment system. These data are combined with the user's personal information and use data and stored in a unified database. Through data analysis tools, these data are preliminarily processed and classified for subsequent revenue analysis.

[0127] By collecting data related to revenue, the user's consumption behavior and preferences can be comprehensively understood. This provides a rich data foundation for subsequent construction of a multi-dimensional revenue analysis model, enabling operators to more accurately assess the revenue situation of the service, optimize pricing strategies and package design, and improve operational efficiency.

[0128] Based on the collected use data and revenue data, a multi-dimensional revenue analysis model is established using statistical analysis methods, including the use frequency of drones, flight time, task type, user willingness to pay, and operational cost.

[0129] Using the collected use data and revenue data, statistical analysis methods such as regression analysis and clustering analysis are used to establish a multi-dimensional revenue analysis model. This model will consider multiple dimensions such as the use frequency of drones, flight time, task type, user willingness to pay, and operational cost. Through data fitting and model verification, it is ensured that the model can accurately reflect the revenue situation of shared drones. The model will be based on a large amount of historical data and real-time data, and the model parameters will be continuously optimized and adjusted through machine learning algorithms.

[0130] By establishing a multi-dimensional revenue analysis model, the revenue situation of shared drones can be comprehensively and accurately assessed. Compared with traditional single-factor revenue analysis, this multi-dimensional model can more realistically reflect the operational situation and provide stronger support for operational decision-making. For example, by analyzing different task types and user willingness to pay, operators can optimize service pricing strategies and increase revenue; by analyzing the use frequency and flight time of drones, operators can optimize drone deployment and maintenance plans and reduce operational costs.

[0131] The multi-dimensional revenue analysis model is verified and optimized to ensure that the model accurately reflects the revenue situation of shared drones.

[0132] After establishing the multi-dimensional revenue analysis model, the model is verified and optimized through actual operational data. First, the model's predicted revenue is compared with the actual revenue to evaluate the model's accuracy and reliability. Then, based on the verification results, machine learning algorithms are used to optimize the model by adjusting model parameters and structures to improve the model's prediction accuracy. The optimized model will be able to more accurately reflect the revenue situation of shared drones and provide stronger support for operational decision-making.

[0133] By verifying and optimizing the multi-dimensional revenue analysis model, the accuracy and reliability of the model can be ensured. This not only improves the accuracy of revenue prediction, but also provides a more scientific basis for decision-making for operators, helping operators better optimize resource allocation, improve operational efficiency and economic benefits.

[0134] Specifically, the multi-dimensional revenue analysis model is used to predict revenue and optimize resource allocation based on the prediction results, dynamically adjusting the number of drones deployed and the distribution area. The specific steps are as follows,

[0135] The multi-dimensional revenue analysis model is used to predict future revenue, and the prediction results include revenue in different time periods, different regions, and different task types.

[0136] According to the revenue prediction results, determine the direction of resource allocation optimization, including the number of drones deployed, distribution area, maintenance plan, etc.

[0137] Optimization algorithms such as genetic algorithms, simulated annealing algorithms, etc. are used to optimize resource allocation to obtain the optimal resource allocation scheme.

[0138] The optimized resource allocation scheme is applied to the operation of shared drones, dynamically adjusting the number of drones deployed and the distribution area.

[0139] It should be noted that the multi-dimensional revenue analysis model is used to predict future revenue, and the prediction results include revenue in different time periods, different regions, and different task types.

[0140] First, the collected usage data and revenue data of shared drones are input into the multi-dimensional revenue analysis model. Based on historical data and real-time data, the model uses statistical analysis and machine learning algorithms to predict future revenue. The prediction results will cover different time periods (such as hours, days, months, seasons), different regions (such as cities, suburbs, rural areas), and different task types (such as logistics distribution, aerial photography, agricultural plant protection, etc.). Through this multi-dimensional prediction, operators can fully understand the revenue trends and potential opportunities in different scenarios.

[0141] By using the multi-dimensional revenue analysis model to predict revenue, operators can understand the revenue situation in different time periods, regions and task types in advance, so as to develop more scientific and reasonable operation strategies. This not only helps to optimize resource allocation and improve operational efficiency, but also enhances the adaptability of operators to market changes, ultimately maximizing revenue.

[0142] According to the revenue prediction results, determine the direction of resource allocation optimization, including the number of drones deployed, distribution area, maintenance plan, etc.

[0143] Based on the revenue prediction results in step 1, analyze the revenue performance of different time periods, regions, and task types. For regions and task types with higher revenue, increase the number of drones deployed; for regions with lower revenue, appropriately reduce the number of deployments or adjust the distribution area. At the same time, according to the usage frequency of drones and the predicted usage demand, develop a reasonable maintenance plan to ensure the efficient operation of drones.

[0144] Optimizing resource allocation based on revenue prediction results can ensure that drones are fully utilized in regions and time periods with high demand, avoiding resource waste. This dynamic adjustment mechanism not only improves resource utilization efficiency, but also reduces operating costs, enhancing the market competitiveness of operators.

[0145] Optimize resource allocation using optimization algorithms such as genetic algorithms, simulated annealing algorithms, etc., to obtain the optimal resource allocation scheme.

[0146] After determining the direction of resource allocation optimization, use genetic algorithms, simulated annealing algorithms, etc. to further optimize resource allocation. These algorithms simulate natural selection and physical annealing processes to efficiently search for the optimal resource allocation scheme. Specifically, genetic algorithms optimize resource allocation schemes through selection, crossover, and mutation operations; simulated annealing algorithms gradually lower the temperature to avoid getting stuck in local optimal solutions and find global optimal solutions.

[0147] Optimizing resource allocation using optimization algorithms can ensure that the optimal resource allocation scheme is found, further improving resource utilization efficiency and operating revenue. Compared with traditional empirical resource allocation, this algorithm-based optimization method is more scientific and efficient, which can significantly improve the economic benefits of operators.

[0148] Apply the optimized resource allocation scheme to the operation of shared drones to dynamically adjust the number of drones deployed and the distribution area.

[0149] Apply the optimized resource allocation scheme to actual operations to dynamically adjust the number of drones deployed and the distribution area to ensure efficient and flexible operations. The operation system will monitor the usage and revenue performance of drones in real time and dynamically adjust the resource allocation scheme according to actual conditions. For example, when demand suddenly increases in a certain region, the system will automatically adjust the number of drones deployed to meet user demand.

[0150] Applying the optimized resource allocation scheme to actual operations can achieve dynamic adjustment of the number of drones deployed and the distribution area, ensuring efficient and flexible operations. This dynamic adjustment mechanism not only improves user satisfaction, but also enhances the adaptability of operators to market changes, ultimately maximizing operating efficiency.

[0151] Specifically, after the user uses the shared drone service, the feedback information of the user is collected, including satisfaction with the service, improvement suggestions, etc., and the user portrait and precision marketing strategy are optimized, and the specific steps are,

[0152] After the user uses the shared drone service, the feedback information of the user is collected through the user mobile application, online questionnaire, customer service feedback, etc., including the satisfaction with the service, improvement suggestions, etc.

[0153] The sentiment analysis technology is used to analyze the feedback information of the user, and the overall satisfaction of the user with the service is judged.

[0154] According to the sentiment analysis result and specific suggestion of the user feedback, the user portrait is updated and optimized, and the features and preference settings in the user portrait are adjusted.

[0155] According to the updated user portrait, the effectiveness of the precision marketing strategy is re-evaluated, and the marketing strategy is adjusted and optimized.

[0156] It should be noted that after the user uses the shared drone service, the feedback information of the user is collected through the user mobile application, online questionnaire, customer service feedback, etc., including the satisfaction with the service, improvement suggestions, etc.

[0157] After the user completes the shared drone service, the system automatically triggers the feedback collection mechanism. Through the user mobile application, the user is pushed to fill in the satisfaction questionnaire, and the questionnaire content covers the overall satisfaction with the service, operation convenience, drone performance, additional services, etc. At the same time, an online questionnaire link is provided for the user to fill in more detailed feedback at any time. In addition, the customer service team actively collects the feedback information of the user through telephone follow-up or online chat tools, especially the improvement suggestions for the service. All feedback information will be collected and stored in the feedback database for subsequent analysis.

[0158] Through multi-channel collection of user feedback information, the real feelings and needs of users for the service can be fully understood. This not only provides a direct basis for subsequent service optimization, but also enhances the interaction between users and operators, and improves the user's participation and satisfaction with the service.

[0159] The sentiment analysis technology is used to analyze the feedback information of the user, and the overall satisfaction of the user with the service is judged.

[0160] The collected user feedback information will be imported into a text analysis system. This system uses natural language processing techniques to preprocess the text content, including text cleaning, word segmentation, and part-of-speech tagging. Then, sentiment analysis algorithms such as dictionary-based methods or machine learning models are used to classify the sentiment orientation in the text and determine the overall satisfaction of users with the service. The sentiment analysis results will be quantified into specific satisfaction scores and stored in the feedback database.

[0161] Through text analysis technology, sentiment analysis of user feedback can quickly and accurately evaluate the overall satisfaction of users with the service. This allows operators to identify problems and deficiencies in the service in a timely manner, providing data support for subsequent optimization, thereby improving service quality and user satisfaction.

[0162] According to the sentiment analysis results and specific suggestions of user feedback, the user portrait is updated and optimized, and the features and preference settings in the user portrait are adjusted.

[0163] Based on the sentiment analysis results and specific suggestions of users, the system will dynamically update the user portrait. The features and preference settings in the user portrait will be adjusted according to the feedback information, for example, if the user feedback on a certain additional service is low, the system will adjust the weight of that service in the user portrait. At the same time, according to the user's improvement suggestions, the system will update the user's preference settings to ensure that the user portrait can more accurately reflect the user's actual needs.

[0164] By updating and optimizing the user portrait according to user feedback, the user portrait can more accurately reflect the user's actual needs and preferences. This not only improves the quality of personalized services, but also enhances user satisfaction and loyalty, providing a more reliable data foundation for subsequent precision marketing and personalized recommendations.

[0165] According to the updated user portrait, the effectiveness of the precision marketing strategy is re-evaluated, and the marketing strategy is adjusted and optimized.

[0166] Using the updated user portrait, the system will re-evaluate the existing precision marketing strategy. Through data analysis tools, compare the user portraits before and after adjustment to evaluate the effectiveness of the marketing strategy. According to the evaluation results, the system will adjust the marketing strategy, for example, increase the promotion of high-satisfaction services shown in the user portrait; for low-satisfaction services, adjust the promotion strategy or optimize the service content. The adjusted marketing strategy will be pushed to users through user mobile applications, emails, etc., to ensure the accuracy and effectiveness of marketing activities.

[0167] By re-evaluating and optimizing the precision marketing strategy according to the updated user portrait, it can ensure that the marketing activities are more in line with the actual needs and preferences of users. This not only improves the response rate and conversion rate of marketing activities, but also enhances user satisfaction and loyalty, ultimately maximizing operational efficiency.

[0168] Specifically, the user portrait, precision marketing strategy, and revenue analysis model are continuously optimized based on user feedback and revenue analysis results, forming a complete market promotion and revenue analysis closed loop. The specific steps are as follows,

[0169] Periodically evaluate the user portrait, precision marketing strategy, and revenue analysis model, including model accuracy, marketing strategy effectiveness, and user satisfaction.

[0170] Based on the evaluation results, determine the direction and content that need to be further optimized.

[0171] Use data analysis and machine learning techniques to continuously optimize the user portrait, precision marketing strategy, and revenue analysis model, and constantly adjust model parameters and strategy content.

[0172] Apply the optimized user portrait, precision marketing strategy, and revenue analysis model to the market promotion and operation of shared drones, forming a continuously improving closed loop system.

[0173] It should be noted that the user portrait, precision marketing strategy, and revenue analysis model are periodically evaluated, including model accuracy, marketing strategy effectiveness, and user satisfaction.

[0174] Regularly conduct evaluation work, with a cycle of once a month or once a quarter. The evaluation team will comprehensively evaluate the user portrait, precision marketing strategy, and revenue analysis model from multiple dimensions. First, by comparing the model prediction results with the actual operation data, calculate the model accuracy indicators such as prediction error rate and classification accuracy. Second, analyze the implementation effect of the precision marketing strategy, including user response rate, conversion rate, and repeat purchase rate. In addition, collect user feedback through questionnaires, online evaluations, and customer service records to quantify user satisfaction and form a comprehensive evaluation report.

[0175] Through regular evaluation, problems and deficiencies in the user portrait, precision marketing strategy, and revenue analysis model can be found in a timely manner. This helps operators quickly adjust and optimize related strategies to ensure they are always in the best state, thereby improving operational efficiency and user satisfaction, and ultimately maximizing operational efficiency.

[0176] Based on the evaluation results, determine the direction and content that need to be further optimized.

[0177] After the evaluation is completed, the data analysis team will conduct in-depth analysis of the evaluation report to identify key issues in the model and strategy. For example, if it is found that the user portrait has low prediction accuracy on certain features, the feature extraction algorithm that needs to be optimized will be determined; if the user response rate of the precision marketing strategy does not meet expectations, the problems in marketing channels, content, and timing will be analyzed. Based on these problems, a detailed optimization plan is developed, specifying the optimization direction and specific tasks, such as improving data mining algorithms, adjusting marketing push times, etc.

[0178] The optimization direction and content are clear, providing clear guidance for subsequent optimization work. This helps to concentrate resources to solve key problems, improve the pertinence and efficiency of optimization work, avoid resource waste caused by blind adjustment, and further improve the overall performance and operation effect of the system.

[0179] Using data analysis and machine learning techniques, continuously optimize user portraits, precision marketing strategies, and revenue analysis models, and constantly adjust model parameters and strategy content.

[0180] The optimization team will use advanced data analysis and machine learning techniques to continuously optimize user portraits, precision marketing strategies, and revenue analysis models. For user portraits, more complex data mining algorithms such as deep learning models are introduced to retrain feature extractors, improving feature accuracy and comprehensiveness. In terms of precision marketing strategy, reinforcement learning algorithms are used to dynamically adjust marketing content and push strategies based on user behavior. For revenue analysis models, ensemble learning methods are used to combine the strengths of multiple models to improve prediction accuracy and stability. At the same time, model parameters such as learning rate and regularization coefficient are constantly adjusted to adapt to changing market environments.

[0181] Through continuous optimization, the accuracy of user portraits, the effectiveness of precision marketing strategies, and the accuracy of revenue analysis models can be continuously improved. This helps operators better understand user needs, provide more personalized services, improve user satisfaction and loyalty, and optimize resource allocation to improve operational revenue.

[0182] Apply the optimized user portraits, precision marketing strategies, and revenue analysis models to the market promotion and operation of shared drones to form a continuously improving closed-loop system.

[0183] The optimized user portrait, precise marketing strategy, and revenue analysis model are deployed into the market promotion and operation system of shared drones. In terms of market promotion, according to the new precise marketing strategy, personalized service packages and preferential activities are pushed to users through user mobile applications, social media, emails, etc. In terms of operation, the number of drones, distribution areas, and maintenance plans are dynamically adjusted according to the optimized revenue analysis model. At the same time, a feedback mechanism is established to collect operation data and user feedback in real time, providing data support for the next round of evaluation and optimization, forming a complete continuous improvement closed loop.

[0184] By applying the optimized model and strategy to actual operation, precise promotion and efficient operation of shared drones can be achieved. This continuous improvement closed loop system can ensure that the operator always masters market dynamics, adjusts strategies in a timely manner, maintains a competitive advantage, and ultimately realizes the continuous improvement of operation efficiency.

[0185] The embodiment also provides a market promotion and revenue analysis system for shared drones, which includes: a portrait module that collects multi-dimensional data of users during the use of shared drones, including personal information, usage habits, preference settings, historical order records, etc., to build detailed user portraits;

[0186] A marketing module generates personalized precise marketing strategies based on the user portraits and uses data analysis techniques to recommend suitable shared drone services and packages to users;

[0187] A modeling module builds a multi-dimensional revenue analysis model based on the collected user data and marketing strategy feedback, considering factors such as usage frequency, flight time, task type, user willingness to pay, and operation cost;

[0188] A prediction module performs revenue prediction based on the multi-dimensional revenue analysis model and optimizes resource allocation according to the prediction results to dynamically adjust the number of drones and distribution areas;

[0189] A feedback module collects user feedback information after using shared drone services, including satisfaction with services, suggestions for improvement, etc., and optimizes user portraits and precise marketing strategies;

[0190] An optimization module continuously optimizes user portraits, precise marketing strategies, and revenue analysis models based on user feedback and revenue analysis results, forming a complete market promotion and revenue analysis closed loop.

[0191] The embodiment also provides a computer device suitable for the market promotion and benefit analysis method of shared drones, including a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions to realize the market promotion and benefit analysis method of shared drones proposed in the above embodiment.

[0192] The computer device can be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner. The wireless manner can be achieved through WIFI, an operator network, NFC (Near Field Communication) or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or a key, trackball or touchpad arranged on the shell of the computer device, or an external keyboard, touchpad or mouse, etc.

[0193] The embodiment also provides a storage medium having a computer program stored thereon, the program being executed by a processor to realize the market promotion and benefit analysis method of shared drones proposed in the above embodiment. The storage medium can be realized by any type of volatile or non-volatile storage device or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic memory, a flash memory, a magnetic disk or an optical disk.

[0194] To sum up, the present application significantly improves the market promotion effect and operation income of shared drones by constructing detailed user portraits and multi-dimensional income analysis models. First, through deep data mining and user portrait technology, precise personalized marketing is achieved, meeting the diverse needs of different user groups, improving user satisfaction and market competitiveness. Second, the multi-dimensional income analysis model considers various factors such as the use frequency, flight time, task type, user willingness to pay and operation cost of the drone, enabling more accurate assessment of the income of shared drones, providing strong support for dynamic resource allocation. In addition, the resource optimization scheme based on income prediction realizes the dynamic adjustment of the number and distribution of drones, improves resource utilization efficiency and reduces operation costs. Finally, through continuous optimization of user portraits, precise marketing strategies and income analysis models, a complete market promotion and income analysis loop is formed, ensuring the long-term stable operation and continuous improvement of the system. These innovations together make the present application significantly practical and innovative in the field of shared drone market promotion and income analysis, effectively improving operation efficiency and economic benefits.

[0195] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and not to limit it. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the present application, which should be covered by the scope of the claims of the present application.

Claims

1. A method for sharing market promotion and benefit analysis of a drone, characterized in that, The method comprises the following steps: Collecting multi-dimensional data of users during the use of shared drones, including personal information, usage habits, preference settings, historical order records, etc., to build detailed user portraits; Based on the user portraits, using data analysis techniques to generate personalized precision marketing strategies and recommend suitable shared drone services and packages to users; According to the collected user data and marketing strategy feedback, a multi-dimensional revenue analysis model is built, considering various factors such as the frequency of use of drones, flight time, task type, user willingness to pay, and operating cost; Based on the multi-dimensional revenue analysis model, revenue prediction is carried out, and resource allocation is optimized according to the prediction results, dynamically adjusting the number of drones and distribution areas; After users use shared drone services, collect user feedback information, including satisfaction with services, suggestions for improvement, etc., and optimize user portraits and precision marketing strategies; According to user feedback and revenue analysis results, continuously optimize user portraits, precision marketing strategies, and revenue analysis models to form a complete market promotion and revenue analysis closed loop. 2.The method of claim 1, wherein the sharing of the UAVs is based on a UAV sharing market and a UAV sharing benefit analysis. The collection of multi-dimensional data of users during the use of shared drones includes personal information, usage habits, preference settings, historical order records, etc., to build detailed user portraits, the specific steps are, Through the reservation platform of shared drones, user mobile applications, and real-time data collection modules during the use of drones, collect users' personal information, usage habits, preference settings, and historical order records; Use data mining techniques to extract key features from the collected data, including users' geographic location, usage frequency, task type preference, and consumption amount; Based on the extracted key features, use clustering analysis algorithms to divide users into different user groups and build user portraits for each group; Store the built user portraits in the user portrait database for subsequent use in precision marketing and revenue analysis. 3.The method of claim 2, wherein: Based on the user portraits, use data analysis techniques to generate personalized precision marketing strategies and recommend suitable shared drone services and packages to users, the specific steps are, For each user group, analyze the features in its user portrait to determine the potential needs and preferences of the group users; According to the potential needs and preferences of the user group, design personalized shared drone service packages, including different drone models, usage time, additional services, etc.; Use machine learning algorithms to predict the response probability of users to different service packages based on user portraits and historical marketing feedback data; According to the predicted response probability, recommend the most likely accepted service package to each user and push it to the user through user mobile applications, emails, etc. 4.The method of claim 3, wherein the sharing of the UAVs is based on a UAV sharing market and a UAV sharing benefit analysis. Collecting shared drone usage data, including flight time, flight distance, task type, drone model, etc. for each use; ​ Collecting data related to revenue, including the amount of payment, the length of use, the type of package, and the discount offered to the user; Based on the collected usage data and revenue data, a multi-dimensional revenue analysis model is established using statistical analysis methods, which includes the usage frequency, flight time, task type, user payment willingness, and operation cost of the drone; The multi-dimensional revenue analysis model is verified and optimized to ensure that the model can accurately reflect the revenue of the shared drone. 5.The method of claim 4, wherein the sharing of the UAVs is based on a UAV sharing market and a UAV sharing benefit analysis. Based on the multi-dimensional revenue analysis model, the revenue is predicted, and the resource allocation is optimized according to the prediction results, and the number of drones and the distribution area are dynamically adjusted, the specific steps are, Using the multi-dimensional revenue analysis model, the future revenue is predicted, and the prediction results include the revenue situation of different time periods, different regions, and different task types; According to the revenue prediction results, determine the direction of resource optimization, including the number of drones, distribution area, maintenance plan, etc. Using optimization algorithms such as genetic algorithms, simulated annealing algorithms, etc., optimize the resource allocation to obtain the optimal resource allocation scheme; Apply the optimized resource allocation scheme to the operation of the shared drone, dynamically adjust the number of drones and the distribution area. 6.The method of claim 5, wherein the sharing of the UAVs is based on a market promotion and a revenue analysis. After the user uses the shared drone service, collect the user's feedback information, including the satisfaction of the service, improvement suggestions, etc., and optimize the user portrait and precision marketing strategy, the specific steps are, After the user uses the shared drone service, collect the user's feedback information through the user mobile application, online questionnaire, customer service feedback, etc., including the satisfaction of the service, improvement suggestions, etc. Use text analysis technology to perform sentiment analysis on user feedback information to determine the overall satisfaction of users with the service; According to the sentiment analysis results and specific suggestions of user feedback, update and optimize the user portrait, and adjust the features and preference settings in the user portrait; According to the updated user portrait, re-evaluate the effectiveness of the precision marketing strategy, and adjust and optimize the marketing strategy. 7.The method of claim 6, wherein the sharing of the UAVs is based on a UAV sharing market and a UAV sharing benefit analysis. According to the user feedback and revenue analysis results, continuously optimize the user portrait, precision marketing strategy, and revenue analysis model to form a complete market promotion and revenue analysis closed loop, the specific steps are, Periodically evaluate the user portrait, precision marketing strategy, and revenue analysis model, including the accuracy of the model, the effectiveness of the marketing strategy, and the user satisfaction; According to the evaluation results, determine the direction and content that needs to be further optimized; Use data analysis and machine learning techniques to continuously optimize the user portrait, precision marketing strategy, and revenue analysis model, and constantly adjust model parameters and strategy content; Apply the optimized user portrait, precision marketing strategy, and revenue analysis model to the market promotion and operation of the shared drone to form a continuously improving closed loop system.

8. A shared UAV market promotion and benefit analysis system based on the shared UAV market promotion and benefit analysis method of any one of claims 1-7. It includes, The portrait module collects multi-dimensional data of users during the use of the shared drone, including personal information, usage habits, preference settings, and historical order records, to build a detailed user portrait; The marketing module generates personalized precision marketing strategies based on the user portrait and recommends suitable shared drone services and packages to users using data analysis techniques; The modeling module builds a multi-dimensional revenue analysis model based on the collected user data and marketing strategy feedback, taking into account factors such as drone usage frequency, flight time, task type, user willingness to pay, and operating costs; The prediction module performs revenue forecasting based on the multi-dimensional revenue analysis model and optimizes resource allocation based on the prediction results to dynamically adjust the number and distribution of drones; The feedback module collects user feedback information after they use shared drone services, including satisfaction with the service and suggestions for improvement, and optimizes the user portrait and precision marketing strategy; The optimization module continuously optimizes the user portrait, precision marketing strategy, and revenue analysis model based on user feedback and revenue analysis results, forming a complete market promotion and revenue analysis loop. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is characterized in that: The processor executes the computer program to implement the steps of the shared drone market promotion and revenue analysis method of any one of claims 1-7.

10. A computer readable storage medium having stored thereon a computer program, characterized in that: The computer program is executed by the processor to implement the steps of the shared drone market promotion and revenue analysis method of any one of claims 1-7.

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