Intelligent scenic spot management and control system and method based on Internet of Things and big data analysis

Abstract

The invention discloses a scenic spot intelligent management and control system and method based on Internet of Things and big data analysis, and particularly relates to the technical field of scenic spot intelligent management and control. The real-time performance of data transmission between devices in a scenic spot is predicted and modeled through a big data analysis algorithm, the data transmission rate between the devices is adjusted according to a prediction result, and meanwhile, the working state of the devices is dynamically scheduled in combination with an energy management system, the energy efficiency of the devices is optimized, and tourist flow data is recollected. The flow fluctuation is analyzed by using a machine learning algorithm, so that accurate personnel dispersion and resource scheduling optimization decision are realized, the problems of insufficient network bandwidth, data transmission delay and packet loss in a scenic spot are effectively solved, the real-time performance and accuracy of data transmission are improved, and especially in a remote area, the real-time performance of the scenic spot is improved. The intelligent management and control system of the scenic area can reflect dynamic conditions in real time, and the management efficiency and tourist experience are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent management and control of scenic spots, and in particular to an intelligent management and control system and method for scenic spots based on the Internet of Things and big data analysis. Background Art

[0002] With the rapid development of Internet technology, the Internet of Things (IoT) and big data analysis have gradually become important supporting technologies for various industries. As a core component of the tourism industry, scenic spot management faces challenges such as large tourist flow, decentralized management, and diverse service needs. Traditional scenic spot management methods often rely on manual experience and have a low degree of informatization, resulting in unreasonable resource allocation, low management efficiency, and even affecting the experience of tourists. Based on IoT technology, scenic spots can achieve real-time monitoring of various facilities and tourist behaviors, collect big data through sensors, smart devices, etc., and use big data analysis technology to deeply mine these data to provide accurate prediction and decision support.

[0003] The prior art has the following deficiencies: In the process of realizing smart management and control of scenic spots based on the Internet of Things and big data analysis, after the Internet of Things devices are widely deployed in the scenic environment, how to solve the problems of insufficient network bandwidth, data transmission delay and packet loss between devices while ensuring data integrity, so that big data analysis can reflect the dynamic status of the scenic spot in real time, has become a key difficulty in realizing smart management and control. Especially in remote areas of some scenic spots, poor network signals and device battery life issues further reduce the real-time nature of data transmission. Summary of the invention

[0004] The purpose of the present invention is to provide a scenic spot intelligent management and control system and method based on the Internet of Things and big data analysis to solve the shortcomings of the background technology.

[0005] In order to achieve the above-mentioned purpose, the present invention provides the following technical solution: a method for intelligent management and control of scenic spots based on the Internet of Things and big data analysis, comprising the following steps: S1: deploy a variety of IoT sensors and smart devices in the scenic area, including network environment monitoring equipment, tourist behavior monitoring equipment, security monitoring equipment and resource management equipment, for real-time collection of multi-source data in the scenic area; S2: Transmitting multi-source data collected by various sensors to a central data processing platform via a wireless communication network, wherein the multi-source data includes tourist flow data, network environment data, and facility status data; S3: Analyze the collected network environment data and facility status data in real time, and predict and model the real-time performance of data transmission between smart devices in the scenic area through big data analysis algorithms; S4: Based on the prediction results, the data transmission rate between smart devices in the scenic area is adjusted in real time, including the communication protocol for reducing power consumption of smart devices in the scenic area, and the dynamic scheduling of the working status of tourist behavior monitoring equipment and security monitoring equipment in the scenic area in combination with the energy management system; S5: Re-collect tourist flow data through the adjusted tourist behavior monitoring equipment and security monitoring equipment, analyze tourist flow fluctuations through machine learning algorithms, and make optimal decisions on personnel guidance and resource scheduling based on the analysis results.

[0006] Preferably, in S3, the bandwidth usage rate drift value is generated by real-time monitoring and analysis of the bandwidth usage rate, wherein the bandwidth usage rate drift value is obtained by: Bandwidth usage It refers to the ratio between the bandwidth actually used by the network and the total available bandwidth: ; In the formula, represents the bandwidth used in the network at time point t, Indicates the total network bandwidth; Collect network bandwidth usage in real time at each time interval , and calculate the bandwidth usage at the current moment ; Set a time window Δt, calculate the change in bandwidth usage within the time window, and calculate the difference between the bandwidth usage at each time point and the previous time point: ; Indicates the change in bandwidth usage at time point t, Indicates the bandwidth usage at the last point in time; The bandwidth usage drift value EQS is an indicator that measures the bandwidth usage change rate. Its calculation formula is: ; Where Δt is the time interval for calculating bandwidth changes, and n is the length of the statistical window.

[0007] Preferably, after analyzing the load condition of the facility with abnormal usage status, a facility load abnormality value is generated, and the method for obtaining the facility load abnormality value is: Set real-time load value Indicates the actual load of the facility at time h, the rated load value Indicates the rated load of the facility under normal operation, load utilization rate Indicates the ratio of the facility's real-time load value to the rated load value, used to measure the current load status: ; By comparing the real-time load value with the historical load trend, the degree of load deviation is calculated. Based on the historical operation data of the facility, the load mean and standard deviation within a time window are calculated: ; In the formula, is the historical average of the facility load, is the historical standard deviation of the facility load, and m is the number of historical data samples; the real-time load deviation value is calculated by the deviation between the real-time load value and the historical mean. : ; Set a threshold λ. When the load deviation value exceeds the standard deviation of the threshold multiple, the load state of the facility is considered abnormal: if , then the facility load is determined to be abnormal; the facility load abnormality value WAS is calculated, and the specific formula is: .

[0008] Preferably, the bandwidth usage drift value and the facility load anomaly value are normalized, and the real-time coefficient of data transmission between smart devices in the scenic area is calculated based on the normalized bandwidth usage drift value and the facility load anomaly value.

[0009] Preferably, the acquired real-time coefficient of data transmission between smart devices in the scenic area is compared with the real-time reference threshold set according to historical data. If the real-time coefficient of data transmission between smart devices in the scenic area is greater than or equal to the set real-time reference threshold, it means that the real-time of data transmission between smart devices in the scenic area is high, and no warning signal is generated at this time; if the real-time coefficient of data transmission between smart devices in the scenic area is less than the set real-time reference threshold, it means that the real-time of data transmission between smart devices in the scenic area is low, and a warning signal is generated at this time, and the data transmission rate between smart devices in the scenic area needs to be adjusted.

[0010] Preferably, in S4, the data transmission rate It is an important indicator of data transmission between smart devices and needs to be adjusted according to real-time requirements. The dynamic adjustment formula of data transmission rate is: ;in: is the dynamic data transmission rate at time t, is the basic transmission rate, the maximum supported transmission rate of the device, is the abnormal value of facility load, is the maximum value of the abnormal value of the facility load, is the bandwidth usage drift value, is the maximum value of the bandwidth usage drift value, α and β are dynamic adjustment coefficients, which are set according to the priority and working requirements of the device; The communication protocol selection formula is: ;in: The communication protocol power consumption of the device at time t is: is the power consumption adjustment coefficient; the working state of the dynamic scheduling equipment, the specific adjustment formula is: ; In the formula, is the working status of the equipment at time t, and is the scheduling coefficient; Devices with high load and low real-time requirements: reduce the data transmission rate, enable low-power communication protocols, and put the device into low-power standby or shutdown state; Devices with low load and high real-time requirements: increase the data transmission rate, use high-power protocols, ensure that the device is always on and guarantee real-time data transmission.

[0011] Preferably, in S5, the flow data is modeled and predicted by cluster analysis to identify the fluctuation pattern of tourist flow and predict the future flow fluctuation trend, including: Before cluster analysis, the collected tourist flow data needs to be preprocessed; Select the number of clusters, randomly select K initial centers, and assign each tourist flow data point to the nearest cluster center based on the distance metric; calculate the average value of each cluster and update the cluster center; repeat until the cluster center no longer changes. After clustering is completed, the scenic spot analyzes different tourist flow fluctuation patterns based on the flow pattern characteristics of each cluster.

[0012] Preferably, the tourist flow fluctuation modes include: peak mode, trough mode, stable mode and abnormal mode.

[0013] The present invention also provides a scenic spot intelligent management and control system based on the Internet of Things and big data analysis, including a data acquisition module, a data transmission module, a modeling module, an adjustment module and a machine learning analysis module; Data collection module: a variety of IoT sensors and smart devices are deployed in the scenic area, including network environment monitoring equipment, tourist behavior monitoring equipment, security monitoring equipment and resource management equipment, which are used to collect multi-source data in the scenic area in real time; Data transmission module: transmits multi-source data collected by various sensors to the central data processing platform through a wireless communication network, wherein the multi-source data includes tourist flow data, network environment data and facility status data; Modeling module: conducts real-time analysis on the collected network environment data and facility status data, and predicts and models the real-time performance of data transmission between smart devices in the scenic area through big data analysis algorithms; Adjustment module: Based on the prediction results, the data transmission rate between smart devices in the scenic area is adjusted in real time, including the communication protocol for reducing power consumption of smart devices in the scenic area, and the dynamic scheduling of the working status of tourist behavior monitoring equipment and security monitoring equipment in the scenic area in combination with the energy management system; Machine learning analysis module: re-collect tourist flow data through adjusted tourist behavior monitoring equipment and security monitoring equipment, analyze tourist flow fluctuations through machine learning algorithms, and make optimized decisions on personnel guidance and resource scheduling based on the analysis results.

[0014] In the above technical solution, the technical effects and advantages provided by the present invention are: 1. Through real-time collection and analysis of multi-source data, the present invention can timely predict the real-time data transmission between intelligent devices in the scenic area, adjust the data transmission rate based on the prediction results, optimize the communication protocol and working status of the equipment, thereby ensuring the efficiency and real-time of data transmission, especially in remote areas of the scenic area, significantly improving the performance and reliability of the intelligent management and control system. Through in-depth analysis and cluster modeling of tourist flow data, the present invention can also identify different flow fluctuation patterns, implement accurate personnel guidance and resource scheduling, and improve the intelligent level of scenic area management and tourist experience.

[0015] 2. The present invention introduces a dynamic adjustment mechanism, based on real-time monitoring and analysis of bandwidth usage drift values ​​and facility load anomalies, to provide more flexible adjustment capabilities for data transmission between smart devices in the scenic area, significantly improving the coordination efficiency between devices. At the same time, the intelligent scheduling system can dynamically optimize according to the real-time load and traffic fluctuation requirements of the facilities, which not only avoids excessive resource consumption, but also ensures the stable operation of equipment with high real-time requirements, ensuring efficient and stable operation of the scenic area. Overall, the present invention solves the problems of data transmission delay and uneven equipment load in traditional scenic area management, has strong innovation and application value, and is particularly suitable for intelligent management and control systems in large-scale and complex scenic areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0017] Figure 1 The figure is a flow chart of the method of the present invention.

[0018] Figure 2 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] Example 1, please refer to Figure 1 As shown, the scenic spot intelligent management and control method based on the Internet of Things and big data analysis described in this embodiment includes the following steps: S1: deploy a variety of IoT sensors and smart devices in the scenic area, including network environment monitoring equipment, tourist behavior monitoring equipment, security monitoring equipment and resource management equipment, for real-time collection of multi-source data in the scenic area; S2: Transmitting multi-source data collected by various sensors to a central data processing platform via a wireless communication network, wherein the multi-source data includes tourist flow data, network environment data, and facility status data; S3: Analyze the collected network environment data and facility status data in real time, and predict and model the real-time performance of data transmission between smart devices in the scenic area through big data analysis algorithms; S4: Based on the prediction results, the data transmission rate between smart devices in the scenic area is adjusted in real time, including the communication protocol for reducing power consumption of smart devices in the scenic area, and the dynamic scheduling of the working status of tourist behavior monitoring equipment and security monitoring equipment in the scenic area in combination with the energy management system; S5: Re-collect tourist flow data through the adjusted tourist behavior monitoring equipment and security monitoring equipment, analyze tourist flow fluctuations through machine learning algorithms, and make optimal decisions on personnel guidance and resource scheduling based on the analysis results.

[0021] In S1, network environment monitoring equipment is mainly used to monitor the network status in the scenic area to ensure the smooth communication of IoT devices. These devices include: Wireless network quality monitoring equipment: used to monitor the coverage, bandwidth, delay and signal strength of wireless communication networks such as Wi-Fi, LoRa, NB-IoT in the scenic area. These data are essential to ensure the stable connection and data transmission of IoT devices. Network bandwidth usage monitor: real-time monitoring of network bandwidth usage, identification of data transmission bottlenecks, and ensuring that measures are taken to adjust bandwidth or optimize transmission methods when the network is highly loaded. Through these devices, the management system can promptly detect areas with unstable network signals or insufficient bandwidth, adjust network configuration, and ensure the real-time and stability of data transmission.

[0022] Tourist behavior monitoring equipment comprehensively monitors the behavior of tourists in scenic spots through the combination of sensors and intelligent technology. These devices include: People flow monitoring equipment: such as infrared sensors, video surveillance, Wi-Fi signal probes, etc., which are used to detect the flow of tourists in scenic spots in real time, helping managers to understand the density and distribution of tourists in scenic spots. This plays an important role in tourist guidance, queue management and scenic spot security. Tourist trajectory tracking equipment: Through mobile phone apps, Bluetooth beacons, Wi-Fi positioning and other technologies, real-time tracking of tourists' movement trajectories in scenic spots. This can not only help managers optimize the tourist experience, but also detect abnormal situations in time, such as tourists staying or getting lost. Tourist behavior analysis equipment: Combined with smart cameras, facial recognition, behavior recognition algorithms and other technologies, analyze tourists' behavior patterns, such as stay time, interest point preferences, social behavior, etc., to provide more accurate personalized services and marketing strategies. Tourist behavior monitoring equipment can provide scenic area managers with accurate data on tourist activities, and provide support for subsequent resource scheduling, service optimization and emergency response.

[0023] Security monitoring equipment is mainly used to ensure the safety of tourists and the order of scenic spots, including: Video monitoring equipment: high-definition cameras are arranged in key areas of scenic spots for all-weather video monitoring to identify suspicious behaviors and abnormal events in real time. For example, through video monitoring, it is possible to detect gatherings of tourists, violent incidents, sudden fires, etc. Intelligent access control system: controls the entrances and exits of scenic spots to ensure that only tourists with valid tickets or authorization can enter, and prevents illegal entry. Environmental sensors: such as smoke, temperature, humidity, gas sensors, etc., monitor the environmental safety conditions in the scenic area in real time, and warn of possible fires or harmful gas leaks in advance. Security monitoring equipment not only ensures the safety of tourists, but also assists scenic area managers to quickly respond to emergencies and reduce safety hazards.

[0024] Resource management equipment is used to monitor the status of various infrastructure and public resources in the scenic area in real time to ensure efficient use and management of resources. These devices include: Facility status monitoring equipment: monitors various facilities in the scenic area, including toilets, parking lots, street lights, elevators, trash cans, etc. Through sensors, the use and fault status of these facilities are monitored and timely feedback is given to the management system to ensure that the facilities are maintained in a timely manner and reasonably allocated. For example, when the toilet equipment is overflowing, the system will automatically notify the management staff to clean and repair it.

[0025] Energy management equipment: used to monitor the energy consumption of electricity, water, gas and other energy sources in the scenic area to ensure the efficient use of energy resources. Through real-time monitoring of energy consumption, scenic area managers can perform energy scheduling and energy-saving optimization according to demand. Garbage management equipment: through sensors to monitor the capacity of trash cans, ensure timely garbage removal, keep the scenic area clean, and avoid garbage accumulation that has a negative impact on the tourist experience. Resource management equipment can effectively optimize the allocation of various resources in the scenic area, reduce operating costs, and improve the service quality of the scenic area.

[0026] In S2, tourist flow data is one of the most basic and important data in scenic area management. By monitoring the flow of tourists in real time through sensors and smart devices, the scenic area management platform can effectively understand the distribution and density of tourists. This type of data is mainly collected in the following ways: Infrared sensors and video surveillance: Infrared sensors or smart cameras in scenic areas can monitor the number and distribution of tourists in real time. Infrared sensors can count the number of tourists passing through a specific area, and video surveillance systems can help determine tourist flow trends by analyzing the number and behavior of tourists in the video stream.

[0027] Wi-Fi and Bluetooth beacons: By deploying Wi-Fi hotspots or Bluetooth beacons in scenic spots, the system can track the movement of tourists in real time. The movement paths and stop locations of tourists can be captured through their mobile phone signals, thus generating a detailed distribution map of tourist traffic.

[0028] Entrance and exit traffic monitoring: Sensors are installed at each entrance and exit of the scenic area to count the number of tourists entering and leaving in real time. This helps to guide and manage tourists during peak hours and avoid excessive concentration of people.

[0029] Network environment data is used to monitor the status of wireless communication networks in scenic areas to ensure stable connection and data transmission of all IoT devices. The main purpose of this type of data collection is to ensure the reliability and real-time performance of wireless communication and avoid affecting the quality of data transmission due to network instability. The main network environment data includes: Network signal strength and coverage: Through the network quality monitoring equipment deployed in the scenic area, the signal strength, coverage and stability of Wi-Fi, LoRa, NB-IoT and other networks in various areas of the scenic area are monitored. This helps to determine which areas have signal blind spots or network bottlenecks, and timely adjust network facilities and optimize network layout.

[0030] Bandwidth usage and network load: Monitor the bandwidth usage of wireless networks in scenic areas and analyze network load and transmission speed. By monitoring bandwidth usage in real time, network congestion can be detected in time to avoid data transmission delays or packet loss.

[0031] Network delay and packet loss rate: The transmission delay and packet loss rate of data packets are crucial for the real-time management of scenic spots. By monitoring network delay and packet loss rate, the system can take appropriate measures, such as dynamically adjusting the data transmission rate or selecting the optimal route, to ensure the timeliness and accuracy of data.

[0032] Facility status data is used to monitor the operating status of various infrastructures in the scenic area in real time, ensure the normal operation of the facilities, and promptly detect and repair problems. This type of data is mainly collected through various sensors, including: Facility operation status sensors: Sensors are used to monitor the usage status of facilities in scenic areas (such as elevators, vending machines, public toilets, lights, air conditioners, etc.). For example, the load sensor of an elevator monitors the number of passengers in the elevator, the inventory sensor of a vending machine checks the remaining amount of goods, and the liquid level sensor of a toilet determines whether cleaning or replenishment is needed.

[0033] Facility failure and health monitoring: By monitoring the status of facilities in real time, the equipment can detect equipment failures or abnormal operation. For example, sensors monitor water pipe pressure, and the equipment can automatically warn when the pressure is too high or too low to avoid failure.

[0034] Energy consumption monitoring: By installing energy consumption monitoring equipment, the consumption of electricity, gas, water and other resources of various facilities in the scenic area can be collected in real time. This helps scenic area managers to timely understand the energy efficiency performance of various facilities, optimize energy management and reduce operating costs.

[0035] These data are transmitted to the central data processing platform through wireless communication networks (such as Wi-Fi, LoRa, NB-IoT, etc.). The transmission process needs to ensure the efficiency and accuracy of the data, usually using the following methods: Wireless transmission: The wireless network coverage arranged in the scenic area ensures that all kinds of sensor data can be uploaded to the cloud platform for processing and analysis in real time. Data encryption and protection: To ensure the security of data, encryption technology, such as SSL / TLS protocol, is usually used in the transmission process to ensure the confidentiality and integrity of data during transmission. Real-time data synchronization: Use efficient transmission protocols (such as MQTT, CoAP, etc.) to ensure the synchronization of real-time data and avoid data delay or loss due to network problems.

[0036] In S3, the bandwidth usage of the wireless network in the scenic area is monitored, the network load and transmission speed are analyzed, and the bandwidth usage drift value is generated by real-time monitoring and analysis of the bandwidth usage. The method for obtaining the bandwidth usage drift value is: Bandwidth usage It refers to the ratio of the bandwidth actually used by the network to the total available bandwidth, usually expressed as a percentage: ; In the formula, Indicates the bandwidth used in the network at time point t. Indicates the total network bandwidth (usually a fixed value in Mbps or Gbps).

[0037] The bandwidth usage drift value reflects the changing trend of bandwidth usage. By calculating the change of bandwidth usage over time, the fluctuation degree of network load can be determined. The specific steps are as follows: Collect network bandwidth usage in real time at each time interval (such as every minute or every second) , and calculate the bandwidth usage at the current moment .

[0038] Set a time window Δt (for example, 5 minutes or 1 hour) and calculate the change in bandwidth usage within the time window. For example, calculate the difference between the bandwidth usage at each time point and the previous time point: ; Indicates the change in bandwidth usage at time point t, It represents the bandwidth usage at the previous time point (t−Δt).

[0039] The bandwidth usage drift value EQS is an indicator that measures the bandwidth usage change rate. Its calculation formula is: ; Where Δt is the time interval for calculating bandwidth changes, and n is the length of the statistical window, which can be the average value of multiple time points (such as the sum of the differences of the past five time points).

[0040] The calculation result of bandwidth usage drift value EQS can be used in multiple optimization scenarios: Network load analysis: When the drift value is large, it means that the network load has changed dramatically and it may be necessary to optimize bandwidth allocation or perform traffic regulation.

[0041] Network congestion warning: If the drift value continues to increase, the network may be about to be congested, which can be dealt with through traffic control or intelligent scheduling.

[0042] Traffic optimization and adjustment: By real-time monitoring of drift value changes, the transmission rate of data streams can be dynamically adjusted to prevent overloaded areas from affecting other network devices.

[0043] The use status of facilities in the scenic area (such as elevators, vending machines, public toilets, lights, air conditioners, etc.) is monitored by sensors, and the load conditions of facilities with abnormal use status are analyzed to generate abnormal load values ​​of facilities. The method for obtaining abnormal load values ​​of facilities is as follows: The facility load is usually expressed as the current usage or working intensity of the facility. The deviation from the rated capacity or standard load value of the facility reflects the abnormal state of the facility. The definition of load indicators includes: Real-time load value Represents the actual usage load of the facility at time h (such as the current load of the elevator, the transaction volume of the vending machine, the usage rate of the toilet, the light power, etc.).

[0044] Rated load value Indicates the rated load of the facility under normal operating conditions, usually provided by the equipment manufacturer or determined through historical data.

[0045] Load utilization Indicates the ratio of the facility's real-time load value to the rated load value, used to measure the current load status: ; When checking whether the load of a facility is abnormal, the deviation of the load can be calculated by comparing the real-time load value with the historical load trend. Based on the historical operation data of the facility, the load mean and standard deviation within a time window are calculated to establish the normal range of the load: ; In the formula, is the historical average of the facility load, is the historical standard deviation of the facility load, and m is the number of historical data samples; the real-time load deviation value is calculated by the deviation between the real-time load value and the historical mean. : ; Set a threshold λ (usually 2 or 3, adjusted according to demand). When the load deviation value exceeds the standard deviation of the threshold multiple, the load state of the facility is considered abnormal: if , then it is determined that the facility load is abnormal.

[0046] In order to quantify the degree of abnormality, the facility load abnormal value WAS is calculated. The specific formula is: ; High facility load anomaly value WAS indicates that the facility load anomaly is serious and requires priority investigation and maintenance.

[0047] The bandwidth utilization drift value and the facility load anomaly value are normalized, and the real-time coefficient of data transmission between smart devices in the scenic area is calculated based on the normalized bandwidth utilization drift value and the facility load anomaly value.

[0048] For example, the present invention can use the following formula to calculate the real-time coefficient of data transmission between smart devices in the scenic area, and the calculation expression is: ; In the formula, is the real-time coefficient of data transmission between smart devices in the scenic area, is the bandwidth usage drift value, is the abnormal value of facility load, is the proportional coefficient of bandwidth usage drift value and facility load abnormal value, and Both are greater than 0.

[0049] The obtained real-time coefficient of data transmission between smart devices in the scenic area is compared with the real-time reference threshold set according to historical data. If the real-time coefficient of data transmission between smart devices in the scenic area is greater than or equal to the set real-time reference threshold, it means that the real-time of data transmission between smart devices in the scenic area is high, and no warning signal is generated at this time; if the real-time coefficient of data transmission between smart devices in the scenic area is less than the set real-time reference threshold, it means that the real-time of data transmission between smart devices in the scenic area is low, and a warning signal is generated at this time, and the data transmission rate between smart devices in the scenic area needs to be adjusted.

[0050] In S4, the data transfer rate It is an important indicator for data transmission between smart devices. Too high a transmission rate will consume too much bandwidth and power, so it needs to be adjusted according to real-time requirements. The dynamic adjustment formula for data transmission rate is: ;in: is the dynamic data transmission rate at time t, is the basic transmission rate, the maximum supported transmission rate of the device, is the abnormal value of facility load, is the maximum value of the facility load anomaly, used for standardization, is the bandwidth usage drift value, is the maximum value of bandwidth usage drift, used for standardization. α and β are dynamic adjustment coefficients, set according to the priority and work requirements of the equipment. For example, visitor behavior monitoring equipment may take precedence over security monitoring equipment, and the α and β values ​​can be adjusted according to the importance of different equipment.

[0051] Under the premise of ensuring the normal operation of the equipment, reducing the power consumption of the equipment is crucial for the long-term operation of smart devices in the scenic area. To achieve this goal, low-power communication protocols (such as LoRa, Zigbee, etc.) can be used to reduce the power consumption of data transmission between devices.

[0052] Dynamically select the appropriate communication protocol based on the real-time load of the device and the network bandwidth usage. For example, the following strategy is adopted: Low traffic and low bandwidth requirements: For devices that transmit less data and do not have high real-time requirements (such as some security equipment or non-critical sensors), low-power protocols (for example, Zigbee, LoRa) are used to reduce energy consumption.

[0053] High traffic and high real-time requirements: For devices that require large amounts of data transmission or have high real-time requirements (such as visitor behavior monitoring equipment and key security equipment), high-speed but more power-consuming protocols (such as Wi-Fi, 4G / 5G) are used to ensure timely data transmission.

[0054] The communication protocol selection formula is: ;in: The communication protocol power consumption of the device at time t is: The power consumption adjustment factor is set according to the real-time demand and importance of the device. When it is larger, a high-power protocol is selected, otherwise a low-power protocol is selected.

[0055] Combined with the energy management system (EMS) in the scenic area, the working status of visitor behavior monitoring equipment and security monitoring equipment is dynamically scheduled to achieve optimal energy distribution and balance of equipment load.

[0056] The working state of a device (e.g., on / off, standby, etc.) is closely related to its task priority, real-time requirements, power consumption, etc. The specific adjustment formula for dynamically scheduling the working state of a device is: ; In the formula, It is the working status of the device at time t (0 means off, 1 means on, and between 0 and 1 means standby or low power consumption). and is the scheduling coefficient, which represents the priority of the device. For high-priority devices (such as visitor behavior monitoring devices, security monitoring devices, etc.), the coefficient is large, and the device remains turned on for a longer time; low-priority devices (such as auxiliary facilities) can enter a low-power standby state.

[0057] Devices with high load and low real-time requirements: Reduce data transmission rate, enable low-power communication protocols, and put devices into low-power standby or shutdown state.

[0058] Devices with low load and high real-time requirements: Increase data transmission rate, use high-power protocols, ensure that the device is always on and guarantee real-time data transmission.

[0059] According to the calculated dynamic adjustment results, the energy management system will dispatch the working status and data transmission strategy of each device in real time in the background to ensure that the smart devices in the scenic area can reduce energy consumption while maintaining the real-time data transmission. The dispatching strategy includes: switching security monitoring equipment and visitor behavior monitoring equipment to low power consumption mode during low traffic periods. Prioritize the stable operation of equipment for emergency tasks (such as monitoring equipment and public safety equipment). Other non-critical equipment enters standby or shutdown state to reduce unnecessary energy consumption.

[0060] In this application, by dynamically adjusting the data transmission rate between smart devices and combining low-power communication protocols with dynamic scheduling of energy management systems, scenic spots can reduce energy consumption and improve the real-time response capabilities of the system while ensuring efficient operation of equipment. By calculating facility load, bandwidth utilization, and device priority in real time, the intelligent scheduling system can maintain optimal resource allocation and energy management in a changing scenic environment.

[0061] In S5, after adjusting the working status of the tourist behavior monitoring equipment and security monitoring equipment, the scenic spot's intelligent system resumes collecting tourist flow data. The collected tourist flow data includes but is not limited to: Real-time location and density of tourists: Collect the real-time location, path and density information of tourists through sensors installed at various key locations in the scenic area (such as cameras, infrared sensors, Wi-Fi tracking devices, etc.). Flow change trend: Analyze the flow patterns of tourists in different time periods (such as peak hours, trough hours, etc.), as well as changes in tourist density in specific areas. Tourist retention: Monitor the length of stay of tourists at specific attractions or areas to identify potential congestion areas.

[0062] The traffic data is modeled and predicted through cluster analysis to identify the fluctuation patterns of tourist traffic and predict future traffic fluctuation trends, including: Before cluster analysis, the collected tourist flow data needs to be preprocessed to ensure that it is suitable for machine learning models. Common preprocessing steps include: Data cleaning: remove invalid data and outliers, and fill in missing values.

[0063] Standardization / normalization: Convert the data into a standardized form to prevent certain features from having too much influence on the clustering process. Commonly used standardization methods include Z-score standardization, minimum-maximum normalization, etc.

[0064] Feature extraction: Extract meaningful features from tourist flow data, such as the fluctuation range of flow, tourist stay time, tourist flow speed, etc.

[0065] Taking K-means clustering as an example, after the tourist flow data is standardized and the corresponding features are extracted (for example: hourly tourist flow, tourist density, flow fluctuation range, etc.), the clustering algorithm can then be applied to group the tourist flow data.

[0066] K-means clustering process: Select the number of clusters (K): Select the number of clusters by algorithm or experience. The best K value can be determined by methods such as silhouette coefficient and elbow rule.

[0067] Initialize cluster centers: randomly select K initial centers (centroids).

[0068] Assign data points: Assign each tourist flow data point to the cluster center that is closest to it based on a distance metric (e.g., Euclidean distance).

[0069] Update cluster centers: Calculate the mean value of each cluster and update the cluster center.

[0070] Repeat until the cluster center no longer changes, or the preset number of iterations is reached.

[0071] After clustering is completed, the scenic spot can analyze different tourist flow fluctuation patterns based on the flow pattern characteristics of each cluster. For example: Peak mode (Cluster 1): represents the peak period of tourist traffic, which usually occurs during holidays or specific time periods (such as 3pm to 5pm).

[0072] Trough mode (Cluster 2): represents the period of relatively low tourist flow, usually occurring in the morning or at night.

[0073] Stable mode (Cluster 3): The tourist flow fluctuates slightly and is within the normal fluctuation range.

[0074] Abnormal mode (Cluster 4): There are sudden changes in tourist flow, such as flow fluctuations caused by special events or weather changes.

[0075] Based on the results of cluster analysis, the scenic spot can predict the future fluctuation trend of tourist flow through the following steps: Each traffic pattern after clustering can be regarded as a specific "traffic pattern". Use time series analysis or regression models to model the time evolution trend of each pattern. For example, use time series models such as ARIMA (autoregressive integrated moving average model) or LSTM (long short-term memory network) to analyze the historical changes in tourist flow patterns and predict future traffic fluctuations. For example, when using the ARIMA model, the traffic data in each cluster can be modeled: ; Where: y(s) represents the tourist flow at time s, p and q are the coefficients of the model, and ϵ(s) is the error term, which represents the random fluctuation of the model.

[0076] Based on historical data and forecasting models, it is possible to predict visitor traffic for several time periods in the future. For example, predict visitor traffic trends for the next hour, day, or during a specific event.

[0077] Through cluster analysis and traffic forecasting, scenic spots can make the following optimization decisions: Personnel dispatch and flow guidance: According to different flow pattern predictions, the layout of security, service personnel and tourist guidance facilities is dynamically adjusted. For example, if the peak period is predicted to arrive, the direction of tourist flow is adjusted in advance, and guidance signs and service windows are added.

[0078] Facility resource optimization: According to the changes in tourist flow, intelligent scheduling of facility resources within the scenic area, such as the activation and shutdown of elevators, air conditioners, toilets and other equipment, to avoid high-load operation.

[0079] Traffic and security warning: When it is predicted that the tourist flow in a certain area or time period will exceed the safe capacity, the traffic dispatch and security warning system will be activated to avoid congestion or accidents.

[0080] In order to ensure the effectiveness and accuracy of decision-making, the system needs to continuously update and feedback real-time data. Whenever new tourist flow data is collected, the cluster analysis and flow prediction model will be recalculated, and the fluctuation trend of tourist flow will be corrected and the optimization strategy will be adjusted. This real-time feedback mechanism will help scenic spots cope with dynamically changing flow demands and maintain efficient personnel guidance and resource allocation.

[0081] In this embodiment, firstly, a variety of IoT sensors and smart devices are arranged in the scenic area, including network environment monitoring, tourist behavior monitoring, security monitoring and resource management equipment, for real-time collection of multi-source data. Through the wireless communication network, the collected data such as tourist flow, network environment and facility status are transmitted to the central data processing platform, and the real-time performance of data transmission is predicted and modeled using big data analysis algorithms. Based on the prediction results, the data transmission rate between smart devices is adjusted in real time, including the communication protocol to reduce the power consumption of the equipment, and the working status of the tourist behavior monitoring equipment and security monitoring equipment is dynamically scheduled in combination with the energy management system. The adjusted equipment re-collects tourist flow data, applies machine learning algorithms to analyze tourist flow fluctuations, and optimizes personnel guidance and resource scheduling decisions based on the analysis results to achieve efficient scenic area management and services.

[0082] Example 2, please refer to Figure 2 As shown, the scenic spot intelligent management and control system based on the Internet of Things and big data analysis described in this embodiment includes a data acquisition module, a data transmission module, a modeling module, an adjustment module and a machine learning analysis module; Data collection module: a variety of IoT sensors and smart devices are deployed in the scenic area, including network environment monitoring equipment, tourist behavior monitoring equipment, security monitoring equipment and resource management equipment, which are used to collect multi-source data in the scenic area in real time; Data transmission module: transmits multi-source data collected by various sensors to the central data processing platform through a wireless communication network, wherein the multi-source data includes tourist flow data, network environment data and facility status data; Modeling module: conducts real-time analysis on the collected network environment data and facility status data, and predicts and models the real-time performance of data transmission between smart devices in the scenic area through big data analysis algorithms; Adjustment module: Based on the prediction results, the data transmission rate between smart devices in the scenic area is adjusted in real time, including the communication protocol for reducing power consumption of smart devices in the scenic area, and the dynamic scheduling of the working status of tourist behavior monitoring equipment and security monitoring equipment in the scenic area in combination with the energy management system; Machine learning analysis module: re-collect tourist flow data through adjusted tourist behavior monitoring equipment and security monitoring equipment, analyze tourist flow fluctuations through machine learning algorithms, and make optimized decisions on personnel guidance and resource scheduling based on the analysis results.

[0083] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0084] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0085] 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.

[0086] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the 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.

Claims

1. A smart scenic area management and control method based on the Internet of Things and big data analysis, characterized by: The following steps are involved: S1: deploy a variety of IoT sensors and smart devices in the scenic area, including network environment monitoring equipment, tourist behavior monitoring equipment, security monitoring equipment and resource management equipment, for real-time collection of multi-source data in the scenic area; S2: Transmitting multi-source data collected by various sensors to a central data processing platform via a wireless communication network, wherein the multi-source data includes tourist flow data, network environment data, and facility status data; S3: Analyze the collected network environment data and facility status data in real time, and predict and model the real-time performance of data transmission between smart devices in the scenic area through big data analysis algorithms; S4: Based on the prediction results, the data transmission rate between smart devices in the scenic area is adjusted in real time, including the communication protocol for reducing power consumption of smart devices in the scenic area, and the dynamic scheduling of the working status of tourist behavior monitoring equipment and security monitoring equipment in the scenic area in combination with the energy management system; S5: Re-collect tourist flow data through the adjusted tourist behavior monitoring equipment and security monitoring equipment, analyze tourist flow fluctuations through machine learning algorithms, and make optimal decisions on personnel guidance and resource scheduling based on the analysis results.

2. The method for intelligent management and control of scenic spots based on the Internet of Things and big data analysis according to claim 1 is characterized in that: In S3, the bandwidth usage rate drift value is generated by real-time monitoring and analysis of the bandwidth usage rate, wherein the bandwidth usage rate drift value is obtained by: Bandwidth usage It refers to the ratio between the bandwidth actually used by the network and the total available bandwidth: ; In the formula, represents the bandwidth used in the network at time point t, Indicates the total network bandwidth; Collect network bandwidth usage in real time at each time interval , and calculate the bandwidth usage at the current moment ; Set a time window Δt, calculate the change in bandwidth usage within the time window, and calculate the difference between the bandwidth usage at each time point and the previous time point: ; Indicates the change in bandwidth usage at time point t, Indicates the bandwidth usage at the last point in time; The bandwidth usage drift value EQS is an indicator that measures the bandwidth usage change rate. Its calculation formula is: ; Where Δt is the time interval for calculating bandwidth changes, and n is the length of the statistical window.

3. The method for intelligent management and control of scenic spots based on the Internet of Things and big data analysis according to claim 2 is characterized in that: After analyzing the load conditions of the facilities with abnormal usage status, the abnormal load value of the facility is generated. The method for obtaining the abnormal load value of the facility is as follows: Set real-time load value Indicates the actual load of the facility at time h, the rated load value Indicates the rated load of the facility under normal operation, load utilization rate Indicates the ratio of the facility's real-time load value to the rated load value, used to measure the current load status: ; By comparing the real-time load value with the historical load trend, the degree of load deviation is calculated. Based on the historical operation data of the facility, the load mean and standard deviation within a time window are calculated: ; In the formula, is the historical average of the facility load, is the historical standard deviation of the facility load, and m is the number of historical data samples; the real-time load deviation value is calculated by the deviation between the real-time load value and the historical mean. : ; A threshold λ is set. When the load deviation value exceeds the standard deviation of the threshold multiple, the load state of the facility is considered abnormal: , then the facility load is determined to be abnormal; the facility load abnormality value WAS is calculated, and the specific formula is: .

4. The method for intelligent management and control of scenic spots based on the Internet of Things and big data analysis according to claim 3 is characterized in that: The bandwidth utilization drift value and the facility load anomaly value are normalized, and the real-time coefficient of data transmission between smart devices in the scenic area is calculated based on the normalized bandwidth utilization drift value and the facility load anomaly value.

5. The method for intelligent management and control of scenic spots based on the Internet of Things and big data analysis according to claim 4 is characterized in that: The obtained real-time coefficient of data transmission between smart devices in the scenic area is compared with the real-time reference threshold set according to historical data. If the real-time coefficient of data transmission between smart devices in the scenic area is greater than or equal to the set real-time reference threshold, it means that the real-time of data transmission between smart devices in the scenic area is high, and no warning signal is generated at this time; if the real-time coefficient of data transmission between smart devices in the scenic area is less than the set real-time reference threshold, it means that the real-time of data transmission between smart devices in the scenic area is low, and a warning signal is generated at this time, and the data transmission rate between smart devices in the scenic area needs to be adjusted.

6. The method for intelligent management and control of scenic spots based on the Internet of Things and big data analysis according to claim 1 is characterized in that: In S4, the data transfer rate It is an important indicator of data transmission between smart devices and needs to be adjusted according to real-time requirements. The dynamic adjustment formula of data transmission rate is: ;in: is the dynamic data transmission rate at time t, is the basic transmission rate, the maximum supported transmission rate of the device, is the facility load anomaly, is the maximum value of the abnormal value of the facility load, is the bandwidth usage drift value, is the maximum value of the bandwidth usage drift value, α and β are dynamic adjustment coefficients, which are set according to the priority and working requirements of the device; The communication protocol selection formula is: ;in: The communication protocol power consumption of the device at time t is: is the power consumption adjustment coefficient; the working state of the dynamic scheduling equipment, the specific adjustment formula is: ; In the formula, is the working status of the equipment at time t, and is the scheduling coefficient; Devices with high load and low real-time requirements: reduce the data transmission rate, enable low-power communication protocols, and put the device into low-power standby or shutdown state; Devices with low load and high real-time requirements: increase the data transmission rate, use high-power protocols, ensure that the device is always on and guarantee real-time data transmission.

7. The method for intelligent management and control of scenic spots based on the Internet of Things and big data analysis according to claim 6 is characterized by: In S5, cluster analysis is used to model and predict traffic data to identify the fluctuation patterns of tourist traffic and predict future traffic fluctuation trends, including: Before cluster analysis, the collected tourist flow data needs to be preprocessed; Select the number of clusters, randomly select K initial centers, and assign each tourist flow data point to the nearest cluster center based on the distance metric; calculate the average value of each cluster and update the cluster center; repeat until the cluster center no longer changes. After clustering is completed, the scenic spot analyzes different tourist flow fluctuation patterns based on the flow pattern characteristics of each cluster.

8. The method for intelligent management and control of scenic spots based on the Internet of Things and big data analysis according to claim 7 is characterized by: Tourist flow fluctuation modes include: peak mode, trough mode, stable mode and abnormal mode.

9. A scenic spot intelligent management and control system based on the Internet of Things and big data analysis, used to implement the scenic spot intelligent management and control method based on the Internet of Things and big data analysis as described in any one of claims 1 to 8, characterized in that: It includes data acquisition module, data transmission module, modeling module, adjustment module and machine learning analysis module; Data collection module: a variety of IoT sensors and smart devices are deployed in the scenic area, including network environment monitoring equipment, tourist behavior monitoring equipment, security monitoring equipment and resource management equipment, which are used to collect multi-source data in the scenic area in real time; Data transmission module: transmits multi-source data collected by various sensors to the central data processing platform through a wireless communication network, wherein the multi-source data includes tourist flow data, network environment data and facility status data; Modeling module: conducts real-time analysis on the collected network environment data and facility status data, and predicts and models the real-time performance of data transmission between smart devices in the scenic area through big data analysis algorithms; Adjustment module: Based on the prediction results, the data transmission rate between smart devices in the scenic area is adjusted in real time, including the communication protocol for reducing power consumption of smart devices in the scenic area, and the dynamic scheduling of the working status of tourist behavior monitoring equipment and security monitoring equipment in the scenic area in combination with the energy management system; Machine learning analysis module: re-collect tourist flow data through adjusted tourist behavior monitoring equipment and security monitoring equipment, analyze tourist flow fluctuations through machine learning algorithms, and make optimized decisions on personnel guidance and resource scheduling based on the analysis results.

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