Multi-person check-in management and early warning method and system based on online room reservation

Abstract

The invention relates to the technical field of online booked house management, in particular to a multi-person check-in management and early warning method and system based on an online booked house. The method comprises the following steps: firstly, screening out multi-person gathering according to check-in information, and then carrying out preliminary judgment according to the historical check-in frequency of check-in and whether the online booked house belongs to a preset high-frequency area; further, in combination with a door lock opening record, collected video data at a doorway on the outer side of the online booked room and the number of human bodies detected by a human body number sensing device in the online booked room, judgment is performed in sequence; and finally, comprehensively obtaining a conclusion of the suspected multi-person gathering abnormal behavior, and sending multi-person gathering abnormal behavior early warning information to an online room-hailing management terminal, thereby realizing early warning of the multi-person gathering abnormal behavior of the online room-hailing, and subsequently reporting the information to a networked public security management platform by the online room-hailing management terminal, thereby avoiding adverse conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of online rental housing management, and particularly to a method and system for managing and warning multiple-person check-ins based on online rental housing. Background Art

[0002] In recent years, as a new model of the Internet sharing economy, online rental housing has quietly emerged across the country. The landlord registers the idle housing as an online housing source through an online reservation platform. The tenant selects the room type and makes an electronic payment through the Internet to complete the online order placement operation. Then, the tenant uses the near-field communication device on the mobile terminal to unlock the smart lock of the online rental housing and can check in with just a bag. Online rental housing is mainly characterized by online short-term rental, meeting the needs of young people such as contactless, cost-effectiveness, and personalization. Therefore, online rental housing has become one of the top choices for more and more people when traveling and staying.

[0003] However, there are deficiencies in the management of online rental housing, especially for the fact that multiple people gathering is likely to induce adverse events. Therefore, a method and system for managing and warning multiple-person check-ins based on online rental housing are needed to solve the above technical problems. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a method and system for managing and warning multiple-person check-ins based on online rental housing to solve the above technical problems.

[0005] To achieve the above purpose, the technical solution adopted by the present invention is as follows: A method for managing and warning multiple-person check-ins based on online rental housing includes the following steps: S1. Obtain all check-in information of the online rental housing, extract the check-in information with the number of check-in registrations exceeding the first preset threshold from all check-in information to obtain the first check-in information; S2. Obtain the historical check-in records of the check-in persons and calculate the historical check-in frequency. When the calculated historical check-in frequency is greater than the second preset threshold, determine whether the online rental housing belongs to a preset high-frequency area. If so, obtain the door opening records of the door lock corresponding to the online rental housing during the check-in period of the check-in persons; S3. According to the door opening records of the door lock, determine whether the number of internal door openings is not higher than the third preset threshold. If so, obtain the video data at the outside door of the online rental housing collected during the check-in period of the check-in persons; S4. Determine whether the portrait data in the video data is greater than the fourth preset threshold. If not, obtain the number of people detected by the human body quantity sensing device in the online rental housing; S5. Determine whether the number of people exceeds the number of check-in registrations. If so, send a warning message of abnormal behavior of multiple people gathering to the online rental housing management terminal.

[0006] Furthermore, step S5 is specifically as follows: Determine whether the number of people exceeds the number of occupants. If so, obtain the number of networked wireless networks configured in the online booking room, and determine whether the number of networked networks exceeds the number of occupants. If so, send an abnormal behavior warning message of multiple people gathering to the online booking room management end.

[0007] Furthermore, step S5 is specifically as follows: Determine whether the number of human bodies exceeds the number of occupants. If so, obtain the number of networked wireless networks configured in the online booking room, and determine whether the number of networked networks exceeds the number of occupants. If so, obtain the number of power switches and power-on duration configured in the online booking room. When the number of power switches is less than the fifth preset threshold and the power-on duration is greater than the sixth preset threshold, send an abnormal behavior warning message of multiple people gathering to the online booking room management end.

[0008] Furthermore, the fifth preset threshold is set to 2 times, and the sixth preset threshold is set to 15 hours.

[0009] Furthermore, the third preset threshold is set to zero; the first preset threshold and the fourth preset threshold are both 2 persons; and the second preset threshold is 2 times per week and 6 times per month.

[0010] A system for multi-occupancy management and early warning based on online booking rooms, comprising a processor and a memory, wherein the memory stores a program or instruction, and when the program or instruction is executed by the processor, the following steps are implemented: S1. Obtain all check-in information of the online booking room, extract the check-in information in which the number of registered check-ins exceeds a first preset threshold from all the check-in information, and obtain the first check-in information; S2. Obtain the historical check-in records of the occupant and calculate the historical check-in frequency. When the calculated historical check-in frequency is greater than a second preset threshold, determine whether the online booking room belongs to a preset high-frequency area. If so, obtain the door lock opening record corresponding to the online booking room during the occupant's stay. S3, judging whether the number of times the door is opened from the inside is not higher than a third preset threshold value according to the door lock opening record, and if so, obtaining video data of the outside door of the online booking room collected by the occupant during the stay; S4, determining whether the portrait data contained in the video data is greater than a fourth preset threshold, and if not, obtaining the number of human bodies detected by the human body number sensing device in the online booking room; S5. Determine whether the number of people exceeds the number of registered guests. If so, send an early warning message of abnormal behavior of multiple people gathering to the online booking room management terminal.

[0011] Furthermore, when the program or instruction is executed by the processor, the following steps are specifically implemented: Determine whether the number of people exceeds the number of occupants. If so, obtain the number of networked wireless networks configured in the online booking room, and determine whether the number of networked networks exceeds the number of occupants. If so, send an abnormal behavior warning message of multiple people gathering to the online booking room management end.

[0012] Furthermore, when the program or instruction is executed by the processor, the following steps are specifically implemented: Determine whether the number of human bodies exceeds the number of occupants. If so, obtain the number of networked wireless networks configured in the online booking room, and determine whether the number of networked networks exceeds the number of occupants. If so, obtain the number of power switches and power-on duration configured in the online booking room. When the number of power switches is less than the fifth preset threshold and the power-on duration is greater than the sixth preset threshold, send an abnormal behavior warning message of multiple people gathering to the online booking room management end.

[0013] Furthermore, when the program or instruction is executed by the processor, the following steps are implemented: the fifth preset threshold is set to 2 times, and the sixth preset threshold is set to 15 hours.

[0014] Furthermore, when the program or instruction is executed by the processor, the following steps are implemented: the third preset threshold is set to zero; the first preset threshold and the fourth preset threshold are both 2 people, and the second preset threshold is 2 times per week and 6 times per month.

[0015] The beneficial effects of the present invention are: A method and system for multi - person check - in management and warning based on online - reserved rooms. By obtaining all check - in information of the online - reserved rooms, extracting the check - in information with the number of check - in registrations exceeding the first preset threshold from all check - in information, the first check - in information is obtained; obtaining the historical check - in records of the check - in persons and calculating the historical check - in frequency. When the calculated historical check - in frequency is greater than the second preset threshold, it is judged whether the online - reserved room belongs to a preset high - frequency area. If so, the door - opening records of the door lock corresponding to the online - reserved room during the check - in period of the check - in person are obtained; according to the door - opening records of the door lock, it is judged whether the number of times of opening the door from the inside is not higher than the third preset threshold. If so, the video data at the outside door of the online - reserved room collected during the check - in period of the check - in person is obtained; it is judged whether the number of portrait data in the video data is greater than the fourth preset threshold. If not, the number of people detected by the human body quantity sensing device in the online - reserved room is obtained; it is judged whether the number of people exceeds the number of check - in registrations. If so, a warning information about abnormal multi - person gathering behavior is sent to the management terminal of the online - reserved room. Through the above - mentioned specific method, multi - person gathering is first screened out from the check - in information, and then a preliminary judgment is made according to the historical check - in frequency of the check - in and whether the online - reserved room belongs to a preset high - frequency area. Then, further judgments are made in turn by combining the door - opening records of the door lock, the video data at the outside door of the online - reserved room collected, and the number of people detected by the human body quantity sensing device in the online - reserved room. Finally, a conclusion of suspected abnormal multi - person gathering behavior is comprehensively obtained and a warning information about abnormal multi - person gathering behavior is sent to the management terminal of the online - reserved room, so as to realize the warning of abnormal multi - person gathering behavior in the online - reserved room. Subsequently, the management terminal of the online - reserved room can report to the connected public security management platform to avoid bad situations. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 The figure shows a flowchart of the steps of a method for multi - person check - in management and warning based on online - reserved rooms according to the present invention; Figure 2 The figure shows a block diagram of the structure of a system for multi - person check - in management and warning based on online - reserved rooms according to the present invention; Explanation of the reference numerals in the drawings: 1 - Processor; 2 - Memory. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] The following further describes the present invention with reference to the drawings and specific embodiments: As Figure 1 shown, a method for multi - person check - in management and warning based on online - reserved rooms provided by the present invention includes the following steps: S1. Obtain all check - in information of the online - reserved room, and extract the check - in information with the number of check - in registrations exceeding the first preset threshold from all check - in information to obtain the first check - in information; S2. Obtain the historical check-in records of the occupant and calculate the historical check-in frequency. When the calculated historical check-in frequency is greater than a second preset threshold, determine whether the online booking room belongs to a preset high-frequency area. If so, obtain the door lock opening record corresponding to the online booking room during the occupant's stay. S3, judging whether the number of times the door is opened from the inside is not higher than a third preset threshold value according to the door lock opening record, and if so, obtaining video data of the outside door of the online booking room collected by the occupant during the stay; S4, determining whether the portrait data contained in the video data is greater than a fourth preset threshold, and if not, obtaining the number of human bodies detected by the human body number sensing device in the online booking room; S5. Determine whether the number of people exceeds the number of registered guests. If so, send an early warning message of abnormal behavior of multiple people gathering to the online booking room management terminal.

[0018] From the above description, it can be seen that the present invention has the following beneficial effects: The present invention provides a method for multi-person occupancy management and early warning based on online-booked houses. The method comprises the following steps: obtaining all occupancy information of the online-booked house, extracting occupancy information in which the number of registered occupants exceeds a first preset threshold from all occupancy information, and obtaining first occupancy information; obtaining historical occupancy records of occupants and calculating historical occupancy frequencies. When the calculated historical occupancy frequencies are greater than a second preset threshold, determining whether the online-booked house belongs to a preset high-frequency area. If so, obtaining a door lock opening record corresponding to the online-booked house during the occupant's stay; determining whether the number of door openings from the inside is not higher than a third preset threshold based on the door lock opening record. If so, obtaining video data at the outer door of the online-booked house collected by the occupant during the occupancy; determining whether the human portrait data contained in the video data is greater than a fourth preset threshold. If not, obtaining the number of human bodies detected by a human body number sensing device in the online-booked house; determining whether the number of human bodies exceeds the number of registered occupants. If so, sending an early warning message of abnormal behavior of multi-person gathering to the online-booked house management terminal. Through the above-mentioned specific method, multiple people gathering are first screened out based on the check-in information, and then a preliminary judgment is made based on the historical check-in frequency and whether the online booking house belongs to the preset high-frequency area. Further judgments are made in sequence based on the door lock opening record, the video data collected at the outer door of the online booking house, and the number of people detected by the human body sensing device in the online booking house. Finally, a conclusion of suspected abnormal behavior of multiple people gathering is drawn comprehensively, and abnormal behavior warning information of multiple people gathering is sent to the online booking house management end, thereby realizing abnormal behavior warning of multiple people gathering in the online booking house. The online booking house management end can subsequently report to the networked public security management platform to avoid adverse situations.

[0019] Furthermore, step S5 is specifically as follows: Determine whether the number of people exceeds the number of occupants. If so, obtain the number of networked wireless networks configured in the online booking room, and determine whether the number of networked networks exceeds the number of occupants. If so, send an abnormal behavior warning message of multiple people gathering to the online booking room management end.

[0020] From the above description, it can be seen that after detecting the number of people through the human body number sensing device in the online booking room and further combining it with the number of wireless networks connected to the room, a comprehensive conclusion can be drawn on suspected abnormal behavior of multiple people gathering.

[0021] Furthermore, step S5 is specifically as follows: Determine whether the number of human bodies exceeds the number of occupants. If so, obtain the number of networked wireless networks configured in the online booking room, and determine whether the number of networked networks exceeds the number of occupants. If so, obtain the number of power switches and power-on duration configured in the online booking room. When the number of power switches is less than the fifth preset threshold and the power-on duration is greater than the sixth preset threshold, send an abnormal behavior warning message of multiple people gathering to the online booking room management end.

[0022] From the above description, it can be seen that the number of people is detected by the human body sensing device in the online booking room, and then combined with the number of wireless networks connected to the room, the number of power switches configured in the room, and the length of time of power supply configured in the room, to further comprehensively determine the conclusion of suspected abnormal behavior of multiple people gathering, with higher accuracy.

[0023] Furthermore, the fifth preset threshold is set to 2 times, and the sixth preset threshold is set to 15 hours.

[0024] From the above description, it can be seen that the above parameter settings meet the actual implementation requirements.

[0025] Furthermore, the third preset threshold is set to zero; the first preset threshold and the fourth preset threshold are both 2 persons; and the second preset threshold is 2 times per week and 6 times per month.

[0026] From the above description, it can be seen that the above parameter settings meet the actual implementation requirements.

[0027] See also Figure 2 The present invention also provides a system for multi-occupancy management and early warning based on online booking rooms, including a processor 1 and a memory 2, wherein the memory 2 stores a program or instruction, and when the program or instruction is executed by the processor 1, the following steps are implemented: S1. Obtain all check-in information of the online booking room, extract the check-in information in which the number of registered check-ins exceeds a first preset threshold from all the check-in information, and obtain the first check-in information; S2. Obtain the historical check-in records of the occupant and calculate the historical check-in frequency. When the calculated historical check-in frequency is greater than a second preset threshold, determine whether the online booking room belongs to a preset high-frequency area. If so, obtain the door lock opening record corresponding to the online booking room during the occupant's stay. S3, judging whether the number of times the door is opened from the inside is not higher than a third preset threshold value according to the door lock opening record, and if so, obtaining video data of the outside door of the online booking room collected by the occupant during the stay; S4, determining whether the portrait data contained in the video data is greater than a fourth preset threshold, and if not, obtaining the number of human bodies detected by the human body number sensing device in the online booking room; S5. Determine whether the number of people exceeds the number of registered guests. If so, send an early warning message of abnormal behavior of multiple people gathering to the online booking room management terminal.

[0028] From the above description, it can be seen that the present invention has the following beneficial effects: The present invention provides a system for multi-occupancy management and early warning based on online-booked houses. The system obtains all occupancy information of the online-booked houses, extracts occupancy information in which the number of registered occupants exceeds a first preset threshold from all occupancy information, and obtains first occupancy information; obtains historical occupancy records of occupants and calculates historical occupancy frequencies. When the calculated historical occupancy frequencies are greater than a second preset threshold, it is determined whether the online-booked house belongs to a preset high-frequency area. If so, the door lock opening records corresponding to the online-booked house during the occupants' occupancy are obtained; based on the door lock opening records, it is determined whether the number of times the door is opened from the inside is not higher than a third preset threshold. If so, video data at the outer door of the online-booked house collected by the occupants during their occupancy is obtained; it is determined whether the portrait data contained in the video data is greater than a fourth preset threshold. If not, the number of human bodies detected by a human body number sensing device in the online-booked house is obtained; it is determined whether the number of human bodies exceeds the registered occupants. If so, an early warning message of abnormal behavior of multi-person gathering is sent to the online-booked house management terminal. Through the above-mentioned specific method, multiple people gathering are first screened out based on the check-in information, and then a preliminary judgment is made based on the historical check-in frequency and whether the online booking house belongs to the preset high-frequency area. Further judgments are made in sequence based on the door lock opening record, the video data collected at the outer door of the online booking house, and the number of people detected by the human body sensing device in the online booking house. Finally, a conclusion of suspected abnormal behavior of multiple people gathering is drawn comprehensively, and abnormal behavior warning information of multiple people gathering is sent to the online booking house management end, thereby realizing abnormal behavior warning of multiple people gathering in the online booking house. The online booking house management end can subsequently report to the networked public security management platform to avoid adverse situations.

[0029] Furthermore, when the program or instruction is executed by the processor, the following steps are specifically implemented: Determine whether the number of people exceeds the number of occupants. If so, obtain the number of networked wireless networks configured in the online booking room, and determine whether the number of networked networks exceeds the number of occupants. If so, send an abnormal behavior warning message of multiple people gathering to the online booking room management end.

[0030] From the above description, it can be seen that after detecting the number of people through the human body number sensing device in the online booking room and further combining it with the number of wireless networks connected to the room, a comprehensive conclusion can be drawn on suspected abnormal behavior of multiple people gathering.

[0031] Furthermore, when the program or instruction is executed by the processor, the following steps are specifically implemented: Determine whether the number of human bodies exceeds the number of occupants. If so, obtain the number of networked wireless networks configured in the online booking room, and determine whether the number of networked networks exceeds the number of occupants. If so, obtain the number of power switches and power-on duration configured in the online booking room. When the number of power switches is less than the fifth preset threshold and the power-on duration is greater than the sixth preset threshold, send an abnormal behavior warning message of multiple people gathering to the online booking room management end.

[0032] From the above description, it can be seen that the number of people is detected by the human body sensing device in the online booking room, and then combined with the number of wireless networks connected to the room, the number of power switches configured in the room, and the length of time of power supply configured in the room, to further comprehensively determine the conclusion of suspected abnormal behavior of multiple people gathering, with higher accuracy.

[0033] Furthermore, when the program or instruction is executed by the processor, the following steps are implemented: the fifth preset threshold is set to 2 times, and the sixth preset threshold is set to 15 hours.

[0034] From the above description, it can be seen that the above parameter settings meet the actual implementation requirements.

[0035] Furthermore, when the program or instruction is executed by the processor, the following steps are implemented: the third preset threshold is set to zero; the first preset threshold and the fourth preset threshold are both 2 people, and the second preset threshold is 2 times per week and 6 times per month.

[0036] From the above description, it can be seen that the above parameter settings meet the actual implementation requirements.

[0037] Several preferred embodiments or application examples are listed below to help those skilled in the art better understand the technical content of the present invention and the technical contribution made by the present invention relative to the prior art: Preferred embodiment one: See also Figure 1 The present invention provides a method for multi-occupancy management and early warning based on online booking rooms, comprising the following steps: S1. Obtain all check-in information of the online booking room, extract the check-in information in which the number of registered check-ins exceeds a first preset threshold from all the check-in information, and obtain the first check-in information; wherein the first preset threshold is 2 people; S2. Obtain the historical check-in records of the occupants and calculate the historical check-in frequency. When the calculated historical check-in frequency is greater than a second preset threshold, determine whether the online-booked house belongs to a preset high-frequency area. If so, obtain the door lock opening record corresponding to the online-booked house during the occupant's stay. Among them, the second preset threshold is 2 times per week and 6 times per month, and the above-mentioned preset high-frequency area is a high-frequency area where multiple people gather to commit crimes distributed by the public security management platform.

[0038] S3. judging whether the number of times the door is opened from the inside is not higher than a third preset threshold value according to the door lock opening record, and if so, obtaining video data of the outside door of the online booking room collected by the occupant during the stay; wherein the third preset threshold value is set to zero; S4. Determine whether the portrait data contained in the video data is greater than the fourth preset threshold. If not, obtain the number of people detected by the human body number sensing device in the online booking room; wherein the fourth preset threshold is 2 people; the judgment of the portrait data can be made using the existing portrait recognition technology. Through the recognition and judgment of the portrait data, it can be known whether anyone enters the room from outside the room during the stay. In addition, the human body number sensing device is an existing product and meets the legal installation requirements of the relevant products.

[0039] S5. Determine whether the number of people exceeds the number of registered guests. If so, send an early warning message of abnormal behavior of multiple people gathering to the online booking room management terminal.

[0040] In this embodiment, step S5 is specifically as follows: Determine whether the number of people exceeds the number of people staying in the room. If so, obtain the number of wireless network connections configured in the online booking room and determine whether the number of connections exceeds the number of people staying in the room. If so, send a warning message of abnormal behavior of multiple people gathering to the online booking room management end. After detecting the number of people through the human number sensing device in the online booking room and further combining it with the number of wireless network connections configured in the online booking room, a conclusion of suspected abnormal behavior of multiple people gathering can be comprehensively determined.

[0041] To further improve the determination accuracy, step S5 is specifically as follows: Determine whether the number of people exceeds the number of people staying in the room. If so, obtain the number of connected wireless networks configured in the online booking room. Determine whether the number of connected networks exceeds the number of people staying in the room. If so, obtain the number of power switches configured in the online booking room and the length of power supply. When the number of power switches is less than the fifth preset threshold and the length of power supply is greater than the sixth preset threshold, send an early warning message of abnormal behavior of multiple people gathering to the online booking room management terminal. Among them, the fifth preset threshold is set to 2 times, and the sixth preset threshold is set to 15 hours. The number of people is detected by the human body number sensing device in the online booking room, and then combined with the number of connected wireless networks configured in the online booking room, the number of power switches configured in the online booking room, and the length of power supply, to further comprehensively determine the conclusion of suspected abnormal behavior of multiple people gathering, with higher accuracy.

[0042] In this embodiment, step S0 is included before step S1, and step S0 includes step S01 and step S02, which are specifically as follows: S01. Obtain an online booking order generated by a user online, obtain the model information of the mobile terminal held by the user according to the online booking order, obtain the corresponding near field communication parameters according to the model information of the mobile terminal held by the user, obtain the online booking information of the smart door lock information paired with it according to the near field communication parameters, and complete the online room booking operation; In this embodiment, step S01 is specifically as follows: Obtain an online booking order generated by the user, wherein the online booking order can be obtained from a third-party network platform. Obtain the model information of the mobile terminal held by the user according to the online booking order, and obtain the corresponding near-field communication parameters according to the model information of the mobile terminal held by the user; in the actual process, if the user uses a mobile terminal to place an order online, the system can automatically obtain the model information of the mobile terminal; if the user uses a PC to place an order online, the model information can be filled in by the user.

[0043] Obtain a first house source list, the first house source list consisting of information of online-booked houses that are currently available for check-in, and obtain corresponding configured smart door lock information according to the online-booked house information in the first house source list; Matching the near field communication parameters with the smart door lock information one by one, obtaining the online booking house information corresponding to the successfully matched smart door lock information and generating a corresponding second house source list; Sending the second housing source list to the user's mobile terminal; If the online booking information selected from the second housing source list is received and sent by the user's mobile terminal within the preset time range, the selected online booking information is bound to the user's mobile terminal to complete the online room booking operation.

[0044] Furthermore, step S01 further includes: If the information of the booker contained in the online booking order is consistent with the information of the occupant, the model information of the mobile terminal held by the user is obtained according to the online booking order, and the corresponding near field communication parameters are obtained according to the model information of the mobile terminal held by the user; If the booker information and the occupant information contained in the online booking order are inconsistent, the historical order information corresponding to the booker information is obtained, and the historical order information is checked to see whether the occupant information is contained. If so, the model information of the mobile terminal held by the occupant information is obtained according to the historical order information; if not, the historical check-in information corresponding to the booker information is obtained, and the historical check-in information is checked to see whether the occupant information is contained. If so, the model information of the mobile terminal held by the occupant information is obtained according to the historical check-in information; if not, the near-field communication parameters of the occupant information are set to the near-field communication parameters of the corresponding lowest version of the smart door lock information.

[0045] Through the above methods, it is possible to first determine whether the person staying in the room is the same as the person who booked the room. If they are the same, the near-field communication parameters corresponding to the model information of the mobile terminal held by the user are obtained, and subsequent matching operations are performed, thereby improving the effectiveness of data processing. When the information of the booker included in the online booking order is inconsistent with the information of the person staying in the room, the acquisition of the near-field communication parameters can be completed through the above three methods, thereby realizing intelligent operation.

[0046] Specifically, there are three categories: 1. The situation where an order has been placed but the guest has not checked in; 2. The situation where an order has not been placed but the guest has checked in; 3. The situation where the guest has neither placed an order nor checked in. The three methods mentioned above are used to obtain the near-field communication parameters, thereby realizing intelligent operation without further error correction and confirmation with the user, saving intermediate links and improving system processing efficiency. It should be noted that this solution is based on the current situation where all users have mobile terminals. If there is a situation where the user does not have a mobile terminal, this solution is not applicable.

[0047] Step S01 also includes: The near-field communication parameters are matched with the smart door lock information one by one, and the smart door lock information with a version lower than the near-field communication parameters is extracted from all the smart door lock information and used as the successfully matched smart door lock information, and the online booking information corresponding to the successfully matched smart door lock information is used to generate a corresponding second house source list. Among them, the online booking information includes the supporting information of the online booking room, such as room type information, supporting facilities, etc., among which supporting facilities mainly include smart door lock information, such as door locks that support multiple near-field communications such as Bluetooth door locks and NFC door locks, and of course there are also smart door locks used in conjunction with gateways.

[0048] In the above manner, the pairing operation of the near-field communication parameters and the intelligent door lock information is completed, and the intelligent door lock information with a version lower than the near-field communication parameters among all the intelligent door lock information is extracted and used as the successfully paired intelligent door lock information, thereby ensuring that the version of the user's mobile terminal is higher than that of the intelligent door lock, and further ensuring that the unlocking operation can be completed.

[0049] Step S01 is specifically as follows: Comprehensively score the online booking house information in the second housing source list, sort it from high to low according to the score of the comprehensive score, and extract a preset number of online booking house information from high to low according to the score of the comprehensive score to form a new second housing source list and send it to the user's mobile terminal; Among them, the preset number is 5-10, preferably 5, which can improve the user experience. Of course, it can also be combined with the user's personal habits. For example, if it is analyzed through big data that the user is usually more hesitant when making choices, the preset number can be dynamically set to be smaller, such as 5, to avoid causing difficulties for the user to make choices; if it is analyzed that the user is usually more decisive when making choices, the preset number can be dynamically set to be larger, such as 10, for the user to choose. In this way, the user experience can be further improved.

[0050] The above comprehensive score is calculated based on the supporting information and historical evaluation information of the online booking house. The supporting information, such as the room type information, facility configuration, etc., can specifically set the corresponding scores and weights; the historical evaluation information, such as the scores and weights obtained from the evaluation information after staying in the online booking house for three months, half a year or one year, is used to calculate the comprehensive score. Specifically: each score is multiplied by the corresponding weight and then accumulated to calculate the comprehensive score. Of course, other calculation methods can also be used to obtain this comprehensive score.

[0051] If the online booking house information selected from the new second housing source list sent by the user's mobile terminal is received within the preset time range, the selected online booking house information is bound to the user's mobile terminal to complete the room arrangement operation. Among them, the preset time is generally set to 1-10 minutes, and it can also be dynamically set according to the user's personal habits as described above, and will not be elaborated here.

[0052] Through the above method, the data in the second housing source list can be further optimized, not only reducing unnecessary data transmission and improving transmission efficiency, but also being able to perform more reasonable screening for users, facilitating users to select more satisfactory online booking houses, and further improving the user experience.

[0053] S02. Perform offline real-name check-in registration according to the online room arrangement result to generate check-in information.

[0054] Preferred Embodiment Two: Refer to Figure 2, the present invention also provides a system for managing multiple-person check-in and early warning based on online reservation rooms, including a processor 1 and a memory 2. A program or instruction is stored on the memory 2, and when the program or instruction is executed by the processor 1, the following steps are implemented: S1. Obtain all check-in information of the online reservation room, extract the check-in information with the number of check-in registrations exceeding the first preset threshold from all the check-in information, and obtain the first check-in information; S2. Obtain the historical check-in records of the check-in person and calculate the historical check-in frequency. When the calculated historical check-in frequency is greater than the second preset threshold, determine whether the online reservation room belongs to a preset high-frequency area. If so, obtain the door lock opening records corresponding to the online reservation room during the check-in period of the check-in person; S3. According to the door lock opening records, determine whether the number of times of opening the door from the inside is not higher than the third preset threshold. If so, obtain the video data at the outside door of the online reservation room collected during the check-in period of the check-in person; S4. Determine whether the number of portrait data in the video data is greater than the fourth preset threshold. If not, obtain the number of people detected by the human body quantity sensing device in the online reservation room; S5. Determine whether the number of people exceeds the number of check-in registrations. If so, send a warning message for abnormal behavior of multiple-person gathering to the management terminal of the online reservation room.

[0055] Further, when the program or instruction is executed by the processor, the following steps are specifically implemented: Determine whether the number of people exceeds the number of check-ins. If so, obtain the number of devices connected to the wireless network configured in the online reservation room, and determine whether the number of connected devices exceeds the number of check-ins. If so, send a warning message for abnormal behavior of multiple-person gathering to the management terminal of the online reservation room.

[0056] Further, when the program or instruction is executed by the processor, the following steps are specifically implemented: Determine whether the number of people exceeds the number of check-ins. If so, obtain the number of times the power-taking switch is turned on and the power-taking duration in the online reservation room. When the number of times the power-taking switch is turned on is less than the fifth preset threshold and the power-taking duration is greater than the sixth preset threshold, send a warning message for abnormal behavior of multiple-person gathering to the management terminal of the online reservation room.

[0057] Further, when the program or instruction is executed by the processor, the following steps are implemented: the fifth preset threshold is set to 2 times, and the sixth preset threshold is set to 15 hours.

[0058] Further, when the program or instruction is executed by the processor, the following steps are implemented: the third preset threshold is set to zero; both the first preset threshold and the fourth preset threshold are 2 persons, and the second preset threshold is 2 times per week and 6 times per month.

[0059] The present invention has been described by the above related embodiments and drawings. However, the above embodiments are only examples for implementing the present invention. It must be noted that the disclosed embodiments do not limit the scope of the present invention. On the contrary, modifications and equivalent arrangements included in the spirit and scope of the claims are included in the scope of the present invention.

Claims

1. A method for multi-occupancy management and early warning based on online booking rooms, characterized in that: The following steps are involved: S1. Obtain all check-in information of the online booking room, extract the check-in information in which the number of registered check-ins exceeds a first preset threshold from all the check-in information, and obtain the first check-in information; S2. Obtain the historical check-in records of the occupant and calculate the historical check-in frequency. When the calculated historical check-in frequency is greater than a second preset threshold, determine whether the online booking room belongs to a preset high-frequency area. If so, obtain the door lock opening record corresponding to the online booking room during the occupant's stay. S3, judging whether the number of times the door is opened from the inside is not higher than a third preset threshold value according to the door lock opening record, and if so, obtaining video data of the outside door of the online booking room collected by the occupant during the stay; S4, determining whether the portrait data contained in the video data is greater than a fourth preset threshold, and if not, obtaining the number of human bodies detected by the human body number sensing device in the online booking room; S5. Determine whether the number of people exceeds the number of registered guests. If so, send an early warning message of abnormal behavior of multiple people gathering to the online booking room management terminal.

2. A method for multi-occupancy management and early warning based on online booking rooms according to claim 1, characterized in that: Step S5 is specifically as follows: Determine whether the number of people exceeds the number of occupants. If so, obtain the number of networked wireless networks configured in the online booking room, and determine whether the number of networked networks exceeds the number of occupants. If so, send an abnormal behavior warning message of multiple people gathering to the online booking room management end.

3. A method for multi-occupancy management and early warning based on online booking rooms according to claim 2, characterized in that: Step S5 is specifically as follows: Determine whether the number of human bodies exceeds the number of occupants. If so, obtain the number of networked wireless networks configured in the online booking room, and determine whether the number of networked networks exceeds the number of occupants. If so, obtain the number of power switches and power-on duration configured in the online booking room. When the number of power switches is less than the fifth preset threshold and the power-on duration is greater than the sixth preset threshold, send an abnormal behavior warning message of multiple people gathering to the online booking room management end.

4. A method for multi-occupancy management and early warning based on online booking rooms according to claim 3, characterized in that: The fifth preset threshold is set to 2 times, and the sixth preset threshold is set to 15 hours.

5. The method for multi-occupancy management and early warning based on online booking rooms according to claim 1 is characterized in that: The third preset threshold is set to zero; the first preset threshold and the fourth preset threshold are both 2 persons; and the second preset threshold is 2 times per week and 6 times per month.

6. A system for multi-occupancy management and early warning based on online booking rooms, characterized in that: The system comprises a processor and a memory, wherein the memory stores a program or an instruction, and when the program or the instruction is executed by the processor, the following steps are implemented: S1. Obtain all check-in information of the online booking room, extract the check-in information in which the number of registered check-ins exceeds a first preset threshold from all the check-in information, and obtain the first check-in information; S2. Obtain the historical check-in records of the occupant and calculate the historical check-in frequency. When the calculated historical check-in frequency is greater than a second preset threshold, determine whether the online booking room belongs to a preset high-frequency area. If so, obtain the door lock opening record corresponding to the online booking room during the occupant's stay. S3, judging whether the number of times the door is opened from the inside is not higher than a third preset threshold value according to the door lock opening record, and if so, obtaining video data of the outside door of the online booking room collected by the occupant during the stay; S4, determining whether the portrait data contained in the video data is greater than a fourth preset threshold, and if not, obtaining the number of human bodies detected by the human body number sensing device in the online booking room; S5. Determine whether the number of people exceeds the number of registered guests. If so, send an early warning message of abnormal behavior of multiple people gathering to the online booking room management terminal.

7. A system for multi-occupancy management and early warning based on online booking rooms according to claim 6, characterized in that: When the program or instruction is executed by the processor, the following steps are specifically implemented: Determine whether the number of people exceeds the number of occupants. If so, obtain the number of networked wireless networks configured in the online booking room, and determine whether the number of networked networks exceeds the number of occupants. If so, send an abnormal behavior warning message of multiple people gathering to the online booking room management end.

8. The system for multi-occupancy management and early warning based on online booking rooms according to claim 7 is characterized in that: When the program or instruction is executed by the processor, the following steps are specifically implemented: Determine whether the number of human bodies exceeds the number of occupants. If so, obtain the number of networked wireless networks configured in the online booking room, and determine whether the number of networked networks exceeds the number of occupants. If so, obtain the number of power switches and power-on duration configured in the online booking room. When the number of power switches is less than the fifth preset threshold and the power-on duration is greater than the sixth preset threshold, send an abnormal behavior warning message of multiple people gathering to the online booking room management end.

9. A system for multi-occupancy management and early warning based on online booking rooms according to claim 8, characterized in that: When the program or instruction is executed by the processor, the following steps are implemented: the fifth preset threshold is set to 2 times, and the sixth preset threshold is set to 15 hours.

10. The system for multi-occupancy management and early warning based on online booking rooms according to claim 6 is characterized in that: When the program or instruction is executed by the processor, the following steps are implemented: the third preset threshold is set to zero; the first preset threshold and the fourth preset threshold are both 2 people, and the second preset threshold is 2 times per week and 6 times per month.

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