An information-based comprehensive management system and method for scenic spot construction

Abstract

This invention relates to the field of scenic area management technology, specifically to an information-based integrated management system and method for scenic area construction. The method includes: collecting real-time multi-dimensional data on tourists, facilities, environment, and personnel; extracting multi-dimensional features; and constructing a comprehensive database of the entire scenic area. Based on the comprehensive database, real-time visitor flow density is calculated, visitor flow changes are predicted, and tiered early warnings are pushed out. Based on the early warning results, requests for visitor flow, facility, environment, and personnel control are triggered respectively, and feedback data is obtained to form a closed-loop management system. The system includes: a multi-dimensional data acquisition module, a visitor flow dynamic early warning module, and a current safety control module. Through the above methods, efficient and comprehensive information-based integrated management is achieved to improve the management level of the scenic area and ensure its safety.

Description

Technical Field

[0001] This invention relates to the field of scenic area management technology, and in particular to an information-based integrated management system and method for scenic area construction. Background Technology

[0002] With the rapid development of the tourism industry, the scale of scenic spots is constantly expanding and the number of tourists is continuously increasing. Traditional scenic spot management methods are no longer able to meet the current needs of scenic spot operation.

[0003] Traditional management models rely on a single data collection dimension, depending on manual statistics or single-device monitoring. They cannot integrate multi-source data such as tourists, facilities, and the environment, resulting in a lack of comprehensive data support for management decisions. At the same time, visitor flow management is mostly "post-event response," which can only passively guide traffic after congestion occurs and cannot predict changes in visitor flow based on real-time data, which can easily lead to problems such as tourist congestion and a decline in the experience.

[0004] Therefore, an efficient and comprehensive information-based integrated management method is proposed to improve the management level of scenic areas and ensure their safety. Summary of the Invention

[0005] The purpose of this invention is to provide an information-based integrated management system and method for scenic area construction, aiming to propose an efficient and comprehensive information-based integrated management method to improve the management level of scenic areas and ensure their safety.

[0006] To achieve the above objectives, the present invention employs an information-based integrated management method for scenic area construction, comprising the following steps: Collect real-time multi-dimensional data on tourists, facilities, environment, and personnel, extract multi-dimensional features, and construct a comprehensive database of the scenic area; Real-time visitor density is calculated based on the scenic area's comprehensive database, visitor flow changes are predicted, and tiered early warnings are pushed out. Based on the early warning results, requests for passenger flow, facility, environment, and personnel control are triggered respectively, and feedback data is obtained to form a closed-loop management system.

[0007] Among the steps involved are: collecting real-time multi-dimensional data on tourists, facilities, environment, and personnel; extracting multi-dimensional features; and constructing a comprehensive database of the scenic area. Deploy data collection equipment to collect real-time, multi-dimensional data on tourists, facilities, environment, and personnel; The raw data collected from each dimension is filtered to remove duplicate and invalid data, and missing data is filled in to complete the data cleaning. Extract features from the cleaned data.

[0008] In the step of extracting features from the cleaned data: Extracting identity identifiers, travel routes, and consumption preference characteristics from the tourist perspective; Extract equipment number, operating parameter thresholds, and fault type features from the facility dimension; Environmental dimensions include extracting regional identifiers, normal ranges of indicators, and early warning triggering conditions. From the personnel dimension, features such as job type, on-duty time, and abnormal behavior category are extracted.

[0009] After the step of extracting features from the cleaned data: After extracting features, the data from each dimension are categorized and stored in the scenic area's cloud database. Data relationships are established to form a comprehensive database covering tourists, facilities, environment, and personnel.

[0010] Among the steps, the following steps are involved: calculating real-time visitor density based on the scenic area's comprehensive database, predicting changes in visitor flow, and issuing tiered early warnings: Real-time tourist location data is retrieved from the scenic area's overall database to calculate real-time visitor density. Based on the functional attributes of different areas of the scenic area, thresholds for visitor flow density are set, and normal, warning, and congestion density ranges are defined. The current density status of each grid is marked with different colors on the cloud platform map. By retrieving historical data from the entire scenic area's database and combining it with current visitor numbers, real-time density in various areas, and weather data, the trend of visitor density changes in each grid unit is predicted. Early warning information is generated based on the prediction results.

[0011] In the step of retrieving real-time tourist location data from the scenic area's overall database and calculating real-time visitor density, the real-time visitor density calculation process is as follows: The scenic area is divided into fixed-size grid units using a grid division method. The number of tourists in each grid is counted, and the real-time visitor density of each grid is calculated based on the grid area.

[0012] In the step of generating early warning information based on the prediction results: If the predicted area reaches the warning threshold, a warning will be generated at the first warning time. If the predicted area reaches the congestion threshold, an early warning will be generated at the second warning time.

[0013] The process involves triggering requests for passenger flow, facility, environmental, and personnel control based on the early warning results, obtaining feedback data, and forming a closed-loop management system. The system triggers control requests based on early warning results and updates the handling progress in real time. The system retrieves real-time visitor density, facility operation status, environmental indicators, and personnel behavior data of the controlled area from the scenic area's overall database, compares the data changes before and after the control measures, and evaluates the effectiveness of the control measures.

[0014] The process includes retrieving real-time visitor density, facility operation status, environmental indicators, and personnel behavior data from the scenic area's overall database, comparing the data changes before and after control measures, and evaluating the effectiveness of the control measures. If the data shows that the indicators have returned to the normal range after the control measures are implemented, the warning will be lifted; if the indicators do not improve, the warning level will be upgraded and the control measures will be adjusted. The data of the control process will be stored in the scenic area's database to form a closed-loop management system.

[0015] This invention also provides an information-based integrated management system for scenic area construction, including a multi-dimensional data collection module, a visitor flow dynamic early warning module, and a current safety control module; wherein: The multi-dimensional data acquisition module is used to collect real-time multi-dimensional data on tourists, facilities, environment, and personnel, extract multi-dimensional features, and construct a comprehensive database of the scenic area. The dynamic passenger flow early warning module is used to calculate real-time passenger flow density based on the scenic area's database, predict passenger flow changes, and push out tiered early warnings. The current safety management module is used to trigger passenger flow, facility, environment, and personnel management requests based on the early warning results, and obtain feedback data to form a closed-loop management system.

[0016] This invention discloses an information-based integrated management system and method for scenic area construction. The system comprises a multi-dimensional data acquisition module, a dynamic visitor flow early warning module, and a current safety control module, which perform the following steps: collecting real-time multi-dimensional data on tourists, facilities, environment, and personnel; extracting multi-dimensional features; and constructing a comprehensive scenic area database. Based on the comprehensive scenic area database, real-time visitor flow density is calculated, visitor flow changes are predicted, and tiered early warnings are pushed out. Based on the early warning results, visitor flow, facility, environment, and personnel control requests are triggered respectively, and feedback data is obtained to form a closed-loop management system. Through the above methods, efficient and comprehensive information-based integrated management is achieved to improve the management level of the scenic area and ensure its safety. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating the steps of the information-based integrated management method for scenic area construction according to the present invention.

[0019] Figure 2 This is a flowchart of steps S100 of the present invention.

[0020] Figure 3 This is a flowchart of steps S200 of the present invention.

[0021] Figure 4 This is a flowchart of steps S300 of the present invention.

[0022] Figure 5 This is a structural diagram of the information-based integrated management system for scenic area construction according to the present invention.

[0023] Figure 6 This is a schematic diagram of the electronic device of the present invention.

[0024] 401 - Multi-dimensional data acquisition module, 402 - Passenger flow dynamic early warning module, 403 - Current security control module. Detailed Implementation

[0025] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application.

[0026] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0027] It should be understood that although the terms first, second, third, etc., may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0028] Please see Figures 1-4 This invention provides an information-based integrated management method for scenic area construction, comprising the following steps: S100: Collects real-time multi-dimensional data on tourists, facilities, environment, and personnel, extracts multi-dimensional features, and constructs a comprehensive database of the scenic area.

[0029] In this implementation, real-time multi-dimensional data on tourists, facilities, environment, and personnel are collected, multi-dimensional features are extracted, and a comprehensive database of the scenic area is constructed. The specific process is as follows: S101: Deploy data collection equipment to collect real-time multi-dimensional data on tourists, facilities, environment, and personnel; S102: Filter the raw data collected from each dimension, remove duplicate and invalid data, and fill in missing data to complete the data cleaning; S103: Extract features from the cleaned data; among them, extract features such as identity identification, tour trajectory and consumption preference from the tourist dimension; extract features such as equipment number, operating parameter threshold and fault type from the facility dimension; extract features such as area identification, normal range of indicators and early warning triggering conditions from the environmental dimension; and extract features such as job type, on-duty time and abnormal behavior category from the personnel dimension. S104: After extracting features, the data of each dimension are classified and stored in the scenic area cloud database, and data relationships are established to form a comprehensive database covering tourists, facilities, environment and personnel of the scenic area.

[0030] In the above process, data collection equipment is deployed. For tourists, ticketing systems, facial recognition gates, and ID card verification devices are installed at entrances and exits to collect identity and entry data. WiFi probes and Bluetooth beacons combined with mini-program positioning are installed at attractions and passages to collect location, dwell time, and trajectory data, and are connected to the POS system of shops to collect consumption data. For facilities, vibration, current, and temperature sensors are installed on amusement facilities, NB-IoT modules are installed on infrastructure, and GPS and speed sensors are provided for transportation facilities to collect operating parameters, status data, and location passenger capacity, respectively. For the environment, monitoring terminals integrating multiple types of sensors are deployed in different functional areas to collect environmental indicators at fixed frequencies. For personnel, smart name tags are provided for staff and connected to an AI video analysis system to collect personnel location, on-duty status, and abnormal behavior data.

[0031] The raw data collected from each dimension was filtered to remove duplicate and invalid data, and missing data was filled in, completing the data cleaning process. For visitor data, duplicate coordinates resulting from multiple location searches by the same visitor and invalid entry records with incorrect ID numbers were removed. For facility data, records with blank operating parameters due to sensor malfunctions and duplicate equipment numbers were deleted. For environmental data, abnormal values ​​exceeding limits due to signal interference were filtered out. For personnel data, invalid location information resulting from lost location signals on employee badges was removed. For missing data, such as missing visitor location data for a specific time period, linear interpolation was used to fill in the gaps using valid coordinates within 10 minutes before and after the location. For missing facility temperature data for a specific time period, the average temperature of similar facilities during the same period was used to fill in the gaps.

[0032] Features were extracted from the cleaned data. For tourists, features included identity identifiers (ID numbers as unique identifiers), tour routes (coordinate sequences arranged chronologically), and consumption preferences (high-frequency consumption categories and average spending range). For facilities, features included equipment serial numbers (unique codes containing facility type and installation location information), operating parameter thresholds (e.g., the normal operating current range for roller coasters is 12-15A), and fault types (e.g., motor overheating, circuit fault). For the environment, features included area identifiers (e.g., "densely populated tourist area at the main peak viewing platform," "ecologically sensitive area in the eastern foothills forest area") and normal ranges for indicators (e.g., PM2.5 ≤ 75 μg / m³). 3 Noise level ≤ 60 dB, and warning trigger conditions (e.g., PM2.5 exceeding 100 μg / m³). 3 Triggering early warning features, personnel-level features include job type (security, cleaning, maintenance), on-duty time (e.g., security early shift 8:00~16:00), and abnormal behavior category (climbing over fences, loitering for a long time, physical conflict).

[0033] After feature extraction, the data from each dimension is categorized and stored in the scenic area's cloud database. Data relationships are established to form a comprehensive database covering tourists, facilities, the environment, and personnel. A distributed storage architecture is adopted, storing tourist data according to "identity identifier - visit data - consumption data," facility data according to "equipment number - operation data - maintenance records," environmental data according to "region identifier - time series - indicator data," and personnel data according to "job type - on-duty data - behavioral data." Simultaneously, relationships are established, such as linking tourist identity identifiers with corresponding visit trajectories and consumption records, equipment numbers with operating parameters and fault types, and region identifiers with environmental indicators and early warning trigger conditions. This ensures cross-dimensional data integration during retrieval, ultimately forming a complete comprehensive database for the entire scenic area.

[0034] S200: Calculates real-time visitor density based on the scenic area's database, predicts changes in visitor flow, and sends out tiered early warnings.

[0035] In this implementation, real-time visitor density is calculated based on the scenic area's comprehensive database, visitor flow changes are predicted, and tiered early warnings are issued. The specific process is as follows: S201: Retrieve real-time location data of tourists from the scenic area's database, divide the scenic area into fixed-size grid units using a grid division method, count the number of tourists in each grid, and calculate the real-time visitor density of each grid based on the grid area. S202: Set visitor density thresholds based on the functional attributes of different areas of the scenic area, delineate normal, warning, and congestion density ranges, and mark the current density status of each grid on the cloud platform map with different colors; S203: Retrieve historical data from the scenic area's overall database, and combine it with current visitor numbers, real-time density in each area, and weather data to predict the trend of visitor density changes in each grid unit; S204: Generate early warning information based on the prediction results; if the predicted area reaches the early warning threshold, generate an early warning at the first early warning time; if the predicted area reaches the congestion threshold, generate an early warning at the second early warning time.

[0036] In the above process, real-time location data of tourists is retrieved from the scenic area's overall database. The scenic area is divided into fixed-size grid units using a grid partitioning method. The number of tourists in each grid is counted, and the real-time visitor density of each grid is calculated based on the grid area. The grid unit is set to 10 meters × 10 meters. The real-time location coordinates of all tourists are retrieved in batches through the database interface, and the area is assigned to the corresponding grid according to the coordinates. The number of tourists in each grid is counted. Then, the number of tourists in the grid is divided by the grid area (100 square meters) to obtain the number of tourists per square meter, i.e., the real-time visitor density. For example, if there are 200 tourists in a certain grid, the visitor density is calculated to be 2 people / square meter.

[0037] Visitor flow density thresholds are set according to the functional attributes of different areas within the scenic area, defining normal, warning, and congestion density ranges. The current density status of each grid is marked on the cloud platform map using different colors. For core attractions (such as the main peak viewing platform and landmark buildings), the normal density is set to ≤2 people / square meter, the warning density to 2-3 people / square meter, and the congestion density to >3 people / square meter. For ordinary passageways, the normal density is set to ≤1 person / square meter, the warning density to 1-1.5 people / square meter, and the congestion density to >1.5 people / square meter. For rest areas, the normal density is set to ≤0.8 people / square meter, the warning density to 0.8-1.2 people / square meter, and the congestion density to >1.2 people / square meter. On the electronic map of the scenic area's cloud management platform, normal density grids are marked in green, warning density grids in yellow, and congestion density grids in red, and the display is updated in real time.

[0038] Historical data from the entire scenic area's database is retrieved, combined with current visitor numbers, real-time density in each area, and weather data to predict the visitor density trend for each grid unit. Historical data includes visitor flow data from the same period over the past three months (e.g., Saturdays and public holidays), including visitor numbers at different times and density variation patterns for each grid. Current data includes the cumulative number of visitors for the day and real-time density for each grid. Weather data is obtained from a third-party meteorological platform, providing real-time weather (sunny, rainy, cloudy), temperature, and wind information. Using the LSTM time series forecasting algorithm, the above data is used as input to construct a prediction model, outputting the visitor density trend for each grid unit within the next hour. For example, it is predicted that the density of a core scenic spot grid will increase from 1.8 people / square meter to 3.1 people / square meter after one hour.

[0039] Early warning information is generated based on the prediction results. If the predicted area reaches the early warning threshold, an early warning is generated at the first early warning time; if the predicted area reaches the congestion threshold, an early warning is generated at the second early warning time. The first early warning time is set to 40 minutes in advance, meaning that when a grid is predicted to reach the early warning density threshold, an early warning is generated 40 minutes in advance; the second early warning time is set to 60 minutes in advance, meaning that when a grid is predicted to reach the congestion density threshold, an early warning is generated 60 minutes in advance. The early warning information includes the name of the warning area (e.g., "Northeast Grid of Main Peak Viewing Platform"), the current density, the predicted density, and the suggested diversion direction (e.g., "It is suggested to guide tourists to the secondary peak viewing platform"). It is classified into three levels: mild (yellow, corresponding to the early warning threshold), moderate (orange, between the early warning and congestion thresholds), and severe (red, corresponding to the congestion threshold). Mild warnings are pushed to the mobile terminal of the area administrator, moderate warnings are also pushed to the scenic area's electronic display screen, and severe warnings are simultaneously pushed to the emergency command center.

[0040] S300: Based on the early warning results, it triggers requests for passenger flow, facility, environment, and personnel control respectively, and obtains feedback data to form a closed-loop management system.

[0041] In this implementation, based on the early warning results, requests for passenger flow, facility, environmental, and personnel control are triggered respectively, and feedback data is obtained to form a closed-loop management system. The specific process is as follows: S301: Trigger control requests based on early warning results and update the handling progress in real time; S302: Retrieve real-time visitor density, facility operation status, environmental indicators, and personnel behavior data of the controlled area from the scenic area's overall database, compare the data changes before and after control, and evaluate the effectiveness of the control measures. S303: If the data shows that the indicators have returned to the normal range after the control measures are implemented, the warning will be lifted; if the indicators do not improve, the warning level will be upgraded and the control measures will be adjusted. The data of the control process will be stored in the scenic area's database to form a closed-loop management system.

[0042] During the above process, control requests are triggered based on the warning results, and the handling progress is updated in real time. If it is a severe passenger congestion warning (red), the emergency command center triggers a passenger flow control request, dispatches sightseeing buses to adjust the shuttle frequency in the warning area from 30 minutes / bus to 15 minutes / bus, arranges security personnel to set up temporary diversion points at the entrance of the warning area to guide the flow of people, closes the two upstream passage entrances to the warning area, and pushes alternative tour routes through the tourist mini-program; at the same time, two temporary rest areas and one portable toilet are added around the warning area, and cleaning personnel are dispatched to increase the frequency of garbage collection in the area. If it is a facility abnormality warning (such as the current of amusement facilities exceeding the standard), a facility control request is triggered, the system automatically generates a maintenance work order, including the facility number, abnormal parameters (such as "roller coaster current 18A, exceeding the normal range by 3A"), equipment location and handling suggestions ("suggest stopping the machine to check the motor circuit"), and pushes it to the mobile terminal of the maintenance personnel; after the maintenance personnel accept the order, they mark the progress status in the system as "departed", "under repair", "pending inspection", etc., and upload maintenance photos and inspection data after the repair is completed. If an environmental exceedance warning is issued (e.g., excessive smoke concentration in the forest area), an environmental control request will be triggered. Environmental protection personnel will be dispatched with portable smoke detectors to the site for verification. Once the exceedance is confirmed, the forest fire prevention plan will be activated, and two teams will be assigned to search for fire sources along the direction of smoke diffusion. At the same time, outdoor fire-related areas within 300 meters of the site will be closed. If an abnormal personnel behavior warning is issued (e.g., tourists climbing over fences), a personnel control request will be triggered. Screenshots of the abnormal behavior and its location will be pushed to the mobile devices of the three nearest security personnel. After arriving at the scene, the security personnel will report the handling results in the system (e.g., "Violating tourists have been persuaded to leave; there is no safety hazard").

[0043] Real-time visitor density, facility operation status, environmental indicators, and personnel behavior data for the controlled area were retrieved from the scenic area's overall database. The changes in data before and after control measures were compared to assess the effectiveness of the control measures. For visitor flow control effectiveness assessment, grid density data for the warning area were retrieved 10 minutes before and 30 minutes after control. For example, if the density was 3.2 people / square meter before control and decreased to 1.6 people / square meter after control, the visitor flow management was deemed effective. For facility control effectiveness assessment, facility operating parameters were compared before and after maintenance. For example, if the roller coaster current was 18A before maintenance and returned to 14A (within the normal range) after maintenance, the facility was confirmed to have returned to normal operation. For environmental control effectiveness assessment, environmental indicator data was reviewed every 5 minutes after control measures were implemented. For example, if the smoke concentration decreased from 0.6 mg / m³... 3 Reduced to 0.1 mg / m³ 3 The environmental risk is deemed eliminated. Regarding the evaluation of personnel control effectiveness, if video surveillance confirms that abnormal behavior has ceased and no similar behavior occurs within 30 minutes, the control is considered effective.

[0044] If data shows that the indicators have returned to normal after control measures, the warning is lifted. If the indicators do not improve, the warning level is upgraded and control measures are adjusted. The data of the control process is stored in the scenic area's database to form a closed-loop management system. When the visitor density remains within the normal range for 20 consecutive minutes, facility operating parameters are stable within normal thresholds, environmental indicators meet standards for three consecutive tests, and there is no recurrence of abnormal behavior by personnel, the system automatically lifts the corresponding warning and updates the grid color on the cloud platform map (e.g., from red to green). If the indicators still do not improve after one hour of control (e.g., visitor density only decreases from 3.2 people / square meter to 2.8 people / square meter), the warning level is upgraded from severe (red) to emergency (purple), and control measures are adjusted, such as adding 5 security personnel to participate in crowd control and suspending ticket sales at attractions around the warning area for one hour. At the same time, information such as request instructions, handling progress records, before-and-after data comparisons, and effect evaluation results during the control process are categorized and stored in the scenic area's database in the format of "control date-warning type-area" for subsequent review and analysis to optimize the control process (e.g., calculating the average handling time for similar warnings and adjusting personnel dispatch plans).

[0045] Corresponding to the aforementioned embodiments of the information-based integrated management method for scenic area construction, this application also provides embodiments of the information-based integrated management system for scenic area construction.

[0046] Figure 5 This is a block diagram of an information-based integrated management system for scenic area construction, illustrated according to an exemplary embodiment. (Refer to...) Figure 5 The system may include: a multi-dimensional data acquisition module 401, a passenger flow dynamic early warning module 402, and a current safety management module 403; wherein: The multi-dimensional data acquisition module 401 is used to collect real-time multi-dimensional data on tourists, facilities, environment, and personnel, extract multi-dimensional features, and construct a comprehensive database of the scenic area. The dynamic passenger flow early warning module 402 is used to calculate real-time passenger flow density based on the scenic area's database, predict passenger flow changes, and push out tiered early warnings. The current safety management module 403 is used to trigger passenger flow, facility, environment, and personnel management requests respectively based on the early warning results, and obtain feedback data to form a closed-loop management.

[0047] In this embodiment, the multi-dimensional data acquisition module 401 collects real-time multi-dimensional data on tourists, facilities, environment, and personnel, extracts multi-dimensional features, and constructs a comprehensive database of the scenic area; the dynamic visitor flow early warning module 402 calculates real-time visitor flow density based on the comprehensive database of the scenic area, predicts changes in visitor flow, and pushes tiered early warnings; the current safety management module 403 triggers visitor flow, facility, environment, and personnel management requests based on the early warning results, and obtains feedback data to form a closed-loop management system; through the above methods, efficient and comprehensive information-based integrated management is achieved to improve the management level of the scenic area and ensure its safety.

[0048] Regarding the system in the above embodiments, the specific ways in which each module performs operations have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0049] For the system embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this application according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0050] Accordingly, this application also provides an electronic device, comprising: one or more processors; a memory for storing one or more programs; and when the one or more programs are executed by the one or more processors, causing the one or more processors to implement the above-described information-based integrated management method for scenic area construction. Figure 6 The diagram shown is a hardware structure diagram of any device with data processing capabilities within an information-based integrated management system for scenic area construction provided by an embodiment of the present invention, except... Figure 6 In addition to the processor, memory, and network interface shown, any data processing device in the embodiment may also include other hardware depending on the actual function of the data processing device, which will not be described in detail here.

[0051] Accordingly, this application also provides a computer-readable storage medium storing computer instructions, which, when executed by a processor, implement the aforementioned information-based integrated management method for scenic area construction. The computer-readable storage medium can be an internal storage unit of any data-processing device as described in any of the foregoing embodiments, such as a hard disk or memory. The computer-readable storage medium can also be an external storage device, such as a plug-in hard disk, smart media card (SMC), SD card, flash card, etc., equipped on the device. Furthermore, the computer-readable storage medium can include both internal storage units of any data-processing device and external storage devices. The computer-readable storage medium is used to store the computer program and other programs and data required by the data-processing device, and can also be used to temporarily store data that has been output or will be output.

[0052] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein.

[0053] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope.

Claims

1. A method for information-based comprehensive management of scenic area construction, characterized in that, Includes the following steps: Collect real-time multi-dimensional data on tourists, facilities, environment, and personnel, extract multi-dimensional features, and construct a comprehensive database of the scenic area; Real-time visitor density is calculated based on the scenic area's comprehensive database, visitor flow changes are predicted, and tiered early warnings are pushed out. Based on the early warning results, requests for passenger flow, facility, environment, and personnel control are triggered respectively, and feedback data is obtained to form a closed-loop management system.

2. The information-based comprehensive management method for scenic area construction according to claim 1, characterized in that, In the steps of collecting real-time multi-dimensional data on tourists, facilities, environment, and personnel, extracting multi-dimensional features, and constructing a comprehensive database of the scenic area: Deploy data collection equipment to collect real-time, multi-dimensional data on tourists, facilities, environment, and personnel; The raw data collected from each dimension is filtered to remove duplicate and invalid data, and missing data is filled in to complete the data cleaning. Extract features from the cleaned data.

3. The information-based comprehensive management method for scenic area construction according to claim 2, characterized in that, In the step of extracting features from cleaned data: Extracting identity identifiers, travel routes, and consumption preference characteristics from the tourist perspective; Extract equipment number, operating parameter thresholds, and fault type features from the facility dimension; Environmental dimensions include extracting regional identifiers, normal ranges of indicators, and early warning triggering conditions. From the personnel dimension, features such as job type, on-duty time, and abnormal behavior category are extracted.

4. The information-based comprehensive management method for scenic area construction according to claim 3, characterized in that, After the step of extracting features from the cleaned data: After extracting features, the data from each dimension are categorized and stored in the scenic area's cloud database. Data relationships are established to form a comprehensive database covering tourists, facilities, environment, and personnel.

5. The information-based integrated management method for scenic area construction as described in claim 1, characterized in that, In the steps of calculating real-time visitor density based on the scenic area's comprehensive database, predicting changes in visitor flow, and issuing tiered early warnings: Real-time tourist location data is retrieved from the scenic area's overall database to calculate real-time visitor density. Based on the functional attributes of different areas of the scenic area, thresholds for visitor flow density are set, and normal, warning, and congestion density ranges are defined. The current density status of each grid is marked with different colors on the cloud platform map. By retrieving historical data from the entire scenic area database and combining it with current visitor numbers, real-time density in each area, and weather data, the trend of visitor density changes in each grid unit is predicted. Early warning information is generated based on the prediction results.

6. The information-based integrated management method for scenic area construction as described in claim 5, characterized in that, In the step of retrieving real-time tourist location data from the scenic area's overall database and calculating real-time visitor density, the calculation process is as follows: The scenic area is divided into fixed-size grid units using a grid division method. The number of tourists in each grid is counted, and the real-time visitor density of each grid is calculated based on the grid area.

7. The information-based integrated management method for scenic area construction as described in claim 6, characterized in that, In the step of generating early warning information based on the prediction results: If the predicted area reaches the warning threshold, a warning will be generated at the first warning time. If the predicted area reaches the congestion threshold, an early warning will be generated at the second warning time.

8. The information-based integrated management method for scenic area construction as described in claim 1, characterized in that, In response to the early warning results, requests for passenger flow, facility, environmental, and personnel control are triggered respectively, and feedback data is obtained to form a closed-loop management process: The system triggers control requests based on early warning results and updates the handling progress in real time. The system retrieves real-time visitor density, facility operation status, environmental indicators, and personnel behavior data of the controlled area from the scenic area's overall database, compares the data changes before and after the control measures, and evaluates the effectiveness of the control measures.

9. The information-based integrated management method for scenic area construction as described in claim 8, characterized in that, After retrieving real-time visitor density, facility operation status, environmental indicators, and personnel behavior data from the scenic area's overall database, comparing the data changes before and after control measures, and evaluating the effectiveness of the control measures: If the data shows that the indicators have returned to the normal range after the control measures are implemented, the warning will be lifted; if the indicators do not improve, the warning level will be upgraded and the control measures will be adjusted. The data of the control process will be stored in the scenic area's database to form a closed-loop management system.

10. An information-based integrated management system for scenic area construction, applied to the information-based integrated management method for scenic area construction as described in claim 1, characterized in that, This includes a multi-dimensional data collection module, a passenger flow dynamic early warning module, and a current safety management module; among which: The multi-dimensional data acquisition module is used to collect real-time multi-dimensional data on tourists, facilities, environment, and personnel, extract multi-dimensional features, and construct a comprehensive database of the scenic area. The dynamic passenger flow early warning module is used to calculate real-time passenger flow density based on the scenic area's database, predict passenger flow changes, and push out tiered early warnings. The current safety management module is used to trigger passenger flow, facility, environment, and personnel management requests based on the early warning results, and obtain feedback data to form a closed-loop management system.

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