A shared ship control system for smart cultural tourism

Abstract

The present invention relates to the field of management technology, and specifically discloses a shared ship control system for smart cultural tourism, which is used to solve the problem of how to improve the real-time processing capability and response speed of an electronic fence system during peak periods. The control system includes a smart cruise ship monitoring and dispatching command center, a smart wharf and ship terminal equipment, a smart cruise ship big data analysis and management platform, a smart cruise ship ticketing management platform, a smart cruise ship data access application and outdoor equipment, and also includes a data stream continuity analysis module, a data stream flow analysis module and a data stream transmission analysis module. The control system uses 5G+ technology to monitor cruise ship data in real time, identify peak period bottlenecks, optimize dispatching strategies, ensure electronic fence reminders, improve management efficiency and tourist experience, and enhance system stability and response speed.

Description

Technical Field

[0001] The present invention relates to the field of management technology, and more specifically, to a shared ship control system for smart cultural tourism. Background Art

[0002] With the development of smart cities and smart tourism, improving tourism experience and management efficiency has become an important development trend. Many tourist attractions and cities have begun to explore the implementation of smart cruise projects. In particular, the introduction of 5G+ technology has provided strong technical support for these smart cruise projects. 5G+ technology refers to the integration and enhancement of the fifth generation of mobile communication technology with other advanced technologies. 5G technology itself has faster transmission speeds, lower latency, larger network capacity and higher connection density, and the "+" part usually refers to the combination of 5G technology with the Internet of Things (IoT), artificial intelligence (AI), cloud computing and edge computing. Through this integration, it can not only provide ultra-high-speed and low-latency network connections, but also realize more intelligent and efficient application scenarios, bringing new experiences and management methods to smart cities and smart tourism. In the lake areas of tourist attractions where the smart cruise project has been implemented, there are a large number of sightseeing cruise ships and self-driving cruise ships. Tourists can book and rent cruise ships through the ticket management platform. The cruise ships are equipped with 5G+ positioning terminals and cameras, which can achieve real-time monitoring and positioning. At the same time, the terminal management system can dispatch and command cruise ships in real time through 5G+ technology. During peak periods, the number of tourists in the lake area increases dramatically, and cruise ships are used frequently. In particular, the electronic fence system faces tremendous pressure. The electronic fence is used to ensure that cruise ships do not enter prohibited areas and to issue timely reminders when cruise ships approach the border. However, during peak periods, due to the simultaneous approach of a large number of cruise ships to the electronic fence, the system needs to process a large amount of positioning data and reminder information in a very short time. How to improve the real-time processing capability and response speed of the electronic fence system during peak periods to ensure that the system can issue timely and accurate reminders to cruise ships approaching the border, thereby avoiding accidents, has become a technical problem that needs to be solved urgently. Summary of the invention

[0003] In order to overcome the above-mentioned defects of the prior art, the present invention provides a shared ship control system for smart cultural tourism, which uses 5G+ technology to monitor cruise ship data in real time, identify peak bottlenecks, optimize scheduling strategies, ensure electronic fence reminders, improve management efficiency and tourist experience, and enhance system stability and response speed.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] A shared ship control system for smart cultural tourism, including a smart cruise ship monitoring and dispatching command center, smart docks and ship terminal equipment, a smart cruise ship big data analysis and management platform, a smart cruise ship ticketing management platform, a smart cruise ship data access application and outdoor equipment. The smart dock terminal equipment collects the location, status and video data of the cruise ship in real time through the positioning terminal and camera on the cruise ship, and transmits it to the smart cruise ship monitoring and dispatching command center through the 5G network. The smart cruise ship monitoring and dispatching command center receives the positioning data, status data and video data from the smart dock terminal equipment, performs data cleaning and preprocessing, filters out noise and invalid data, and stores the preprocessed data in the database. The smart cruise ship big data analysis and management platform uses big data analysis technology to analyze the preprocessed historical data and real-time data, generates decision suggestions and optimization strategies based on the analysis results, and transmits them to the smart cruise ship monitoring and dispatching command center. The smart monitoring and dispatching command center receives the decision suggestions and optimization strategies of the smart cruise ship big data analysis and management platform. The smart cruise data access application receives reminder information and dispatch instructions from the smart cruise dispatch command center, sends electronic fence reminders, track viewing and navigation services to tourists, and assists tourists in renting and returning cruise ships. Outdoor equipment monitors the operating status of cruise ships in real time through positioning terminals and cameras, collects data and transmits it to the smart cruise monitoring and dispatch command center. The smart cruise ticketing management platform simplifies ticket booking and leasing, and is connected to the smart cruise monitoring and dispatch command center, the smart cruise big data analysis management platform, the smart terminal and ship terminal equipment in real time through data sharing and communication networks. The smart terminal and ship terminal equipment, the smart cruise monitoring and dispatch command center and the smart cruise data access application are all connected to a data flow continuous analysis module for analyzing the balance and continuity of data flows. The smart cruise big data analysis management platform and the smart cruise monitoring and dispatch command center are both connected to a data flow flow analysis module and a data flow transmission analysis module.

[0006] As a further solution of the present invention, in the data flow continuous analysis module, based on the data flow continuous analysis model, the divergence of the data flow velocity field is zero, and the inflow and outflow of the data flow at the monitored point are balanced and continuous. The equation of the data flow continuous analysis model is:

[0007] ;

[0008] Where: The speed at which data flows in the system is collected through smart dock and ship terminal equipment, smart cruise ship monitoring and dispatching command center, and smart cruise ship data access application. is the divergence of the data flow velocity field, , is the component of data flow speed in the direction of smart terminal and ship terminal equipment, is the coordinate of the data flow in the direction of the smart terminal and ship terminal equipment, is the component of data flow speed in the direction of the smart cruise ship monitoring and dispatching command center, is the coordinate of the data flow in the direction of the smart cruise ship monitoring and dispatching center, The data flow is the component of the smart cruise data access application direction. It is the coordinate of the data flow in the direction of the smart cruise data access application.

[0009] As a further solution of the present invention, in the data flow continuous analysis module, the solution process of the data flow continuous analysis model includes:

[0010] Step 11, data collection and data preprocessing: The location, status and video data of the cruise ship are collected in real time through smart terminal and ship terminal equipment, smart cruise ship monitoring and dispatching command center and smart cruise ship data access application, and the data is transmitted through the 5G network. Without increasing or reducing the data stream dimension and the number of data streams, the collected data is cleaned and preprocessed to filter out noise and invalid data;

[0011] Step 12: Non-dimensionalization of data: transform the coordinates , , Non-dimensionalization , , ,in , , The coordinates are , , The non-dimensional coordinates of The characteristic length is taken as the mean value of the distance between the smart terminal and ship terminal equipment, the smart cruise monitoring and dispatching command center and the smart cruise data access application, and the speed is non-dimensionalized as , The data diffusion rate is determined by historical data analysis and network bandwidth testing. The data flow continuous analysis equation is converted into a dimensionless form by applying non-dimensional variables. ;

[0012] Step 13, discretization equation: discretize the data flow continuous analysis equation using the finite difference method;

[0013] Step 14, setting initial conditions and boundary conditions: setting the initial value of the data flow speed to the real-time measurement data, and setting the data inflow or outflow speed at the boundary;

[0014] Step 15, solving the discretized equation: Select the Jacobi iteration method to solve the discretized equation, initialize the initial value of the data flow speed, adjust the value of the data flow speed through iteration until the discretized data flow continuity analysis equation is satisfied, and check the convergence of the solution;

[0015] Step 17, real-time monitoring and adjustment: By real-time monitoring of changes in data flow, timely analysis is performed, and data processing and transmission strategies are adjusted in real time according to the analysis results to provide feedback and optimization.

[0016] As a further solution of the present invention, in the data flow analysis module, the equation based on the data flow analysis model is solved. In the smart cruise ship monitoring and dispatching command center, the solution results are used to optimize data flow and avoid data congestion. In the smart cruise ship big data analysis and management platform, the solution results are used to analyze data flow behavior, identify potential bottlenecks during peak hours, and optimize data flow paths and speeds. The equation of the data flow analysis model is:

[0017] ;

[0018] Where: is the time variable considered in the data flow analysis process, Data density is obtained through statistics and analysis of historical data and real-time data. is the data pressure, which is calculated based on the data transmission status of each node in the system. is the data flow viscosity, obtained through system performance testing and historical data analysis. is the driving force constant of the data flow, which is set according to system requirements and actual conditions. The data expansion coefficient is obtained by analyzing historical data, counting the data request volumes during peak periods and non-peak periods, and calculating the difference ratio of the data request volumes. The data density difference is obtained by calculating the data density difference by counting the data traffic during peak hours and non-peak hours. is the time rate of change of data flow speed, The impact of data flow on its own speed in dimensional space, is the data pressure gradient term, which indicates the influence of the data pressure gradient on data flow. is the data viscosity diffusion term, which represents the viscosity resistance of data flow, The driving effect of the difference in data density between peak and non-peak periods on data flow.

[0019] As a further solution of the present invention, in the data flow analysis module, the equation solving process of the data flow analysis model is:

[0020] Step 21, data collection and preprocessing: The data flow speed, pressure and density data are collected in real time through the smart cruise big data analysis management platform and the smart cruise monitoring and dispatching command center, and the 5G network is used to realize the real-time transmission of the collected data. The collected data is cleaned and preprocessed without increasing or reducing the data flow dimension and the number of data flows;

[0021] Step 22, non-dimensional processing: transform the coordinates , , Non-dimensionalization , , ,in , , The coordinates are , , The non-dimensional coordinates of The characteristic length is taken as the mean value of the distance between the smart terminal and ship terminal equipment, the smart cruise monitoring and dispatching command center and the smart cruise data access application, and the speed is non-dimensionalized as , The data diffusion rate is determined by historical data analysis and network bandwidth testing. The equations of the data flow analysis model are transformed into dimensionless form by applying non-dimensional variables. ;

[0022] Step 24, discretizing equations: discretizing equations of the data flow analysis model using a finite difference method;

[0023] Step 25, setting initial conditions and boundary conditions: setting the initial value of the data flow speed, and setting the inflow and outflow speeds at the boundary;

[0024] Step 26, solving the discretized equation: using Jacobi iteration method to solve the discretized equation, initializing the initial value of the data flow velocity, adjusting the value of the data flow through iteration until the discretized momentum equation is satisfied, and checking the convergence of the solution;

[0025] Step 27, result application and optimization: The smart cruise ship monitoring and dispatching command center uses the solution results to optimize data flow, and adjusts data processing and transmission strategies based on real-time data and analysis results. The smart cruise ship big data analysis management platform uses the solution results to analyze data flow behavior and identify potential bottlenecks during peak periods.

[0026] As a further solution of the present invention, in the data flow analysis module, the solution results are used to analyze the data flow behavior, and the potential bottlenecks during peak periods are identified as follows: the regional data flow rate classification threshold is set based on experience, and the area greater than or equal to the data flow rate classification threshold is a high flow rate area, and the data transmission channel is unobstructed; the area below the data flow rate classification threshold is a stagnant area, and there is data congestion and processing bottlenecks.

[0027] As a further solution of the present invention, in the data stream transmission analysis module, the equation of the data stream transmission analysis model is used to analyze the distribution and transmission of data density, optimize the scheduling strategy, ensure efficient processing of a large number of data requests, and ensure the stability of the system during peak periods. The equation of the data stream transmission analysis model is:

[0028] ;

[0029] Where: is the time rate of change of data density, is the effect of data flow speed on data density in space, It is the spread and homogenization trend of data in different areas of the system.

[0030] As a further solution of the present invention, the equation solving process of the data flow transmission analysis model includes:

[0031] Step 31, data collection and preprocessing: collect data flow speed and density data in real time through the smart cruise big data analysis management platform and the smart cruise monitoring and dispatching command center, use the 5G network to achieve real-time transmission of collected data, and clean and preprocess the collected data without increasing or reducing the data flow dimension and the number of data flows;

[0032] Step 32, non-dimensional processing: transform the coordinates , , Non-dimensionalization , , ,in , , The coordinates are , , The non-dimensional coordinates of The characteristic length is taken as the mean value of the distance between the smart terminal and ship terminal equipment, the smart cruise monitoring and dispatching command center and the smart cruise data access application, and the speed is non-dimensionalized as , The data diffusion rate is determined by historical data analysis and network bandwidth testing. Non-dimensionalization ,in, For variables The non-dimensional variables, is the reference data density, is the reference data density difference, and By setting actual requirements, the equations of the data flow transmission analysis model are transformed into dimensionless form using non-dimensional variables. ;

[0033] Step 34, discretizing equations: discretizing equations of the data flow transmission analysis model using a finite difference method;

[0034] Step 35, setting initial conditions and boundary conditions: setting the initial value of the data flow speed, and setting the data density inflow and data density outflow at the boundary;

[0035] Step 36, solving the discretized equation: using Jacobi iteration method to solve the discretized equation, initializing the initial value of the data density, adjusting the value of the data density by iteration until the discretized momentum equation is satisfied, and checking the convergence of the solution;

[0036] Step 37, result application and optimization: The smart cruise ship monitoring and dispatching command center uses the solution results to optimize the data transmission path, and adjusts the data processing and transmission strategies based on the real-time data and analysis results. The smart cruise ship big data analysis and management platform uses the solution results to analyze the distribution of data density, identify potential bottlenecks during peak hours, and optimize the dispatching strategy based on the analysis results.

[0037] As a further solution of the present invention, in the data flow transmission analysis module, the data density distribution is analyzed according to the solution results, and the method for identifying potential bottlenecks during peak periods is as follows: the regional data density classification threshold is set according to experience, and the area greater than or equal to the data density classification threshold is a high-density area, where data requests are concentrated and require optimized processing and scheduling strategies. The area below the data density classification threshold is a low-density area, where there is less data and the system resource utilization rate is not high.

[0038] As a further solution of the present invention, in the smart cruise ship big data processing platform, the product of the peak and non-peak data density, data expansion coefficient, cube of characteristic length and driving constant of data flow is used as the numerator, and the product of data flow viscosity and data diffusion rate is used as the denominator. The numerical value of the fraction composed of the two is used as the data flow bottleneck prediction and analysis index to predict the potential bottleneck position during the peak period. The formula of the data flow bottleneck prediction and analysis index is:

[0039] ;

[0040] Where: Predict analytical metrics for data flow bottlenecks, The spatial scale of the system processing data is set according to the system architecture and network topology. , These are the data densities during peak and off-peak periods, respectively.

[0041] Compared with the prior art, the technical effect of the system proposed in the present invention is as follows: by combining the ultra-high speed, low latency, large capacity and high connection density of 5G, as well as advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), cloud computing and edge computing, the present invention enables the system to monitor and locate cruise ships in real time and process massive data. The smart cruise ship monitoring and dispatching command center and the smart cruise ship big data analysis and management platform use data stream continuity analysis modules, data stream flow analysis modules and data stream transmission analysis modules to conduct in-depth analysis of the balance, continuity, flow behavior and transmission conditions of the data stream, especially during peak periods, to quickly identify and predict potential bottlenecks. By optimizing the dispatching strategy and the cruise ship operation path, it ensures that the electronic fence system can issue reminders to cruise ships approaching the boundary in a timely and accurate manner, effectively avoiding accidents. This not only improves management efficiency and tourist experience, but also enhances the stability and response speed of the system during peak periods, providing strong technical support for smart cities and smart tourism. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 The figure is a system block diagram of the system proposed in the present invention. DETAILED DESCRIPTION

[0043] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings of the present invention. Obviously, the described technical solution is only a part of the present invention. Based on the technical solution of the present invention, all other technical solutions obtained by ordinary technicians in the field without creative work are within the scope of protection of the present invention.

[0044] like Figure 1As shown, the present invention proposes a shared ship control system for smart cultural tourism, including a smart cruise ship monitoring and dispatching command center, a smart wharf and ship terminal equipment, a smart cruise ship big data analysis and management platform, a smart cruise ship ticketing management platform, a smart cruise ship data access application and outdoor equipment. The smart wharf terminal equipment collects the position, status and video data of the cruise ship in real time through the positioning terminal and camera on the cruise ship, and transmits it to the smart cruise ship monitoring and dispatching command center through the 5G network. The smart cruise ship monitoring and dispatching command center receives the positioning data, status data and video data from the smart wharf terminal equipment, performs data cleaning and preprocessing, filters out noise and invalid data, and stores the preprocessed data in a database. The smart cruise ship big data analysis and management platform uses big data analysis technology to analyze the preprocessed historical data and real-time data, generates decision suggestions and optimization strategies based on the analysis results, and transmits them to the smart cruise ship monitoring and dispatching command center. The smart monitoring and dispatching command center receives the decision suggestions of the smart cruise ship big data analysis and management platform and optimization strategies, issue dispatch instructions, optimize the operation path and speed of the cruise ship, and track the execution of the cruise ship in real time. The smart cruise data access application receives reminder information and dispatch instructions from the smart cruise dispatch command center, sends electronic fence reminders, trajectory viewing and navigation services to tourists, and assists tourists in renting and returning cruise ships. Outdoor equipment monitors the operation status of the cruise ship in real time through positioning terminals and cameras, collects data and transmits it to the smart cruise monitoring and dispatch command center. The smart cruise ticketing management platform simplifies ticket booking and leasing, and is connected to the smart cruise monitoring and dispatch command center, the smart cruise big data analysis management platform, the smart terminal and ship terminal equipment in real time through data sharing and communication networks. The smart terminal and ship terminal equipment, the smart cruise monitoring and dispatch command center and the smart cruise data access application are all connected with a data flow continuity analysis module for analyzing the balance and continuity of the data flow. The smart cruise big data analysis management platform and the smart cruise monitoring and dispatch command center are both connected with a data flow flow analysis module and a data flow transmission analysis module.

[0045] The present invention combines the ultra-high speed, low latency, large capacity and high connection density of 5G, as well as advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), cloud computing and edge computing, so that the system can monitor and locate cruise ships in real time and process massive data. The smart cruise ship monitoring and dispatching command center and the smart cruise ship big data analysis and management platform use data stream continuity analysis modules, data stream flow analysis modules and data stream transmission analysis modules to conduct in-depth analysis of the balance, continuity, flow behavior and transmission conditions of data streams, especially during peak periods, and can quickly identify and predict potential bottlenecks. By optimizing the scheduling strategy and cruise ship operation path, it ensures that the electronic fence system can promptly and accurately issue reminders to cruise ships approaching the boundary, effectively avoiding accidents. This not only improves management efficiency and tourist experience, but also enhances the stability and response speed of the system during peak periods, providing strong technical support for smart cities and smart tourism.

[0046] It should be noted that in the data flow continuous analysis module, the data flow continuous analysis model indicates that the divergence of the data flow velocity field is zero, and the inflow and outflow of the data flow at the monitored point are balanced and continuous. The equation of the data flow continuous analysis model is:

[0047] ;

[0048] Where: The speed at which data flows in the system is collected through smart dock and ship terminal equipment, smart cruise ship monitoring and dispatching command center, and smart cruise ship data access application. is the divergence of the data flow velocity field, , is the component of data flow speed in the direction of smart terminal and ship terminal equipment, is the coordinate of the data flow in the direction of the smart terminal and ship terminal equipment, is the component of data flow speed in the direction of the smart cruise ship monitoring and dispatching command center, is the coordinate of the data flow in the direction of the smart cruise ship monitoring and dispatching center, The data flow is the component of the smart cruise data access application direction. It is the coordinate of the data flow in the direction of the smart cruise data access application.

[0049] The core of this equation is to ensure that the data flow at any monitoring point in the system is balanced and continuous. Specifically: at any monitoring point, the inflow and outflow of data flow are equal, there is no data backlog or loss; the data flow is continuous in space, without sudden interruptions or mutations. The analysis steps include:

[0050] Data collection: Through smart docks and ship terminal equipment, smart cruise ship monitoring and dispatching command center and smart cruise ship data access applications, the data flow speed and direction components are collected in real time.

[0051] Calculate divergence: Use the collected data to calculate the divergence of the data flow velocity field at each monitoring point.

[0052] Determine balance and continuity: Determine the balance and continuity of the data flow at each monitoring point by judging whether the divergence is zero.

[0053] By real-time monitoring of the balance and continuity of data flow, anomalies in the data flow, such as data blockage or loss, can be discovered in time, so as to optimize the scheduling and ensure the efficient operation of the system; by ensuring the balance and continuity of data flow, the blockage and delay in data flow are reduced, and the overall response speed of the system is improved; the continuity equation ensures the stable flow of data flow in the entire system, avoiding the system instability caused by uneven data flow; by optimizing scheduling and improving the system response speed, the use experience of tourists is improved, and the timeliness and accuracy of cruise ship scheduling are ensured. During peak hours, due to a large number of cruise ships approaching the electronic fence at the same time, the system needs to process a large amount of positioning data and reminder information in a very short time. Through the data flow continuous analysis model, the balance and continuity of the data flow are ensured, and the system can process a large amount of real-time data more efficiently, avoiding data blockage and processing delays. By real-time monitoring and analyzing the data flow, the system can more accurately determine the location of the cruise ship, issue electronic fence reminders in time, and avoid accidents. After identifying potential data flow problems, the system can dynamically adjust the cruise ship's operation path and speed to avoid congestion during peak hours.

[0054] It should be noted that in the data flow continuous analysis module, the solution process of the data flow continuous analysis model includes:

[0055] Step 11, data collection and data preprocessing: The location, status and video data of the cruise ship are collected in real time through smart terminal and ship terminal equipment, smart cruise ship monitoring and dispatching command center and smart cruise ship data access application, and the data is transmitted through the 5G network. Without increasing or reducing the data stream dimension and the number of data streams, the collected data is cleaned and preprocessed to filter out noise and invalid data;

[0056] Step 12: Non-dimensionalization of data: transform the coordinates , , Non-dimensionalization , , ,in , , The coordinates are , , The non-dimensional coordinates of The characteristic length is taken as the mean value of the distance between the smart terminal and ship terminal equipment, the smart cruise monitoring and dispatching command center and the smart cruise data access application, and the speed is non-dimensionalized as , The data diffusion rate is determined by historical data analysis and network bandwidth testing. The data flow continuous analysis equation is converted into a dimensionless form by applying non-dimensional variables. ;

[0057] Step 13, discretization equation: discretize the data flow continuous analysis equation using the finite difference method;

[0058] Step 14, setting initial conditions and boundary conditions: setting the initial value of the data flow speed to the real-time measurement data, and setting the data inflow or outflow speed at the boundary;

[0059] Step 15, solving the discretized equation: Select the Jacobi iteration method to solve the discretized equation, initialize the initial value of the data flow speed, adjust the value of the data flow speed through iteration until the discretized data flow continuity analysis equation is satisfied, and check the convergence of the solution;

[0060] Step 17, real-time monitoring and adjustment: By real-time monitoring of changes in data flow, timely analysis is performed, and data processing and transmission strategies are adjusted in real time according to the analysis results to provide feedback and optimization.

[0061] Through the above steps, the data flow continuity analysis module ensures the balance and continuity of the data flow in the system. First, the quality and accuracy of the input data are ensured by real-time data collection and preprocessing. Then, through non-dimensional processing and discretization of equations, the complex continuity equation becomes suitable for numerical solution, simplifying the calculation process. The initial conditions and boundary conditions are set, and the Jacobi iteration method is used to solve the discretization equation to ensure the accuracy and convergence of the solution. Finally, through real-time monitoring and adjustment, the system can respond to changes in data flow in a timely manner and optimize data processing and transmission strategies. This method improves the system's processing capacity and response speed, enhances stability during peak periods, and ensures the efficiency and accuracy of cruise ship scheduling and management.

[0062] It should be noted that in the data flow analysis module, the equations based on the data flow analysis model are solved. In the smart cruise monitoring and dispatching command center, the solution results are used to optimize data flow and avoid data congestion. In the smart cruise big data analysis and management platform, the solution results are used to analyze data flow behavior, identify potential bottlenecks during peak hours, and optimize data flow paths and speeds. The equation of the data flow analysis model is:

[0063] ;

[0064] Where: is the time variable considered in the data flow analysis process, Data density is obtained through statistics and analysis of historical data and real-time data. is the data pressure, which is calculated based on the data transmission status of each node in the system. is the data flow viscosity, obtained through system performance testing and historical data analysis. is the driving force constant of the data flow, which is set according to system requirements and actual conditions. The data expansion coefficient is obtained by analyzing historical data, counting the data request volumes during peak periods and non-peak periods, and calculating the difference ratio of the data request volumes. The data density difference is obtained by calculating the data density difference by counting the data traffic during peak hours and non-peak hours. is the time rate of change of data flow speed, The impact of data flow on its own speed in dimensional space, is the data pressure gradient term, which indicates the influence of the data pressure gradient on data flow. is the data viscosity diffusion term, which represents the viscosity resistance of data flow, The driving effect of the difference in data density between peak and non-peak periods on data flow.

[0065] In the data flow analysis module, by solving equations based on the data flow analysis model, the system can optimize data flow, avoid data congestion, identify potential bottlenecks during peak hours in the smart cruise big data analysis management platform, and optimize the data flow path and speed. By introducing time variables, data density, data pressure, data flow viscosity, data expansion coefficient, data density difference, and driving force constant, the model can describe the dynamic behavior of data flow and its influencing factors in detail. Using these parameters, the system can accurately simulate and analyze the data flow during peak hours, identify bottleneck areas, and formulate optimization strategies. The time rate of change of data flow speed and the spatial convection term enable the system to dynamically adjust the data flow path and speed; the data pressure gradient term and the viscosity diffusion term ensure the balance and stability of data flow. Through these analyses, the system can optimize the data transmission path in real time during peak hours, alleviate congestion, improve response speed, and ensure the stability and efficiency of the system during peak hours, thereby effectively solving the processing delays and potential accident risks caused by a large number of cruise ships approaching the electronic fence at the same time.

[0066] It should be noted that in the data flow analysis module, the equation solving process of the data flow analysis model is:

[0067] Step 21, data collection and preprocessing: The data flow speed, pressure and density data are collected in real time through the smart cruise big data analysis management platform and the smart cruise monitoring and dispatching command center, and the 5G network is used to realize the real-time transmission of the collected data. The collected data is cleaned and preprocessed without increasing or reducing the data flow dimension and the number of data flows;

[0068] Step 22, non-dimensional processing: transform the coordinates , , Non-dimensionalization , , ,in , , The coordinates are , , The non-dimensional coordinates of The characteristic length is taken as the mean value of the distance between the smart terminal and ship terminal equipment, the smart cruise monitoring and dispatching command center and the smart cruise data access application, and the speed is non-dimensionalized as , The data diffusion rate is determined by historical data analysis and network bandwidth testing. The equations of the data flow analysis model are transformed into dimensionless form by applying non-dimensional variables. ;

[0069] Step 24, discretizing equations: discretizing equations of the data flow analysis model using a finite difference method;

[0070] Step 25, setting initial conditions and boundary conditions: setting the initial value of the data flow speed, and setting the inflow and outflow speeds at the boundary;

[0071] Step 26, solving the discretized equation: using Jacobi iteration method to solve the discretized equation, initializing the initial value of the data flow velocity, adjusting the value of the data flow through iteration until the discretized momentum equation is satisfied, and checking the convergence of the solution;

[0072] Step 27, result application and optimization: The smart cruise ship monitoring and dispatching command center uses the solution results to optimize data flow, and adjusts data processing and transmission strategies based on real-time data and analysis results. The smart cruise ship big data analysis management platform uses the solution results to analyze data flow behavior and identify potential bottlenecks during peak periods.

[0073] Through the above steps, the data flow analysis module can collect and preprocess data in real time, perform non-dimensional processing and solve discretized equations, so as to accurately simulate and analyze data flow behavior. The Jacobi iteration method is used to solve the discretized equations to ensure the accuracy and convergence of the solution. The smart cruise ship monitoring and dispatching command center and the big data analysis management platform use the solution results to optimize data flow, identify and predict potential bottlenecks during peak hours, and adjust data processing and transmission strategies in real time. This process improves the system's processing capacity and response speed, improves stability and efficiency during peak hours, and effectively solves data congestion and delay problems.

[0074] It should be noted that in the data flow analysis module, the solution results are used to analyze the data flow behavior and identify potential bottlenecks during peak periods in the following way: the regional data flow rate classification threshold is set based on experience. The area with a flow rate greater than or equal to the data flow rate classification threshold is a high-flow rate area, and the data transmission channel is unobstructed. The area with a flow rate lower than the data flow rate classification threshold is a stagnant area, and there is data congestion and processing bottlenecks.

[0075] It should be noted that in the data flow transmission analysis module, the equation of the data flow transmission analysis model is used to analyze the distribution and transmission of data density, optimize the scheduling strategy, ensure efficient processing of a large number of data requests, and ensure the stability of the system during peak periods. The equation of the data flow transmission analysis model is:

[0076] ;

[0077] Where: is the time rate of change of data density, is the effect of data flow speed on data density in space, It is the spread and homogenization trend of data in different areas of the system.

[0078] In the data flow transmission analysis module, by solving the data flow transmission analysis model equation, the distribution and transmission of data density can be effectively analyzed. This model can describe the dynamic changes of data density in the system. Through non-dimensionalization and discretization processing, it can transform complex continuous equations into a form suitable for numerical solution. The Jacobi iteration method is used to solve the discretized equation to ensure the accuracy and convergence of the solution. The smart cruise ship monitoring and dispatching command center and the big data analysis management platform can use the solution results to optimize the data flow path and speed in real time, identify and predict potential bottlenecks during peak hours, and adjust data processing and transmission strategies. This process improves the system's processing capacity and response speed during peak hours, enhances the system's stability and efficiency, effectively solves the delays and potential accident risks caused by data congestion, and ensures that the system can operate stably and efficiently during peak hours.

[0079] It should be noted that the equation solving process of the data flow transmission analysis model includes:

[0080] Step 31, data collection and preprocessing: collect data flow speed and density data in real time through the smart cruise big data analysis management platform and the smart cruise monitoring and dispatching command center, use the 5G network to achieve real-time transmission of collected data, and clean and preprocess the collected data without increasing or reducing the data flow dimension and the number of data flows;

[0081] Step 32, non-dimensional processing: transform the coordinates , , Non-dimensionalization , , ,in , , The coordinates are , , The non-dimensional coordinates of The characteristic length is taken as the mean value of the distance between the smart terminal and ship terminal equipment, the smart cruise monitoring and dispatching command center and the smart cruise data access application, and the speed is non-dimensionalized as , The data diffusion rate is determined by historical data analysis and network bandwidth testing. Non-dimensionalization ,in, For variables The non-dimensional variables, is the reference data density, is the reference data density difference, and By setting actual requirements, the equations of the data flow transmission analysis model are transformed into dimensionless form using non-dimensional variables. ;

[0082] Step 34, discretizing equations: discretizing equations of the data flow transmission analysis model using a finite difference method;

[0083] Step 35, setting initial conditions and boundary conditions: setting the initial value of the data flow speed, setting the data density inflow and data density outflow at the boundary;

[0084] Step 36, solving the discretized equation: using Jacobi iteration method to solve the discretized equation, initializing the initial value of the data density, adjusting the value of the data density by iteration until the discretized momentum equation is satisfied, and checking the convergence of the solution;

[0085] Step 37, result application and optimization: The smart cruise ship monitoring and dispatching command center uses the solution results to optimize the data transmission path, and adjusts the data processing and transmission strategies based on the real-time data and analysis results. The smart cruise ship big data analysis and management platform uses the solution results to analyze the distribution of data density, identify potential bottlenecks during peak hours, and optimize the dispatching strategy based on the analysis results.

[0086] It should be noted that in the data flow transmission analysis module, the data density distribution is analyzed according to the solution results, and the method of identifying potential bottlenecks during peak periods is: the regional data density classification threshold is set based on experience. The area greater than or equal to the data density classification threshold is a high-density area, where data requests are concentrated and require optimized processing and scheduling strategies. The area below the data density classification threshold is a low-density area, with less data and low system resource utilization.

[0087] It should be noted that in the smart cruise ship big data processing platform, the product of the peak and non-peak data density, data expansion coefficient, characteristic length cube and data flow driving constant is used as the numerator, and the product of data flow viscosity and data diffusion rate is used as the denominator. The value of the fraction composed of the two is used as the data flow bottleneck prediction and analysis indicator to predict the potential bottleneck position during the peak period. The formula for the data flow bottleneck prediction and analysis indicator is:

[0088] ;

[0089] Where: Predict analytical metrics for data flow bottlenecks, The spatial scale of the system processing data is set according to the system architecture and network topology. , These are the data densities during peak and off-peak periods, respectively.

[0090] The calculation of the data flow bottleneck prediction analysis index provides a quantitative tool for the smart cruise big data processing platform to predict the potential bottleneck position during peak hours. This index uses the data density difference between peak and non-peak hours, the data expansion coefficient, the cube of the characteristic length and the driving constant of the data flow as the numerator, and the product of the data flow viscosity and the data diffusion rate as the denominator to obtain a dimensionless value that can reflect the relative strength of the driving force and resistance in the data flow. This index provides a quantitative method to analyze the bottleneck of data flow, identify potential congestion areas during peak hours through numerical calculation, and help understand the behavior of the system under different load conditions by analyzing the relative relationship between the driving force (data density difference, expansion coefficient, characteristic length) and resistance (flow viscosity, diffusion rate). The size of the index value can be used to determine which areas of the system need to be optimized, thereby guiding specific scheduling and data transmission strategies. This index exists simultaneously with the three equations to form a comprehensive data flow analysis framework, which is of great necessity: the equations in the data flow continuous analysis model ensure the balance and continuity of data flow and avoid local data backlogs. The equations in the data flow analysis model analyze the dynamic behavior of data flow and identify flow bottlenecks during peak hours. The equations in the data transmission analysis model ensure that data density is evenly propagated and distributed in the system. Through the combined application of data flow bottleneck prediction analysis indicators and the three equations, the smart cruise big data processing platform can more accurately identify potential bottlenecks during peak periods and optimize data flow paths and speeds, thereby improving the system's processing capacity and stability and ensuring efficient operation during peak periods. This multi-dimensional analysis and prediction provides a comprehensive optimization basis for the system, significantly improving the system's response speed and stability.

[0091] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0092] Finally: The above description is only the preferred technical solution of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A shared ship control system for smart cultural tourism, including a smart cruise ship monitoring and dispatching command center, a smart wharf and ship terminal equipment, characterized in that: It also includes a smart cruise big data analysis management platform, a smart cruise ticketing management platform, a smart cruise data access application and outdoor equipment. The smart dock and ship terminal equipment collect the location, status and video data of the cruise ship in real time through the positioning terminal and camera on the cruise ship, and transmit it to the smart cruise monitoring and dispatching command center through the 5G network. The smart cruise monitoring and dispatching command center receives the positioning data, status data and video data from the smart dock and ship terminal equipment, performs data cleaning and preprocessing, filters out noise and invalid data, and stores the preprocessed data in the database. The smart cruise big data analysis management platform uses big data analysis technology to analyze the preprocessed historical data and real-time data, and generates decision suggestions and optimization strategies based on the analysis results, and transmits them to the smart cruise monitoring and dispatching command center. The smart cruise monitoring and dispatching command center receives the decision suggestions and optimization strategies of the smart cruise big data analysis management platform, issues dispatch instructions, and optimizes the operation path of the cruise ship. and speed, and track the execution of the cruise ship in real time. The smart cruise ship data access application receives reminder information and dispatch instructions from the smart cruise ship dispatch command center, sends electronic fence reminders, track viewing and navigation services to tourists, and assists tourists in renting and returning cruise ships. Outdoor equipment monitors the operating status of the cruise ship in real time through positioning terminals and cameras, collects data and transmits it to the smart cruise ship monitoring and dispatch command center. The smart cruise ticket management platform simplifies ticket booking and leasing, and is connected to the smart cruise ship monitoring and dispatch command center, the smart cruise big data analysis management platform, the smart terminal and ship terminal equipment in real time through data sharing and communication networks. The smart terminal and ship terminal equipment, the smart cruise ship monitoring and dispatch command center and the smart cruise data access application are all connected to a data flow continuity analysis module for analyzing the balance and continuity of the data flow. The smart cruise big data analysis management platform and the smart cruise ship monitoring and dispatch command center are both connected to a data flow flow analysis module and a data flow transmission analysis module. In the data flow continuous analysis module, the data flow continuous analysis model indicates that the divergence of the data flow velocity field is zero, and the inflow and outflow of the data flow at the monitored point are balanced and continuous. The equation of the data flow continuous analysis model is: Where: v is the flow speed of data in the system, which is collected through smart terminal and ship terminal equipment, smart cruise ship monitoring and dispatching command center and smart cruise ship data access application. is the divergence of the data flow velocity field, v x is the component of the data flow velocity in the direction of the smart terminal and ship terminal equipment, x is the coordinate of the data flow in the direction of the smart terminal and ship terminal equipment, and v y is the component of the data flow velocity in the direction of the smart cruise ship monitoring and dispatching command center, y is the coordinate of the data flow in the direction of the smart cruise ship monitoring and dispatching center, and v z is the component of the data flow velocity in the direction of the smart cruise data access application, and z is the coordinate of the data flow in the direction of the smart cruise data access application; Ensure that the inflow and outflow of data are balanced, avoid data backlog or loss, ensure the continuous flow of data, avoid data flow interruption, analyze the changes in data flow in real time, and promptly discover and solve potential problems; In the data flow continuous analysis module, the solution process of the data flow continuous analysis model includes: Step 11, data collection and data preprocessing: The location, status and video data of the cruise ship are collected in real time through smart terminal and ship terminal equipment, smart cruise ship monitoring and dispatching command center and smart cruise ship data access application, and the data is transmitted through the 5G network. Without increasing or reducing the data stream dimension and the number of data streams, the collected data is cleaned and preprocessed to filter out noise and invalid data; Step 12: Non-dimensionalization of data: Non-dimensionalize the coordinates x, y, and z into where x * ,y * 、z * are the non-dimensionalized coordinates of x, y, and z, respectively. L is the characteristic length. The average distance between the smart terminal and ship terminal equipment, the smart cruise monitoring and dispatching command center, and the smart cruise data access application is taken as the characteristic length. The speed is non-dimensionalized as α is the data diffusion rate, which is determined by historical data analysis and network bandwidth testing. Non-dimensional variables are applied to transform the data flow continuous analysis equation into a dimensionless form. Step 13, discretization equation: discretize the data flow continuous analysis equation using the finite difference method; Step 14, setting initial conditions and boundary conditions: setting the initial value of the data flow speed to the real-time measurement data, and setting the data inflow or outflow speed at the boundary; Step 15, solving the discretized equation: Select the Jacobi iteration method to solve the discretized equation, initialize the initial value of the data flow speed, adjust the value of the data flow speed through iteration until the discretized data flow continuity analysis equation is satisfied, and check the convergence of the solution; Step 17, real-time monitoring and adjustment: by real-time monitoring of data flow changes, timely analysis, real-time adjustment of data processing and transmission strategies based on the analysis results, feedback and optimization; In the data flow analysis module, the equations based on the data flow analysis model are solved. In the smart cruise monitoring and dispatching command center, the solution results are used to optimize data flow and avoid data congestion. In the smart cruise big data analysis and management platform, the solution results are used to analyze data flow behavior, identify potential bottlenecks during peak hours, and optimize data flow paths and speeds. The equation of the data flow analysis model is: Where: t is the time variable considered in the data flow analysis process, ρ is the data density, which is obtained through statistics and analysis of historical data and real-time data, p is the data pressure, which is calculated through the data transmission of each node in the system, v is the data flow viscosity, which is obtained through system performance testing and historical data analysis, g is the driving force constant of the data flow, which is set according to system requirements and actual conditions, β is the data expansion coefficient, which is obtained by analyzing historical data, counting the data request volume during peak and non-peak periods, and calculating the difference ratio of the data request volume, ΔD is the data density difference, which is obtained by counting the data flow during peak and non-peak periods and calculating the data density difference, is the time rate of change of data flow speed, The impact of data flow on its own speed in dimensional space, is the data pressure gradient term, which indicates the influence of the data pressure gradient on data flow. is the data viscosity diffusion term, which represents the viscosity resistance of data flow, and gβ(ΔD) is the driving effect of the difference in data density between peak and non-peak periods on data flow; In the data flow analysis module, the equation solving process of the data flow analysis model is: Step 21, data collection and preprocessing: The data flow speed, pressure and density data are collected in real time through the smart cruise big data analysis management platform and the smart cruise monitoring and dispatching command center, and the 5G network is used to realize the real-time transmission of the collected data. The collected data is cleaned and preprocessed without increasing or reducing the data flow dimension and the number of data flows; Step 22, non-dimensional processing: non-dimensionalize the coordinates x, y, and z into where x * ,y * 、z * are the non-dimensionalized coordinates of x, y, and z, respectively. L is the characteristic length. The average distance between the smart terminal and ship terminal equipment, the smart cruise monitoring and dispatching command center, and the smart cruise data access application is taken as the characteristic length. The speed is non-dimensionalized as α is the data diffusion rate, which is determined by historical data analysis and network bandwidth testing. Non-dimensional variables are applied to transform the equation of the data flow analysis model into a dimensionless form. Step 24, discretizing equations: discretizing equations of the data flow analysis model using a finite difference method; Step 25, setting initial conditions and boundary conditions: setting the initial value of the data flow speed, and setting the inflow and outflow speeds at the boundary; Step 26, solving the discretized equation: using Jacobi iteration method to solve the discretized equation, initializing the initial value of the data flow velocity, adjusting the value of the data flow through iteration until the discretized momentum equation is satisfied, and checking the convergence of the solution; Step 27, result application and optimization: The smart cruise ship monitoring and dispatching command center uses the solution results to optimize data flow, adjusts data processing and transmission strategies based on real-time data and analysis results, and the smart cruise ship big data analysis management platform uses the solution results to analyze data flow behavior and identify potential bottlenecks during peak periods; In the data flow analysis module, the solution results are used to analyze the data flow behavior and identify potential bottlenecks during peak hours. The method is as follows: according to experience, the regional data flow rate classification threshold is set. The area with a data flow rate greater than or equal to the data flow rate classification threshold is a high flow rate area, and the data transmission channel is unobstructed. The area with a data flow rate lower than the data flow rate classification threshold is a stagnant area, and there is data congestion and processing bottlenecks. In the data flow transmission analysis module, the equation of the data flow transmission analysis model is used to analyze the distribution and transmission of data density, optimize the scheduling strategy, ensure efficient processing of a large number of data requests, and ensure the stability of the system during peak periods. The equation of the data flow transmission analysis model is: Where: is the time rate of change of data density, is the effect of data flow speed on data density in space, It is the spread and homogenization trend of data in different areas of the system.

2. A shared ship control system for smart cultural tourism according to claim 1, characterized in that: The equation solving process of the data flow analysis model includes: Step 31, data collection and preprocessing: collect data flow speed and density data in real time through the smart cruise big data analysis management platform and the smart cruise monitoring and dispatching command center, use the 5G network to achieve real-time transmission of collected data, and clean and preprocess the collected data without increasing or reducing the data flow dimension and the number of data flows; Step 32, non-dimensionalization processing: non-dimensionalize the coordinates x, y, and z into where x * ,y * 、z * are the non-dimensionalized coordinates of x, y, and z, respectively. L is the characteristic length. The average distance between the smart terminal and ship terminal equipment, the smart cruise monitoring and dispatching command center, and the smart cruise data access application is taken as the characteristic length. The speed is non-dimensionalized as α is the data diffusion rate, which is determined by historical data analysis and network bandwidth testing. The data density D is non-dimensionalized as Among them, D * is the non-dimensional variable of variable D, D0 is the reference data density, D ref is the reference data density difference, D0 and D ref By setting actual requirements, the equations of the data flow transmission analysis model are transformed into dimensionless form using non-dimensional variables. Step 34, discretizing equations: discretizing equations of the data flow transmission analysis model using a finite difference method; Step 35, setting initial conditions and boundary conditions: setting the initial value of the data flow speed, setting the data density inflow and data density outflow at the boundary; Step 36, solving the discretized equation: using Jacobi iteration method to solve the discretized equation, initializing the initial value of the data density, adjusting the value of the data density by iteration until the discretized momentum equation is satisfied, and checking the convergence of the solution; Step 37, result application and optimization: The smart cruise ship monitoring and dispatching command center uses the solution results to optimize the data transmission path, and adjusts the data processing and transmission strategies based on the real-time data and analysis results. The smart cruise ship big data analysis and management platform uses the solution results to analyze the distribution of data density, identify potential bottlenecks during peak hours, and optimize the dispatching strategy based on the analysis results.

3. A shared ship control system for smart cultural tourism according to claim 2, characterized in that: In the data flow transmission analysis module, the data density distribution is analyzed according to the solution results, and the method to identify potential bottlenecks during peak periods is as follows: the regional data density classification threshold is set based on experience. The area with a data density classification threshold greater than or equal to the data density classification threshold is a high-density area, where data requests are concentrated and require optimized processing and scheduling strategies. The area with a data density classification threshold below the data density classification threshold is a low-density area, where system resource utilization is not high.

4. A shared ship control system for smart cultural tourism according to claim 3, characterized in that: In the smart cruise big data analysis and management platform, the peak and non-peak data density, data expansion coefficient, the cube of the spatial scale of the system processing data, and the product of the driving constant of the data flow are used as the numerator, and the product of the data flow viscosity and the data diffusion rate is used as the denominator. The value of the fraction composed of the two is used as the data flow bottleneck prediction and analysis indicator to predict the potential bottleneck position during the peak period. The formula for the data flow bottleneck prediction and analysis indicator is: Where: R data is the data flow bottleneck prediction and analysis indicator, d is the spatial scale of system data processing, which is set according to the system architecture and network topology. b , D t These are the data densities during peak and off-peak periods, respectively.

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