Exhibition decision management method and device, equipment and medium

Abstract

The embodiment of the invention discloses an exhibition decision management method and device, equipment and a medium, and the method comprises the steps: carrying out the butt joint with an association system of a specified exhibition through a preset connection mode, so as to obtain the current data of the specified exhibition in real time; analyzing the current data according to a data mining algorithm to obtain relevance between the current data; performing trend prediction based on the historical data of the specified exhibition and the current data to obtain a key index of the specified exhibition; according to the preset requirement of the specified exhibition, the relevance and the key index, an initial exhibition decision model is set, and the initial exhibition decision model is used for outputting an exhibition decision; according to the historical data, training the initial exhibition decision model to obtain an exhibition decision model meeting requirements; and deploying the exhibition decision model to a specified device so as to perform decision management on a specified exhibition through the specified device.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a method, device, equipment and medium for exhibition decision-making management. Background Art

[0002] Exhibition is a comprehensive activity that integrates exhibition, transaction, conference, forum and other forms. It usually revolves around a specific industry, product or service field, providing a communication platform for exhibitors and visitors. Exhibition decision-making refers to the decision made on the development direction, resource allocation, event arrangement, etc. of the exhibition based on various internal and external factors during the planning, organization and management of exhibition activities. With the rapid development of the exhibition industry, the complexity of decision-making continues to increase.

[0003] Traditional exhibition decisions often rely on the intuitive judgment of experienced organizers or managers, an approach that is somewhat subjective and uncertain. Summary of the invention

[0004] One or more embodiments of this specification provide a method, device, equipment and medium for exhibition decision management, which are used to solve the technical problems raised by the background technology.

[0005] One or more embodiments of this specification adopt the following technical solutions:

[0006] One or more embodiments of this specification provide a method for exhibition decision management, the method comprising:

[0007] Connecting with the relevant system of the designated exhibition through a preset connection method to obtain the current data of the designated exhibition in real time;

[0008] Analyze the current data according to a data mining algorithm to obtain the correlation between the current data;

[0009] Perform trend forecasting based on the historical data and current data of the designated exhibition to obtain key indicators of the designated exhibition;

[0010] According to the preset requirements of the designated exhibition, the correlation and the key indicators, an initial exhibition decision model is set, and the initial exhibition decision model is used to output the exhibition decision;

[0011] According to the historical data, the initial exhibition decision model is trained to obtain an exhibition decision model that meets the requirements;

[0012] The exhibition decision model is deployed to a designated device so as to make decision management on the designated exhibition through the designated device.

[0013] It should be noted that the embodiments of this specification have the following beneficial effects through the above contents:

[0014] Reduce subjectivity and uncertainty: Through real-time data acquisition and historical data analysis, the subjectivity in the decision-making process is reduced, making the decision more objective and scientific.

[0015] Improve decision-making efficiency: Data mining algorithms can quickly analyze large amounts of data, improving the speed and efficiency of decision-making.

[0016] Enhance forecast accuracy: Using historical data and current data for trend forecasting can more accurately predict future market changes and industry trends, thereby making more reasonable decisions.

[0017] Optimize resource allocation: By analyzing key indicators, decision-making models can help organizers allocate resources more effectively, such as booth allocation, marketing budget, etc.

[0018] Furthermore, the system is connected to the relevant system of the designated exhibition through a preset connection method to obtain the current data of the designated exhibition in real time, including:

[0019] Through API interface connection or database connection, it is connected with the exhibitor system, audience service system and ticketing system to obtain the current data of the designated exhibition in real time, including exhibitor behavior data, audience activity data, sales data and exhibition activity data.

[0020] It should be noted that the embodiments of this specification have the following beneficial effects through the above contents:

[0021] Real-time monitoring and response: Real-time acquisition of exhibitor behavior data, visitor activity data, sales data and exhibition activity data enables organizers to instantly monitor the operation of the exhibition and quickly respond to possible problems.

[0022] Data-driven decision making: Based on real-time data, organizers can make more data-driven decisions rather than relying on guesswork or historical experience.

[0023] Optimize exhibitor management: By analyzing exhibitor behavior data, we can better understand exhibitors' needs and preferences, and thus provide services and support that better meet their expectations.

[0024] Further, analyzing the current data according to the data mining algorithm to obtain the correlation between the current data includes:

[0025] According to the pre-written association rule mining algorithm, the exhibitor behavior data, audience activity data, sales data and exhibition activity data are analyzed to obtain the exhibitor's behavior pattern and the audience's activity preference.

[0026] It should be noted that the embodiments of this specification have the following beneficial effects through the above contents:

[0027] Gain in-depth understanding of exhibitor behavior: By analyzing exhibitors’ behavioral patterns, we can understand their motivations, interests, and preferences for participating in the exhibition, and thus provide services and products that better meet their needs.

[0028] Personalized exhibitor services: Based on the exhibitors’ behavior patterns, we can provide personalized services, such as customized booth layout, marketing strategies, etc., to improve the exhibition effect.

[0029] Optimize visitor experience: By analyzing visitors' activity preferences, we can optimize their visiting experience, for example, by guiding them to visit booths of interest through an intelligent navigation system.

[0030] Furthermore, the trend forecasting based on the historical data and the current data of the designated exhibition to obtain the key indicators of the designated exhibition includes:

[0031] Constructing a trend prediction model of the designated exhibition based on the historical data of the designated exhibition;

[0032] The current data is input into the trend prediction model to obtain key indicators of the designated exhibition, including exhibitor participation, audience satisfaction, sales, number of event participants and booth reservations.

[0033] It should be noted that the embodiments of this specification have the following beneficial effects through the above contents:

[0034] Predict future trends: Through the analysis of historical and current data, future exhibitor participation, visitor satisfaction, sales, event attendance and booth bookings can be more accurately predicted, helping organizers make more precise plans.

[0035] Optimize resource allocation: Understanding future trends can help allocate resources more rationally, such as budget, manpower, and space, to avoid excess or shortage of resources.

[0036] Improve exhibitor experience: By predicting exhibitor engagement, organizers can prepare in advance and ensure exhibitors are provided with the necessary support and services to enhance their experience.

[0037] Enhance visitor satisfaction: By predicting visitor satisfaction, event arrangements and content can be optimized to ensure that visitors receive satisfactory services and experiences during the exhibition.

[0038] Increase sales: Sales forecasts help organizers adjust marketing strategies, optimize sales targets and activities, and thus increase overall sales.

[0039] Furthermore, the constructing of a trend prediction model of the designated exhibition based on the historical data of the designated exhibition includes:

[0040] Determine the key indicators that need to be predicted by the trend prediction model;

[0041] Constructing an initial trend prediction model based on the key indicators to be predicted, wherein the initial trend prediction model is a neural network model;

[0042] A training data set is formed based on the historical data, and the initial trend prediction model is trained by the training data set to obtain a trend prediction model that meets the requirements.

[0043] It should be noted that the embodiments of this specification have the following beneficial effects through the above contents:

[0044] Accurate prediction: Through the analysis of neural network models, key indicators of exhibitions, such as exhibitor participation, number of visitors, sales, etc., can be predicted more accurately.

[0045] Adapting to complex relationships: Neural networks are able to capture and process complex nonlinear relationships in data, which is very helpful for understanding the interactions between multiple variables in exhibitions.

[0046] Furthermore, the preset requirements include one or more of increasing exhibitor participation, improving visitor satisfaction, increasing sales, increasing the number of event participants, and improving booth reservations.

[0047] Furthermore, the exhibition decision model is a neural network model.

[0048] One or more embodiments of this specification provide a conference and exhibition decision management device, including:

[0049] A docking unit, which is connected to a system associated with a designated exhibition through a preset connection method to obtain current data of the designated exhibition in real time;

[0050] An analysis unit, analyzing the current data according to a data mining algorithm to obtain the correlation between the current data;

[0051] A prediction unit, performing trend prediction based on the historical data of the designated exhibition and the current data to obtain key indicators of the designated exhibition;

[0052] A decision-making unit, which sets an initial exhibition decision model according to the preset requirements of the designated exhibition, the correlation and the key indicators, wherein the initial exhibition decision model is used to output an exhibition decision;

[0053] A training unit, which trains the initial exhibition decision model according to the historical data to obtain an exhibition decision model that meets the requirements;

[0054] The deployment unit deploys the exhibition decision model to a designated device so as to perform decision management on the designated exhibition through the designated device.

[0055] One or more embodiments of this specification provide a conference and exhibition decision management device, including:

[0056] at least one processor; and,

[0057] a memory communicatively connected to the at least one processor; wherein,

[0058] The memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to:

[0059] Connecting with the relevant system of the designated exhibition through a preset connection method to obtain the current data of the designated exhibition in real time;

[0060] Analyze the current data according to a data mining algorithm to obtain the correlation between the current data;

[0061] Perform trend forecasting based on the historical data and current data of the designated exhibition to obtain key indicators of the designated exhibition;

[0062] According to the preset requirements of the designated exhibition, the correlation and the key indicators, an initial exhibition decision model is set, and the initial exhibition decision model is used to output the exhibition decision;

[0063] According to the historical data, the initial exhibition decision model is trained to obtain an exhibition decision model that meets the requirements;

[0064] The exhibition decision model is deployed to a designated device so as to make decision management on the designated exhibition through the designated device.

[0065] One or more embodiments of this specification provide a non-volatile computer storage medium storing computer executable instructions, which can achieve the following when executed by a computer:

[0066] Connecting with the relevant system of the designated exhibition through a preset connection method to obtain the current data of the designated exhibition in real time;

[0067] Analyze the current data according to a data mining algorithm to obtain the correlation between the current data;

[0068] Perform trend forecasting based on the historical data and current data of the designated exhibition to obtain key indicators of the designated exhibition;

[0069] According to the preset requirements of the designated exhibition, the correlation and the key indicators, an initial exhibition decision model is set, and the initial exhibition decision model is used to output the exhibition decision;

[0070] According to the historical data, the initial exhibition decision model is trained to obtain an exhibition decision model that meets the requirements;

[0071] The exhibition decision model is deployed to a designated device so as to make decision management on the designated exhibition through the designated device.

[0072] At least one of the above technical solutions adopted in the embodiments of this specification can achieve the following beneficial effects:

[0073] Reduce subjectivity and uncertainty: Through real-time data acquisition and historical data analysis, the subjectivity in the decision-making process is reduced, making the decision more objective and scientific.

[0074] Improve decision-making efficiency: Data mining algorithms can quickly analyze large amounts of data, improving the speed and efficiency of decision-making.

[0075] Enhance forecast accuracy: Using historical data and current data for trend forecasting can more accurately predict future market changes and industry trends, thereby making more reasonable decisions.

[0076] Optimize resource allocation: By analyzing key indicators, decision-making models can help organizers allocate resources more effectively, such as booth allocation, marketing budget, etc. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] In order to more clearly illustrate the technical solutions in the embodiments of this specification or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art description. Obviously, the drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. In the drawings:

[0078] Figure 1 A flowchart of a conference and exhibition decision management method provided for one or more embodiments of this specification;

[0079] Figure 2 A schematic diagram of the structure of an exhibition decision-making management device provided for one or more embodiments of this specification;

[0080] Figure 3A schematic diagram of the structure of an exhibition decision management device provided in one or more embodiments of this specification. DETAILED DESCRIPTION

[0081] The embodiments of this specification provide a method, device, equipment and medium for conference and exhibition decision management.

[0082] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0083] Figure 1 This is a flow chart of a conference decision management method provided in one or more embodiments of this specification, and the flow can be executed by a conference decision management system. Some input parameters or intermediate results in the flow allow manual intervention and adjustment to help improve accuracy.

[0084] The method steps of the embodiment of this specification are as follows:

[0085] S101, connecting with a system associated with a designated exhibition through a preset connection method to obtain current data of the designated exhibition in real time.

[0086] In the embodiments of this specification, it is possible to connect to the exhibitor system, audience service system and ticketing system through an API interface connection or a database connection to obtain the current data of the designated exhibition in real time. The current data may include exhibitor behavior data, audience activity data, sales data and exhibition activity data.

[0087] It should be noted that the embodiments of this specification have the following beneficial effects through the above contents:

[0088] Real-time monitoring and response: Real-time acquisition of exhibitor behavior data, visitor activity data, sales data and exhibition activity data enables organizers to instantly monitor the operation of the exhibition and quickly respond to possible problems.

[0089] Data-driven decision making: Based on real-time data, organizers can make more data-driven decisions rather than relying on guesswork or historical experience.

[0090] Optimize exhibitor management: By analyzing exhibitor behavior data, we can better understand exhibitors' needs and preferences, and thus provide services and support that better meet their expectations.

[0091] S102, analyzing the current data according to a data mining algorithm to obtain the correlation between the current data.

[0092] In the embodiment of the present specification, the exhibitor behavior data, the audience activity data, the sales data and the exhibition activity data are analyzed according to the pre-written association rule mining algorithm to obtain the exhibitor behavior pattern and the audience activity preference.

[0093] It should be noted that the above content can be implemented through the following specific implementation plans:

[0094] Data collection and integration: Collect exhibitor behavior data, including exhibitor registration information, exhibition history, product preferences, etc.; collect visitor activity data, such as visitor registration information, tour route, stay time, interactive behavior, etc. Collect sales data, including sales, product sales, customer purchase records, etc.; collect exhibition activity data, such as event schedule, exhibitor feedback, on-site survey results, etc. Integrate the above data to ensure data consistency and analyzability.

[0095] Data preprocessing: clean the data to remove duplicate, erroneous or incomplete data; convert the data format to ensure that all data is stored in a unified format; feature engineering, extract or construct features that help to mine association rules.

[0096] Association rule mining algorithm selection: Select an appropriate association rule mining algorithm, such as Apriori algorithm, Eclat algorithm, FP-growth algorithm or algorithm based on machine learning.

[0097] Algorithm parameter setting: According to data characteristics and business needs, set the parameters of the association rule mining algorithm, such as support threshold, confidence threshold and lift threshold.

[0098] Perform association rule mining: Run an association rule mining algorithm to analyze the integrated data. The algorithm will identify frequently occurring association rules in the data, such as "if visitors visit booth A, then they are likely to visit booth B."

[0099] Result interpretation and analysis: Analyze the mined association rules to understand the behavior patterns of exhibitors and the activity preferences of visitors. Explain the meaning of the rules, such as "most exhibitors start to actively register one month before the event."

[0100] It should be noted that the embodiments of this specification have the following beneficial effects through the above contents:

[0101] Gain in-depth understanding of exhibitor behavior: By analyzing exhibitors’ behavioral patterns, we can understand their motivations, interests, and preferences for participating in the exhibition, and thus provide services and products that better meet their needs.

[0102] Personalized exhibitor services: Based on the exhibitors’ behavior patterns, we can provide personalized services, such as customized booth layout, marketing strategies, etc., to improve the exhibition effect.

[0103] Optimize visitor experience: By analyzing visitors' activity preferences, we can optimize their visiting experience, for example, by guiding them to visit booths of interest through an intelligent navigation system.

[0104] S103, performing trend prediction based on the historical data and the current data of the designated exhibition to obtain key indicators of the designated exhibition.

[0105] In an embodiment of the present specification, a trend prediction model of the designated exhibition can be constructed based on the historical data of the designated exhibition; the current data is input into the trend prediction model to obtain key indicators of the designated exhibition, which include exhibitor participation, audience satisfaction, sales, number of event participants and booth reservations.

[0106] It should be noted that the above content can be implemented through the following specific implementation plans:

[0107] Data collection and processing are as follows:

[0108] Gather historical data: including exhibitor participation, visitor satisfaction, sales, event attendance, and booth bookings for all relevant events over the past few years.

[0109] Collect current data: Get the latest data about the event, including information about potential exhibitors and visitors.

[0110] Data preprocessing is as follows:

[0111] Cleaning data: ensuring the accuracy and completeness of data, and handling missing values ​​and outliers.

[0112] Data transformation: Convert non-numeric data into numerical form for model analysis.

[0113] Feature Engineering: Extracting useful features from the data, such as seasonality, historical growth rates, etc.

[0114] Construct a trend prediction model as follows:

[0115] Select model type: Select an appropriate trend prediction model based on data characteristics, such as time series analysis (ARIMA, exponential smoothing, etc.), machine learning models (linear regression, random forest, etc.), or deep learning models (such as LSTM neural network).

[0116] Model training: Use historical data to train the selected model and adjust model parameters to optimize performance.

[0117] The key indicators are defined as follows:

[0118] Identify key metrics: Identify the key metrics that need to be forecasted, including exhibitor participation, visitor satisfaction, sales, event attendance, and booth bookings.

[0119] The model parameters are adjusted as follows:

[0120] Adjust model parameters based on historical data to ensure that the model can capture trends and periodicity of the data.

[0121] Model validation is as follows:

[0122] Use some historical data to verify the model to ensure the accuracy and reliability of the model's predictions. Adjust the model until a satisfactory prediction effect is achieved on the validation set.

[0123] The current data input and forecast are as follows:

[0124] Input the latest current data into the trained trend prediction model. Use the model to predict key indicators and obtain future trend prediction values.

[0125] The results analysis and interpretation are as follows:

[0126] Analyze forecast results and interpret trends and patterns. Identify possible trend changes, such as a drop in exhibitor participation or an increase in sales.

[0127] It should be noted that the embodiments of this specification have the following beneficial effects through the above contents:

[0128] Predict future trends: Through the analysis of historical and current data, future exhibitor participation, visitor satisfaction, sales, event attendance and booth bookings can be more accurately predicted, helping organizers make more precise plans.

[0129] Optimize resource allocation: Understanding future trends can help allocate resources more rationally, such as budget, manpower, and space, to avoid excess or shortage of resources.

[0130] Improve exhibitor experience: By predicting exhibitor engagement, organizers can prepare in advance and ensure exhibitors are provided with the necessary support and services to enhance their experience.

[0131] Enhance visitor satisfaction: By predicting visitor satisfaction, event arrangements and content can be optimized to ensure that visitors receive satisfactory services and experiences during the exhibition.

[0132] Increase sales: Sales forecasts help organizers adjust marketing strategies, optimize sales targets and activities, and thus increase overall sales.

[0133] Furthermore, when constructing the trend prediction model of the designated exhibition based on the historical data of the designated exhibition, the key indicators that need to be predicted by the trend prediction model can be determined first; an initial trend prediction model is constructed based on the key indicators that need to be predicted, and the initial trend prediction model is a neural network model; a training data set is composed based on the historical data, and the initial trend prediction model is trained by the training data set to obtain a trend prediction model that meets the requirements.

[0134] It should be noted that the embodiments of this specification have the following beneficial effects through the above contents:

[0135] Accurate prediction: Through the analysis of neural network models, key indicators of exhibitions, such as exhibitor participation, number of visitors, sales, etc., can be predicted more accurately.

[0136] Adapting to complex relationships: Neural networks are able to capture and process complex nonlinear relationships in data, which is very helpful for understanding the interactions between multiple variables in exhibitions.

[0137] S104, setting an initial exhibition decision model according to the preset requirements of the designated exhibition, the correlation and the key indicators, wherein the initial exhibition decision model is used to output an exhibition decision.

[0138] S105: training the initial exhibition decision model according to the historical data to obtain an exhibition decision model that meets the requirements.

[0139] In the embodiment of this specification, the preset demand includes one or more of increasing exhibitor participation, improving visitor satisfaction, increasing sales, increasing the number of event participants, and improving booth reservations. The exhibition decision model is a neural network model.

[0140] It should be noted that the exhibition decision output by the exhibition decision model involves multiple aspects, including marketing, operation management, exhibitors and audience management. The following are some specific exhibition decision contents:

[0141] Marketing Decisions:

[0142] 1. Target market positioning: determine the target audience group and exhibitor type.

[0143] 2. Brand promotion strategy: Develop a promotion plan, including media selection, advertising content and budget allocation.

[0144] 3. Ticket and exhibition fee pricing: Set ticket prices and exhibitor booth fees.

[0145] 4. Partnerships: Seek and establish partnerships with relevant industry organizations, media and other institutions.

[0146] 5. Promotional activities: planning and executing promotional activities such as discounts, giveaways, raffles, etc.

[0147] Operational management decisions:

[0148] 1. Exhibition venue selection: Choose a suitable exhibition hall, considering factors such as transportation convenience, completeness of facilities, and cost.

[0149] 2. Exhibition date arrangement: Determine the time of the exhibition to avoid conflicts with similar activities.

[0150] 3. Booth allocation: Booths will be allocated reasonably according to exhibitors’ needs and the layout of the exhibition hall.

[0151] 4. Exhibition event arrangement: planning and arranging seminars, lectures, product launches and other activities.

[0152] 5. On-site service: Ensure that there are sufficient staff on site to provide consultation, guidance, security and other services.

[0153] Exhibitor Management Decisions:

[0154] 1. Exhibitor Recruitment: Develop exhibitor recruitment strategies, including publicity, communication and follow-up.

[0155] 2. Exhibitor services: Provide exhibitor manuals, booth construction support, on-site management and other services.

[0156] 3. Exhibitor communication: Communicate with exhibitors regularly to understand their needs and provide necessary support.

[0157] 4. Exhibitor evaluation: Evaluate the performance of exhibitors and provide a basis for future cooperation.

[0158] Audience Management Decisions:

[0159] 1. Audience attraction strategy: Develop activities and promotional strategies to attract audience participation.

[0160] 2. Audience registration: Design an audience registration system to collect audience information.

[0161] 3. Visitor experience: Ensure that visitors have a good experience at the exhibition, including exhibition hall layout, transportation arrangements, rest areas, etc.

[0162] 4. Audience feedback collection: Collect audience feedback through questionnaires and other means to improve future exhibitions.

[0163] Financial Decisions:

[0164] 1. Budget preparation: Prepare a detailed exhibition budget, including marketing, operations, personnel and other expenses.

[0165] 2. Cost control: monitor and control costs to ensure the rationality of the budget.

[0166] 3. Revenue forecast: forecast exhibition revenue, including ticket sales, exhibitor fees, advertising revenue, etc.

[0167] 4. Profit analysis: Analyze exhibition profits and provide financial reference for future exhibitions.

[0168] Organization and personnel decisions:

[0169] 1. Team building: Establish a professional exhibition team, including project managers, marketing, operations, finance and other positions.

[0170] 2. Staff training: Train team members to ensure they understand the exhibition objectives and processes.

[0171] 3. Risk management: identify potential risks and develop response strategies.

[0172] It should be noted that the above decision-making content needs to be adjusted and optimized based on specific exhibition type, scale, target market and other factors.

[0173] S106, deploying the exhibition decision model to a designated device, so as to perform decision management on the designated exhibition through the designated device.

[0174] It should be noted that the above content can be implemented through the following specific implementation plans:

[0175] Model validation and optimization: Before deployment, ensure that the model has been fully tested and verified to meet the expected performance standards. Based on the test results, make necessary optimizations to the model to ensure its accuracy and reliability.

[0176] Select a deployment platform: Select an appropriate deployment platform based on the hardware configuration and software environment of the specified device. Possible platforms include: cloud services (such as AWS, Azure, Google Cloud); local servers; mobile devices (such as tablets or smartphones)

[0177] Develop deployment scripts: Write a deployment script or use a deployment tool (such as Docker, Kubernetes) to package the model and all dependencies. Make sure the script contains the following:

[0178] The model files and weights; any necessary libraries and tools; and the code or application to run the model.

[0179] Environment configuration: Configure the running environment on the specified device, including installing necessary software and libraries. Ensure that all dependencies required for model running are installed on the device.

[0180] Model deployment: Upload the packaged model and deployment script to the specified device. Use the deployment script or platform to run the model on the specified device.

[0181] The purpose of the embodiments of this specification is to develop a set of exhibition big data reporting and management system to solve the problems of low efficiency and inaccurate data processing in traditional exhibition management methods, and to realize the comprehensive collection, integration, analysis and application of exhibition data through the application of big data technology.

[0182] The technical solutions of the embodiments of this specification are as follows:

[0183] 1. Data Collection and Integration

[0184] 1. Data source access

[0185] The system uses API interfaces, database connections and other methods to connect with data sources such as exhibitor systems, hotel reservation systems, ticketing systems, etc., and extract exhibition-related data in real time.

[0186] 2. Data cleaning

[0187] Perform pre-processing operations such as verification and format conversion on the extracted raw data to ensure the accuracy and consistency of the data.

[0188] 3. Data Integration

[0189] Utilize data warehouse technology (such as Hadoop, Spark, etc.) to integrate the cleaned data into a unified data warehouse to facilitate subsequent data analysis and mining.

[0190] 4. Real-time data stream processing

[0191] For data with high real-time requirements (such as the number of audience admissions, booth traffic data, etc.), real-time data stream processing technology is used for processing.

[0192] 2. Data Analysis and Mining

[0193] 1. Data preprocessing

[0194] Before the data enters the analysis stage, necessary data preprocessing work is performed, such as data conversion, data integration, etc., in order to convert the data into a format suitable for analysis.

[0195] 2. Data mining and association analysis

[0196] Use data mining algorithms (such as cluster analysis, association rule mining, etc.) to analyze exhibition data and discover the correlation and potential rules between data. For example, by analyzing the behavior patterns of exhibitors and the interests and preferences of visitors, we can provide decision support for the optimization of exhibition activities.

[0197] 3. Trend prediction

[0198] Based on historical and current data, trend forecasting is performed using time series analysis, machine learning and other technical means. For example, key indicators such as the visitor flow and the number of exhibitors at exhibitions can be predicted to help managers formulate response strategies in advance.

[0199] 3. Intelligent Decision-making Model Construction

[0200] 1. Decision model design

[0201] Design corresponding decision-making algorithms and models based on the specific needs and goals of exhibition management. These models can be built based on historical data, current data, business rules and other factors.

[0202] 2. Model training and optimization

[0203] Use historical data to train the decision model, and improve the accuracy and robustness of the model through iterative optimization. During the training process, cross-validation, parameter adjustment and other technical means can be used to optimize the performance of the model.

[0204] 3. Model deployment and application

[0205] The trained decision model is deployed into the system, and decision support is provided to managers through user interaction interfaces, etc. Managers can formulate and adjust exhibition management strategies based on the results output by the model.

[0206] 4. User Interaction and Decision Support

[0207] 1. User Interface Design

[0208] Design a friendly and intuitive user interface to facilitate managers to manage and make decisions about exhibitions. The interface should support access from multiple devices (such as PCs, tablets, mobile phones, etc.) and provide rich interactive functions.

[0209] 2. Decision results display

[0210] Through data visualization technology, the output results of the decision model are intuitively presented to managers in the form of charts, dashboards, etc. At the same time, detailed explanations and instructions are provided to help managers understand the meaning and basis of the decision results.

[0211] 3. Decision-making tools

[0212] Provide decision-making support tools, such as decision trees and recommendation algorithms, to help managers formulate exhibition management strategies more accurately. These tools can be personalized according to the needs and preferences of managers.

[0213] The implementation of the exhibition big data declaration management system will bring many beneficial effects, not only improving the intelligent level of exhibition management, but also optimizing the overall operation process of the exhibition, bringing significant value to the exhibition industry. The following are the main beneficial effects of the system:

[0214] 1. Improve exhibition management efficiency:

[0215] Through automated data collection and integration, the workload of manual entry and proofreading is reduced, and the efficiency of data processing is improved.

[0216] The real-time data analysis function enables managers to quickly grasp the real-time situation of the exhibition and make decisions quickly.

[0217] 2. Optimize the quality of exhibition decision-making:

[0218] With the help of data mining and trend forecasting technology, the system can provide managers with accurate and comprehensive data support, making decisions more scientific and reasonable.

[0219] Intelligent decision-making models can provide managers with customized decision-making recommendations based on historical data and business rules, thereby improving the pertinence and effectiveness of decisions.

[0220] 3. Enhance the competitiveness of exhibitions:

[0221] Through systematic analysis of data on exhibitors, visitors, etc., managers can better understand market demand and competitive situation, and thus formulate more precise marketing strategies and exhibition themes.

[0222] Accurate data analysis and forecasting help exhibition organizers predict and respond to market changes in advance, ensuring the smooth progress and success of the exhibition.

[0223] 4. Promote the sustainable development of the exhibition industry:

[0224] Through the application of big data technology, the exhibition industry can grasp market trends and user needs more accurately and promote innovation in exhibition content and form.

[0225] Intelligent management helps to improve the efficiency and effectiveness of exhibitions, reduce operating costs, and provide strong support for the sustainable development of the exhibition industry.

[0226] Figure 2A schematic diagram of the structure of an exhibition decision management device provided for one or more embodiments of this specification includes: a docking unit 201, an analysis unit 202, a prediction unit 203, a decision unit 204, a training unit 205 and a deployment unit 206.

[0227] The docking unit 201 is connected with the associated system of the designated exhibition through a preset connection method to obtain the current data of the designated exhibition in real time;

[0228] An analysis unit 202 analyzes the current data according to a data mining algorithm to obtain the correlation between the current data;

[0229] A prediction unit 203 performs trend prediction based on the historical data of the designated exhibition and the current data to obtain key indicators of the designated exhibition;

[0230] The decision unit 204 sets an initial exhibition decision model according to the preset requirements of the designated exhibition, the correlation and the key indicators, and the initial exhibition decision model is used to output the exhibition decision;

[0231] The training unit 205 trains the initial exhibition decision model according to the historical data to obtain an exhibition decision model that meets the requirements;

[0232] The deployment unit 206 deploys the exhibition decision model to a designated device so as to perform decision management on the designated exhibition through the designated device.

[0233] Figure 3 A schematic diagram of the structure of an exhibition decision management device provided for one or more embodiments of this specification includes:

[0234] at least one processor; and,

[0235] a memory communicatively connected to the at least one processor; wherein,

[0236] The memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to:

[0237] Connecting with the relevant system of the designated exhibition through a preset connection method to obtain the current data of the designated exhibition in real time;

[0238] Analyze the current data according to a data mining algorithm to obtain the correlation between the current data;

[0239] Perform trend forecasting based on the historical data and current data of the designated exhibition to obtain key indicators of the designated exhibition;

[0240] According to the preset requirements of the designated exhibition, the correlation and the key indicators, an initial exhibition decision model is set, and the initial exhibition decision model is used to output the exhibition decision;

[0241] According to the historical data, the initial exhibition decision model is trained to obtain an exhibition decision model that meets the requirements;

[0242] The exhibition decision model is deployed to a designated device so as to make decision management on the designated exhibition through the designated device.

[0243] One or more embodiments of this specification provide a non-volatile computer storage medium storing computer executable instructions, which can achieve the following when executed by a computer:

[0244] Connecting with the relevant system of the designated exhibition through a preset connection method to obtain the current data of the designated exhibition in real time;

[0245] Analyze the current data according to a data mining algorithm to obtain the correlation between the current data;

[0246] Perform trend forecasting based on the historical data and current data of the designated exhibition to obtain key indicators of the designated exhibition;

[0247] According to the preset requirements of the designated exhibition, the correlation and the key indicators, an initial exhibition decision model is set, and the initial exhibition decision model is used to output the exhibition decision;

[0248] According to the historical data, the initial exhibition decision model is trained to obtain an exhibition decision model that meets the requirements;

[0249] The exhibition decision model is deployed to a designated device so as to make decision management on the designated exhibition through the designated device.

[0250] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0251] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0252] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0253] In the embodiments provided in the present application, it should be understood that the disclosed devices / network equipment and methods can be implemented in other ways. For example, the device / network equipment embodiments described above are merely schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0254] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0255] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above units may be implemented in the form of hardware or software.

[0256] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0257] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A conference and exhibition decision management method, characterized in that: include: Connecting with the relevant system of the designated exhibition through a preset connection method to obtain the current data of the designated exhibition in real time; Analyze the current data according to a data mining algorithm to obtain the correlation between the current data; Perform trend forecasting based on the historical data and current data of the designated exhibition to obtain key indicators of the designated exhibition; According to the preset requirements of the designated exhibition, the correlation and the key indicators, an initial exhibition decision model is set, and the initial exhibition decision model is used to output the exhibition decision; According to the historical data, the initial exhibition decision model is trained to obtain an exhibition decision model that meets the requirements; The exhibition decision model is deployed to a designated device so as to make decision management on the designated exhibition through the designated device.

2. The method according to claim 1, characterized in that Connect with the relevant system of the designated exhibition through a preset connection method to obtain the current data of the designated exhibition in real time, including: Through API interface connection or database connection, it is connected with the exhibitor system, audience service system and ticketing system to obtain the current data of the designated exhibition in real time, including exhibitor behavior data, audience activity data, sales data and exhibition activity data.

3. The method according to claim 2, characterized in that The analyzing the current data according to the data mining algorithm to obtain the correlation between the current data includes: According to the pre-written association rule mining algorithm, the exhibitor behavior data, audience activity data, sales data and exhibition activity data are analyzed to obtain the exhibitor's behavior pattern and the audience's activity preference.

4. The method according to claim 2, characterized in that: The trend forecasting based on the historical data and the current data of the designated exhibition to obtain the key indicators of the designated exhibition includes: Constructing a trend prediction model of the designated exhibition based on the historical data of the designated exhibition; The current data is input into the trend prediction model to obtain key indicators of the designated exhibition, including exhibitor participation, audience satisfaction, sales, number of event participants and booth reservations.

5. The method according to claim 4, characterized in that The step of constructing a trend prediction model of the designated exhibition based on the historical data of the designated exhibition includes: Determine the key indicators that need to be predicted by the trend prediction model; Constructing an initial trend prediction model based on the key indicators to be predicted, wherein the initial trend prediction model is a neural network model; A training data set is formed based on the historical data, and the initial trend prediction model is trained by the training data set to obtain a trend prediction model that meets the requirements.

6. The method according to claim 1, characterized in that The preset requirements include one or more of increasing exhibitor participation, improving visitor satisfaction, increasing sales, increasing the number of event participants, and improving booth reservations.

7. The method according to claim 1, characterized in that The exhibition decision model is a neural network model.

8. A conference and exhibition decision management device, characterized in that: include: A docking unit, which is connected to a system associated with a designated exhibition through a preset connection method to obtain current data of the designated exhibition in real time; An analysis unit, analyzing the current data according to a data mining algorithm to obtain the correlation between the current data; A prediction unit, performing trend prediction based on the historical data of the designated exhibition and the current data to obtain key indicators of the designated exhibition; A decision-making unit, which sets an initial exhibition decision model according to the preset requirements of the designated exhibition, the correlation and the key indicators, wherein the initial exhibition decision model is used to output an exhibition decision; A training unit, which trains the initial exhibition decision model according to the historical data to obtain an exhibition decision model that meets the requirements; The deployment unit deploys the exhibition decision model to a designated device so as to perform decision management on the designated exhibition through the designated device.

9. A conference and exhibition decision management device, characterized in that: include: at least one processor; as well as, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to: Connecting with the relevant system of the designated exhibition through a preset connection method to obtain the current data of the designated exhibition in real time; Analyze the current data according to a data mining algorithm to obtain the correlation between the current data; Perform trend forecasting based on the historical data and current data of the designated exhibition to obtain key indicators of the designated exhibition; According to the preset requirements of the designated exhibition, the correlation and the key indicators, an initial exhibition decision model is set, and the initial exhibition decision model is used to output the exhibition decision; According to the historical data, the initial exhibition decision model is trained to obtain an exhibition decision model that meets the requirements; The exhibition decision model is deployed to a designated device so as to make decision management on the designated exhibition through the designated device.

10. A non-volatile computer storage medium, characterized in that: Computer executable instructions are stored, and when the computer executable instructions are executed by a computer, the following can be achieved: Connecting with the relevant system of the designated exhibition through a preset connection method to obtain the current data of the designated exhibition in real time; Analyze the current data according to a data mining algorithm to obtain the correlation between the current data; Perform trend forecasting based on the historical data and current data of the designated exhibition to obtain key indicators of the designated exhibition; According to the preset requirements of the designated exhibition, the correlation and the key indicators, an initial exhibition decision model is set, and the initial exhibition decision model is used to output the exhibition decision; According to the historical data, the initial exhibition decision model is trained to obtain an exhibition decision model that meets the requirements; The exhibition decision model is deployed to a designated device so as to make decision management on the designated exhibition through the designated device.

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