Scenic area unmanned post management method and system based on Internet of Things

Abstract

The invention provides a scenic spot unmanned post management method and system based on the Internet of Things. The method comprises the following steps: acquiring Internet of Things sensing information of an area where a target unmanned post is located in a scenic spot; extracting a repeated operation state and an emotion change state of a person who operates the target unmanned post from the Internet of Things perception information; and predicting an abnormal state of the target unmanned post based on the repeated operation state, the emotion change state and the category of the target unmanned post, so as to manage the target unmanned post based on the abnormal state. According to the invention, problems encountered by the user in the process of using the unmanned post can be accurately positioned, and equipment faults or deficiencies in a service process can be timely found and solved, so that the user experience is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of the Internet of Things, and particularly to a management method and system for scenic area unmanned stations based on the Internet of Things. Background Art

[0002] The functions and service types of scenic area unmanned stations include self-guided tour, ticket service, vending machine, luggage storage, emergency assistance station, charging station, information query, etc. These unmanned stations utilize modern information technologies such as the Internet of Things, cloud computing, and big data analysis to achieve effective management and optimal allocation of scenic area resources, improving tourist satisfaction and service efficiency. By setting up scenic area unmanned stations in tourist scenic areas, services can be provided to users without manual intervention.

[0003] However, currently, the degree of automation in managing scenic area unmanned stations is relatively low. Moreover, due to the large fluctuations in the usage frequency of users of scenic area unmanned stations and the relatively low level of understanding of the functions of unmanned stations by users, there may be operational obstacles for self-service for tourists who are not familiar with smart devices. When an abnormality occurs in the unmanned station, users often think that the functions of the unmanned station are not perfect, choose to abandon using the unmanned station and switch to using manual services, resulting in a further reduction in the usage frequency of the unmanned station, and it is difficult for maintenance personnel to detect the abnormal situation of the unmanned station, making it difficult to achieve the original intention of providing services to users. Summary of the Invention

[0004] Embodiments of the present invention provide a management method and system for scenic area unmanned stations based on the Internet of Things to solve the problem of relatively low automation in managing scenic area unmanned stations.

[0005] In a first aspect, embodiments of the present invention provide a management method for scenic area unmanned stations based on the Internet of Things, including: Obtaining Internet of Things perception information of the area where a target unmanned station in the scenic area is located; Extracting the repeated operation state and emotional change state of the personnel operating on the target unmanned station from the Internet of Things perception information; Predicting the abnormal state of the target unmanned station based on the repeated operation state, emotional change state, and category of the target unmanned station, so as to manage the target unmanned station based on the abnormal state.

[0006] In a possible implementation manner, the Internet of Things perception information includes multiple frames of facial images; extracting the emotional change state of the personnel operating on the target unmanned station from the Internet of Things perception information includes: Inputting each frame of facial image into an emotion analysis model to obtain the emotion of the personnel corresponding to each frame of facial image; Determining the emotional change state of the personnel based on the order of each frame of facial image and the corresponding emotion of the personnel.

[0007] In a possible implementation, the IoT perception information includes wireless signals; extracting the repeated operation status of the personnel operating the target unmanned station from the IoT perception information includes: Obtaining the wireless signals in the area where the target unmanned station is located in the scenic area; Identifying the gesture types of the personnel in the area where the target unmanned station is located in the scenic area and the occurrence time periods of each gesture based on the wireless signals; If the consecutive occurrence times of any gesture type are greater than the first preset number of times and the occurrence interval is less than the first preset interval, it is determined that the personnel perform repeated operations on the target unmanned station.

[0008] In a possible implementation, the IoT perception information includes input instructions; extracting the repeated operation status of the personnel operating the target unmanned station from the IoT perception information includes: If the consecutive input times of the input instructions are greater than the second preset number of times and the input interval is less than the second preset interval, it is determined that the personnel perform repeated operations on the target unmanned station.

[0009] In a possible implementation, the repeated operation status includes the repeated operation frequency and the operation duration; predicting the abnormal status of the target unmanned station based on the repeated operation status, the emotional change status, and the category of the target unmanned station includes: Performing a Granger causality test on the repeated operation frequency, the operation duration, and the emotional change status, and judging whether there is a causal relationship between the emotional change status of the personnel and the repeated operation status based on the test results; If there is a causal relationship, it is determined that the target unmanned station has an abnormal status, and the abnormal status of the target unmanned station is predicted based on the abnormal recognition model corresponding to the category of the target unmanned station.

[0010] In a possible implementation, the repeated operation status further includes the repeated operation category; predicting the abnormal status of the target unmanned station based on the abnormal recognition model corresponding to the category of the target unmanned station includes: Inputting the repeated operation category into the abnormal recognition model corresponding to the category of the target unmanned station to obtain the abnormal status of the target unmanned station.

[0011] In a possible implementation, after predicting the abnormal status of the target unmanned station based on the repeated operation status, the emotional change status, and the category of the target unmanned station, it further includes: Generating and playing an inquiry voice based on the abnormal status of the target unmanned station; Collecting the voice feedback information of the personnel; If the voice feedback information is that the abnormal status of the target unmanned station is correct, reporting the abnormal status of the target unmanned station.

[0012] In a second aspect, an embodiment of the present invention provides a management system for unattended scenic area stations based on the Internet of Things, including: An acquisition module, configured to acquire Internet of Things perception information of the area where a target unattended scenic area station is located in the scenic area; An extraction module, configured to extract the repeated operation status and emotional change status of the personnel operating on the target unattended scenic area station from the Internet of Things perception information; A management module, configured to predict the abnormal status of the target unattended scenic area station based on the repeated operation status, emotional change status, and the category of the target unattended scenic area station, so as to manage the target unattended scenic area station based on the abnormal status.

[0013] In a third aspect, an embodiment of the present invention provides a terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method in the first aspect or any possible implementation manner of the first aspect above are implemented.

[0014] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps of the method in the first aspect or any possible implementation manner of the first aspect above are implemented.

[0015] An embodiment of the present invention provides a method and system for managing unattended scenic area stations based on the Internet of Things. By monitoring the behavior patterns (such as repeated operations) and emotional changes of operators through Internet of Things perception information, problems encountered by users during the use of unattended scenic area stations can be located more accurately, which helps to timely discover and solve equipment failures or deficiencies in service processes, thereby improving the user experience, effectively enhancing the operation efficiency and service quality of unattended scenic area stations in the scenic area, and can also provide a more considerate and convenient service experience for users. At the same time, it provides support for scientific decision-making for managers and is an important means to achieve intelligent management and high-quality services. Description of the Drawings

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0017] Figure 1 It is a flowchart of the implementation of a method for managing unattended scenic area stations based on the Internet of Things provided by an embodiment of the present invention; Figure 2It is a schematic structural diagram of a scenic area unmanned station management system based on the Internet of Things provided by an embodiment of the present invention; Figure 3 It is a schematic diagram of a terminal provided by an embodiment of the present invention. Detailed implementation manners

[0018] In the following description, specific details such as specific system structures and technologies are proposed for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present invention. However, those skilled in the art should clearly understand that the present invention can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present invention.

[0019] To make the purpose, technical solutions, and advantages of the present invention clearer, the following will be described through specific embodiments with reference to the accompanying drawings.

[0020] Refer to Figure 1 , which shows the implementation flowchart of the scenic area unmanned station management method based on the Internet of Things provided by the embodiment of the present invention, and is described in detail as follows: Step 101, obtain the Internet of Things perception information of the area where the target unmanned station in the scenic area is located.

[0021] In this embodiment, the management of the scenic area unmanned station based on the Internet of Things technology can greatly improve the operation efficiency and service quality. The following are some system structures and configurations for realizing this function: 1. Device access and networking Internet of Things sensors and devices: Deploy various sensors (such as temperature and humidity sensors, smoke sensors, cameras, etc.) and intelligent devices (such as vending machines, intelligent door locks, etc.) inside the station.

[0022] Network connection: Ensure that all devices can access the Internet through wireless technologies such as Wi-Fi, Bluetooth, NB-IoT, or LoRa.

[0023] 2. Data collection and processing Data collection: Real-time collect information such as environmental data, device status, and video monitoring.

[0024] Data processing: Process and analyze the collected data through edge computing or cloud computing.

[0025] 3. System integration and management platform Integration platform: Build a central management platform to integrate all Internet of Things device and sensor data.

[0026] Data visualization: Realize data visualization through a large screen or a Web interface, which is convenient for management personnel to monitor and analyze.

[0027] 4. Specific Applications and Management Measures Environmental Monitoring: Real-time monitoring of the environmental conditions inside the rest stop, such as temperature, humidity, etc., and automatic adjustment of equipment such as air conditioners and humidifiers.

[0028] Security Monitoring: Use cameras for real-time monitoring and combine face recognition technology for security control.

[0029] Intelligent Vending: Provide goods for tourists through vending machines and automatically replenish stock in combination with the inventory management system.

[0030] Intelligent Lighting: Automatically adjust lighting according to the light intensity and the number of tourists.

[0031] Energy Management: Monitor electricity usage, optimize energy consumption, and achieve energy conservation and emission reduction.

[0032] Information Dissemination: Publish scenic area information, weather forecasts, safety tips, etc. through electronic displays or voice systems.

[0033] 5. Visitor Interaction and Services Self-Service Terminals: Provide services such as self-guided tours, electronic maps, and online ticket purchases.

[0034] Mobile Applications: Develop a scenic area APP and integrate the services of the unmanned rest stop, such as navigation, reservation, payment, etc.

[0035] Voice Assistants: Deploy voice assistants to provide tourists with consultations and assistance.

[0036] 6. Maintenance and Operation Remote Control: Managers can remotely control the equipment inside the rest stop through the management platform.

[0037] Predictive Maintenance: Analyze the equipment operation data to predict potential failures and maintenance requirements.

[0038] Emergency Response: In case of abnormal situations, the system will automatically alarm and notify the managers.

[0039] 7. Safety and Privacy Data Encryption: Ensure the security of data during the transmission process.

[0040] Permission Management: Set different levels of access permissions to protect the data security of tourists and the system.

[0041] The perception information of the Internet of Things refers to the data collected by various sensors (such as WiFi probes, cameras, temperature and humidity sensors, etc.) deployed in a specific area, which reflects the environmental status and personnel activities. Using wireless communication technologies (such as WiFi, Bluetooth, Zigbee, etc.), the sensors can capture information such as the signal strength (RSSI), MAC address, and video images of surrounding devices. By analyzing this data, useful information about the user's location, behavior patterns, etc. can be obtained, thereby assisting in judging the status of the unattended station.

[0042] Step 102, extract the repeated operation status and emotional change status of the personnel operating the target unattended station from the Internet of Things perception information.

[0043] In this embodiment, the repeated operation status can be the frequency and duration of the user performing the same or similar operations on the same unattended station. By analyzing the log records of the user's interaction with the unattended station (such as the number of clicks, stay time, etc.), identify those operation behaviors that exceed the normal range, which may be caused by system failures, poor user experience, etc.

[0044] The emotional change status refers to the change trend of the user's emotions during the use of the unattended station, including positive emotions (such as satisfaction, happiness) and negative emotions (such as frustration, anger). By using computer vision technology and natural language processing technology, emotional features can be extracted from the user's facial expressions or speech and converted into quantified emotion scores.

[0045] By analyzing the repeated operation status and emotional change status of the user when operating the target unattended station, it can be judged whether this information is related to system failures or poor user experience of the target unattended station.

[0046] Step 103, predict the abnormal status of the target unattended station based on the repeated operation status, emotional change status, and category of the target unattended station, so as to manage the target unattended station based on the abnormal status.

[0047] In this embodiment, based on the above analysis results (repeated operation status, emotional change status, etc.), combined with the specific type of the unattended station (such as ticket service, vending machine, etc.), machine learning algorithms (such as support vector machine, random forest classifier, etc.) can be used to establish a model to predict the occurrence probability of the abnormal status, and corresponding management measures can be taken accordingly.

[0048] Combining the characteristics of different types of unattended stations to determine the conditions for their anomalies can further refine the monitoring and identification mechanisms. Each type of unattended station has its uniqueness in function and service, so it is necessary to customize the anomaly determination conditions according to these characteristics. The following are specific suggestions for several typical types of unattended stations: 1. Self-guided tour system Abnormal conditions: The user stays on the same information page for a long time (exceeding the preset time).

[0049] Frequently click the "Back" or "Help" button.

[0050] Fail to respond or respond incorrectly to voice commands multiple times.

[0051] 2. Ticket service system Abnormal conditions: The payment failure rate is significantly higher than the average level.

[0052] Frequently request refunds within a short time after successfully purchasing tickets.

[0053] Multiple users report the same ticket problems (such as the QR code not working).

[0054] 3. Vending machine Abnormal conditions: The number of failed product shipments exceeds a certain proportion.

[0055] The cases of receiving counterfeit money or failing to recognize coins / banknotes increase.

[0056] The inventory is exhausted but not replenished in time.

[0057] 4. Luggage locker Abnormal conditions: The failure rate of opening and closing the door increases.

[0058] The user tries to open a locker that does not belong to him / her.

[0059] Complaints about the fee calculation increase.

[0060] 5. Emergency assistance station Abnormal conditions: The help signal is not responded to in time.

[0061] The pressing frequency of the emergency button is abnormally high or low (which may indicate frequent equipment failures or emergencies).

[0062] The device is offline for too long.

[0063] 6. Charging station Abnormal conditions: Reports of damaged or unavailable charging interfaces increase.

[0064] The charging efficiency is significantly lower than the expected standard.

[0065] Users feedback that the battery level is insufficient after charging is completed.

[0066] 7. Information query terminal Abnormal conditions: Information errors caused by untimely data updates.

[0067] The terminal responds sluggishly or does not respond at all.

[0068] Users repeatedly access the same problem without getting a satisfactory answer.

[0069] When applying these conditions to judge the abnormal state, factors such as age and gender that affect the user experience should also be considered, and different thresholds and judgment criteria should be set for different groups.

[0070] The embodiments of the present invention monitor the behavior patterns (such as repeated operations) and emotional changes of operators through Internet of Things perception information, can more accurately locate the problems encountered by users during the use of unmanned stations, help to timely discover and solve equipment failures or deficiencies in service processes, thereby improving the user experience, effectively enhancing the operation efficiency and service quality of unmanned stations in scenic areas, can also provide users with a more considerate and convenient service experience, and at the same time provide support for scientific decision-making for managers, which is an important means to achieve intelligent management and high-quality services.

[0071] In a possible implementation, the Internet of Things perception information includes multiple frames of facial images; extracting the emotional change state of the person operating the target unmanned station from the Internet of Things perception information includes: Inputting each frame of facial image into an emotion analysis model to obtain the emotion of the person corresponding to each frame of facial image; Based on the order of each frame of facial image and the corresponding emotion of the person, determining the emotional change state of the person.

[0072] In this embodiment, using computer vision technology, capturing the facial images of users through a camera and using a deep learning model to analyze facial expressions to judge the emotional state of users (such as happy, sad, angry, surprised, etc.). This method is applicable to most applications with a direct face-to-camera scenario.

[0073] Using a pre-trained emotion recognition model (such as DeepFace or FER+) to process each frame of facial image, the corresponding emotion score can be obtained. Then, based on these scores and their chronological order, analyze how the emotion changes over time. This may involve calculating the change rate of emotion scores or identifying specific emotion patterns (such as a sudden deterioration of emotion may be due to encountering problems).

[0074] If it is detected that the user's emotion changes from positive to negative and this change occurs during the process of attempting to complete a certain task, it may be due to operational difficulties or other problems. At this time, the system can automatically pop up a help guide or suggest contacting customer service to help the user complete the operation smoothly.

[0075] In addition, if the unattended station supports voice interaction, the emotional state of the user can also be inferred by analyzing the user's voice characteristics (such as pitch, speech rate, intonation, etc.). Natural Language Processing (NLP) technology can help understand the content of the discourse and the emotional tendency behind it.

[0076] In a possible implementation, the Internet of Things perception information includes wireless signals; the repeated operation status of the person operating on the target unattended station is extracted from the Internet of Things perception information, including: Obtain the wireless signals in the area where the target unattended station in the scenic area is located; Based on the wireless signals, identify the gesture types of the people in the area where the target unattended station in the scenic area is located and the appearance time periods of each gesture; If the continuous occurrence times of any gesture type are greater than the first preset number of times and the occurrence interval is less than the first preset interval, it is determined that the person performs repeated operations on the target unattended station.

[0077] In this embodiment, the wireless signal can be a WiFi signal. The first preset number of times and the first preset interval can be set according to experience and appropriately adjusted according to the age and gender of the user. When a person moves, it will change the reflection path of the WiFi signal, thereby causing a change in the received signal frequency. By analyzing these changes, gesture actions can be inferred. Modern WiFi devices can provide detailed channel state information, including changes in signal amplitude and phase, which provides a rich data source for gesture recognition. Collect CSI data under different gestures for a period of time. Use machine learning algorithms (such as Support Vector Machine SVM, Convolutional Neural Network CNN, etc.) to train the extracted features to distinguish different gesture patterns.

[0078] In a possible implementation, the Internet of Things perception information includes input instructions; the repeated operation status of the person operating on the target unattended station is extracted from the Internet of Things perception information, including: If the continuous input times of the input instructions are greater than the second preset number of times and the input interval is less than the second preset interval, it is determined that the person performs repeated operations on the target unattended station.

[0079] In this embodiment, by observing the user's operation behaviors (such as click frequency, dwell time, number of repeated attempts, etc.), the user's emotional state can be indirectly inferred. For example, frequent incorrect operations or long stays at a certain step may indicate that the user is confused or frustrated. The second preset number and the second preset interval can be set according to experience and appropriately adjusted according to the user's age and gender.

[0080] When the data transmission between the target unmanned service station and the central management platform is normal, the central management platform can directly determine the instruction input by the user through the target unmanned service station. Taking a self-service ticket vending machine as an example, if it is detected that the user is performing repeated operations on the self-service ticket vending machine (such as repeatedly attempting to submit an order but not succeeding), the system can automatically display a friendly prompt message asking if help is needed, or directly connect to the customer service center to further assist the user in solving the problem. At the same time, the system can also send the information of these repeated operations to the background management system for technicians to analyze the cause of the problem, and then optimize the system performance or adjust the interface design.

[0081] In a possible implementation manner, the repeated operation state includes the repeated operation frequency and the operation duration; based on the repeated operation state, the emotional change state, and the category of the target unmanned service station, predicting the abnormal state of the target unmanned service station includes: Performing a Granger causality test on the repeated operation frequency, the operation duration, and the emotional change state, and judging whether there is a causal relationship between the emotional change state of the person and the repeated operation state based on the test result; If there is a causal relationship, it is determined that the target unmanned service station is in an abnormal state, and the abnormal state of the target unmanned service station is predicted based on the abnormal recognition model corresponding to the category of the target unmanned service station.

[0082] In this embodiment, the Granger causality test is a statistical method used to determine whether one time series can predict another time series. Based on time series data, the Granger causality test judges whether there is a causal relationship by comparing the prediction capabilities between two variables. If one variable (such as the emotional change state) can significantly improve the prediction accuracy of another variable (such as the repeated operation state), then the former is considered to "Granger cause" the latter.

[0083] Since the user's repeated operation state usually has a causal relationship with the abnormal state of the unmanned service station, if there is also a causal relationship between the user's emotional change state and the repeated operation state, it is considered that the unmanned service station is very likely to be in an abnormal state.

[0084] Taking a self-guided tour system in a scenic area as an example, whenever a user interacts with the self-guided tour system (such as querying scenic spot information, selecting a route, etc.), the system records the corresponding input commands and their timestamps. At the same time, a camera is used to capture the user's facial expressions, and these expressions are converted into emotion scores through an emotion analysis model. Also, for each command type, the number of occurrences within a certain time period (repetitive operation frequency) is calculated. The duration of each operation (operation duration) is calculated, that is, the time difference from the start of executing the command to the completion or abandonment of the command.

[0085] If the Granger causality test indicates a causal relationship between the emotional change state and the repetitive operation state, it is considered that the unattended station is in an abnormal state, and an anomaly recognition model based on the category of the unattended station is further used to predict the specific abnormal state.

[0086] In a possible implementation, the repetitive operation state also includes the repetitive operation category; predicting the abnormal state of the target unattended station based on the anomaly recognition model corresponding to the category of the target unattended station includes: Inputting the repetitive operation category into the anomaly recognition model corresponding to the category of the target unattended station to obtain the abnormal state of the target unattended station.

[0087] In this embodiment, the repetitive operation category refers to the specific operation type performed by the user on the unattended station. For example, in a self-service ticket vending machine, the repetitive operation category may include "selecting a destination", "inputting a date", "making a payment", etc. The anomaly recognition model is a machine learning model specifically used to predict the specific abnormal state of the unattended station s. This model is customized and trained according to the category of the unattended station (such as self-service ticket vending machine, vending machine, etc.) to adapt to different types of business scenarios.

[0088] Suppose in a specific scenario, the user of the self-service ticket vending machine frequently attempts to select a destination but fails to purchase a ticket successfully, and there is an increase in negative emotions: Data collection: The user attempts to select a destination multiple times, and the system records the selected destination, operation time, and emotion score each time.

[0089] Feature extraction: Statistics show that the repetitive frequency of the operation of "selecting a destination" is relatively high, and the operation duration is relatively short, indicating that the user may have encountered a problem.

[0090] The emotion score shows that the user's emotion gradually becomes negative.

[0091] Anomaly recognition model prediction: Input the extracted features (repetitive operation frequency, operation duration, repetitive operation category, emotion score) into the anomaly recognition model of the self-service ticket vending machine.

[0092] The model output shows that the current self-service ticket vending machine is in an abnormal state.

[0093] Measures to be taken: The system automatically pops up a help prompt, suggesting that the user check the network connection or try again later.

[0094] At the same time, the background management system receives an alarm, and technicians can quickly troubleshoot problems to ensure that the device resumes normal operation.

[0095] By analyzing a large amount of user interaction data, managers can gain important insights into which aspects most often cause operation difficulties, guide product improvement and service upgrade, and give early warnings of potential operation anomalies, which helps to quickly locate and solve system failures, thus improving the stability and reliability of the system.

[0096] In a possible implementation, after predicting the abnormal state of the target unmanned service station based on the repeated operation state, emotional change state, and category of the target unmanned service station, it further includes: Generating and playing an inquiry voice based on the abnormal state of the target unmanned service station; Collecting the voice feedback information of the personnel; If the voice feedback information is that the abnormal state of the target unmanned service station is correct, reporting the abnormal state of the target unmanned service station.

[0097] In this embodiment, when it is predicted that there may be an abnormal state in the unmanned service station, the system will automatically generate an inquiry voice and play it to the user to confirm whether there is really a problem. Specifically, according to the predicted content of the abnormal state, the text-to-speech (TTS) technology can be used to generate corresponding inquiry sentences and play them to the user through the audio device. For example, "We noticed that you encountered some problems during the ticket purchase process. Do you need any help?" Then, the speech recognition technology is used to capture the user's voice reply and convert it into text format. This step usually involves natural language processing (NLP) technology to understand the user's intention. If the user's voice feedback confirms that there is indeed an abnormal state in the unmanned service station, the system will report this situation for subsequent processing.

[0098] This method can not only effectively improve the operation efficiency and service quality of the unmanned service station in the scenic area, but also provide a more considerate and convenient service experience for users. By combining user behavior analysis, emotion monitoring, and voice interaction technology, a comprehensive abnormal detection mechanism is provided, which helps to solve problems in a timely manner and improve the user experience.

[0099] It should be understood that the sequence numbers of the steps in the above embodiments do not indicate the order of execution, and the execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0100] The following is a system embodiment of the present invention. For the details not described in detail, reference may be made to the corresponding method embodiments above.

[0101] Figure 2 The structural schematic diagram of the scenic area unmanned post management system based on the Internet of Things provided by the embodiment of the present invention is shown. For the convenience of description, only the parts related to the embodiment of the present invention are shown and are described in detail as follows: As Figure 2 shown, the scenic area unmanned post management system 2 based on the Internet of Things includes: An acquisition module 21, configured to acquire the Internet of Things perception information of the area where the target unmanned post in the scenic area is located; An extraction module 22, configured to extract the repeated operation state and emotional change state of the personnel operating on the target unmanned post from the Internet of Things perception information; A management module 23, configured to predict the abnormal state of the target unmanned post based on the repeated operation state, emotional change state and the category of the target unmanned post, so as to manage the target unmanned post based on the abnormal state.

[0102] In a possible implementation manner, the Internet of Things perception information includes multiple frames of facial images; the extraction module 22 is specifically configured to: Input each frame of facial image into the emotion analysis model to obtain the emotion of the personnel corresponding to each frame of facial image; Determine the emotional change state of the personnel based on the order of each frame of facial image and the corresponding emotion of the personnel.

[0103] In a possible implementation manner, the Internet of Things perception information includes wireless signals; the extraction module 22 is specifically configured to: Acquire the wireless signals of the area where the target unmanned post in the scenic area is located; Identify the gesture types of the personnel in the area where the target unmanned post in the scenic area is located and the appearance time periods of each gesture based on the wireless signals; If the consecutive occurrence times of any gesture type are greater than the first preset number of times and the occurrence interval is less than the first preset interval, it is determined that the personnel perform repeated operations on the target unmanned post.

[0104] In a possible implementation manner, the Internet of Things perception information includes input instructions; the extraction module 22 is specifically configured to: If the consecutive input times of the input instructions are greater than the second preset number of times and the input interval is less than the second preset interval, it is determined that the personnel perform repeated operations on the target unmanned post.

[0105] In a possible implementation, the repeated operation state includes the repeated operation frequency and the operation duration; specifically, the management module 23 is configured to: Perform a Granger causality test on the repeated operation frequency, the operation duration, and the emotional change state, and determine whether there is a causal relationship between the emotional change state of the person and the repeated operation state based on the test result; If there is a causal relationship, it is determined that the target unmanned service station is in an abnormal state, and the abnormal state of the target unmanned service station is predicted based on the abnormal recognition model corresponding to the category of the target unmanned service station.

[0106] In a possible implementation, the repeated operation state further includes the repeated operation category; specifically, the management module 23 is configured to: Input the repeated operation category into the abnormal recognition model corresponding to the category of the target unmanned service station to obtain the abnormal state of the target unmanned service station.

[0107] In a possible implementation, the management module 23 is further configured to: After predicting the abnormal state of the target unmanned service station based on the repeated operation state, the emotional change state, and the category of the target unmanned service station, generate an inquiry voice based on the abnormal state of the target unmanned service station and play it; Collect the voice feedback information of the person; If the voice feedback information is that the abnormal state of the target unmanned service station is correct, report the abnormal state of the target unmanned service station.

[0108] In the embodiments of the present invention, by using the Internet of Things perception information, the monitoring of the behavior patterns (such as repeated operations) and emotional changes of the operators can be realized, the problems encountered by users during the use of the unmanned service station can be located more accurately, which helps to timely discover and solve the equipment failures or deficiencies in the service process, thereby improving the user experience, effectively enhancing the operation efficiency and service quality of the unmanned service station in the scenic area, and can also provide a more considerate and convenient service experience for users. At the same time, it provides support for scientific decision-making for managers and is an important means to achieve intelligent management and high-quality service.

[0109] Figure 3 It is a schematic diagram of the terminal provided by the embodiments of the present invention. As Figure 3 shown, the terminal 3 of this embodiment includes: a processor 30, a memory 31, and a computer program 32 stored in the memory 31 and executable on the processor 30. When the processor 30 executes the computer program 32, the steps in the above-mentioned various embodiments of the method for managing an unmanned service station in a scenic area based on the Internet of Things are implemented, such as Figure 1 the steps 101 to 103 shown. Alternatively, when the processor 30 executes the computer program 32, the functions of the various modules / units in the above-mentioned system embodiments are implemented, such as Figure 2Functions of the illustrated modules / units 21 to 23.

[0110] Exemplarily, the computer program 32 may be divided into one or more modules / units, which are stored in the memory 31 and executed by the processor 30 to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing specific functions, and these instruction segments are used to describe the execution process of the computer program 32 in the terminal 3. For example, the computer program 32 may be divided into Figure 2 the illustrated modules / units 21 to 23.

[0111] The terminal 3 may be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The terminal 3 may include, but is not limited to, a processor 30 and a memory 31. Those skilled in the art can understand that Figure 3 merely examples of the terminal 3, which do not constitute a limitation to the terminal 3, may include more or fewer components than shown in the figure, or combine certain components, or different components. For example, the terminal may further include input / output devices, network access devices, a bus, etc.

[0112] The so-called processor 30 may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0113] The memory 31 may be an internal storage unit of the terminal 3, such as the hard disk or memory of the terminal 3. The memory 31 may also be an external storage device of the terminal 3, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the terminal 3. Further, the memory 31 may also include both the internal storage unit and the external storage device of the terminal 3. The memory 31 is used to store the computer program and other programs and data required by the terminal 3. The memory 31 may also be used to temporarily store data that has been output or will be output.

[0114] Those skilled in the art can clearly understand that, for the convenience and conciseness of description, only the above division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of this application. The specific working processes of the units and modules in the above system can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0115] In the above embodiments, the descriptions of the various embodiments have their own emphases. For the parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0116] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed in the form of hardware or software depends on the specific application and design constraints of the technical solution. Professionals can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0117] In the embodiments provided by the present invention, it should be understood that the disclosed device / terminal and method can be implemented in other ways. For example, the device / terminal embodiments described above are only illustrative. For example, the division of the module or unit is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection can be through some interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical, mechanical or other form.

[0118] The unit described as a separated component may or may not be physically separated, and the component displayed as a unit may or may not be a physical unit, that is, it can be located in one place, or can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0119] In addition, in each embodiment of the present invention, each functional unit can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0120] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, to implement all or part of the processes in the above-mentioned embodiment methods of the present invention, it can also be completed by a computer program instructing relevant hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of each of the above-mentioned embodiment methods of the scenic area unmanned post management method based on the Internet of Things can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device that can carry the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0121] The above-mentioned embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of each embodiment of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. An Internet of Things-based management method for unmanned scenic area rest stops, characterized in that, Including: Obtain the Internet of Things perception information of the area where the target unmanned post station is located in the scenic area; Extract the repeated operation status and emotional change status of the personnel operating the target unmanned post station from the Internet of Things perception information; Predict the abnormal status of the target unmanned post station based on the repeated operation status, the emotional change status, and the category of the target unmanned post station, so as to manage the target unmanned post station based on the abnormal status.

2. The method for managing a scenic area unmanned post based on the Internet of Things according to claim 1, wherein, The Internet of Things perception information includes multiple frames of facial images; extracting the emotional change status of the personnel operating the target unmanned post station from the Internet of Things perception information includes: Input each frame of facial image into the emotion analysis model to obtain the emotion of the personnel corresponding to each frame of facial image; Determine the emotional change status of the personnel based on the order of each frame of facial image and the corresponding emotion of the personnel.

3. The method for managing a scenic area unmanned post based on the Internet of Things according to claim 1, characterized in that The Internet of Things perception information includes wireless signals; extracting the repeated operation status of the personnel operating the target unmanned post station from the Internet of Things perception information includes: Obtain the wireless signals of the area where the target unmanned post station is located in the scenic area; Identify the gesture types of the personnel in the area where the target unmanned post station is located in the scenic area and the occurrence time periods of each gesture based on the wireless signals; If the continuous occurrence times of any gesture type are greater than the first preset number of times and the occurrence interval is less than the first preset interval, it is determined that the personnel perform repeated operations on the target unmanned post station.

4. The method for managing a scenic area unmanned post based on the Internet of Things according to claim 1, wherein, The Internet of Things perception information includes input instructions; extracting the repeated operation status of the personnel operating the target unmanned post station from the Internet of Things perception information includes: If the continuous input times of the input instructions are greater than the second preset number of times and the input interval is less than the second preset interval, it is determined that the personnel perform repeated operations on the target unmanned post station.

5. The method for managing a scenic area unmanned post based on the Internet of Things according to claim 1, wherein The repeated operation status includes the repeated operation frequency and the operation duration; predicting the abnormal status of the target unmanned post station based on the repeated operation status, the emotional change status, and the category of the target unmanned post station includes: Perform a Granger causality test on the repeated operation frequency, the operation duration, and the emotional change status, and judge whether there is a causal relationship between the emotional change status and the repeated operation status of the personnel based on the test results; If there is a causal relationship, it is determined that the target unmanned post station has an abnormal status, and the abnormal status of the target unmanned post station is predicted based on the abnormal recognition model corresponding to the category of the target unmanned post station.

6. The method for managing a scenic area unmanned post based on the Internet of Things according to claim 5, characterized in that, The repeated operation status further includes the repeated operation category; predicting the abnormal status of the target unmanned post station based on the abnormal recognition model corresponding to the category of the target unmanned post station includes: Input the repeated operation category into the abnormal recognition model corresponding to the category of the target unmanned post station to obtain the abnormal status of the target unmanned post station.

7. The method for managing a scenic area unmanned post based on the Internet of Things according to claim 1, characterized in that After predicting the abnormal status of the target unmanned post station based on the repeated operation status, the emotional change status, and the category of the target unmanned post station, it further includes: Generate an inquiry voice based on the abnormal status of the target unmanned post station and play it; Collect the voice feedback information of the personnel; If the voice feedback information is correct for the abnormal status of the target unmanned service station, report the abnormal status of the target unmanned service station.

8. An unattended post management system for scenic spots based on the Internet of Things, characterized in that, Including: An acquisition module, configured to acquire Internet of Things perception information of the area where the target unmanned service station is located in the scenic area; An extraction module, configured to extract the repeated operation status and emotional change status of the personnel operating on the target unmanned service station from the Internet of Things perception information; A management module, configured to predict the abnormal status of the target unmanned service station based on the repeated operation status, the emotional change status, and the category of the target unmanned service station, so as to manage the target unmanned service station based on the abnormal status.

9. A terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 above are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 7 above are implemented.

Images