Internet-based intelligent check-in method and system for accommodation industry

Abstract

The invention relates to the technical field of accommodation industry housing management, in particular to an internet-based intelligent check-in method and system for accommodation industry. In the check-in process, an unlocking request sent by a mobile terminal held by a user is received, whether positioning information of the mobile terminal is in a preset area range of the online booked house corresponding to the intelligent door lock information matched with the mobile terminal or not is judged, and if yes, check-in is completed; if yes, acquiring face data, matching the acquired face data with face data uploaded during registration, judging whether matching succeeds or not, if yes, acquiring video data, corresponding to the matched intelligent door lock information, at the doorway of the outer side of the online booked house, judging whether portrait data contained in the video data is equal to the number of check-in persons during registration or not, and if yes, executing the step 3; if yes, the intelligent door lock of the online booked house is opened, check-in operation is completed, check-in information is generated, a judgment method combining on-site, real person and real number is achieved, and therefore the effectiveness of intelligent check-in management is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of housing management in the accommodation industry, and in particular to an intelligent check-in method and system for the accommodation industry based on the Internet. Background Art

[0002] In recent years, online rental housing, a new model within the accommodation industry and a part of the internet sharing economy, has quietly taken off across China. Landlords register their unused properties as online listings through online booking platforms. Tenants select a room type and make electronic payments online, completing their order. They then unlock the smart door lock using their mobile device's near-field communication device, allowing them to move in with just their luggage. Online rental housing, primarily offering short-term online rentals, meets the needs of young people for contactless, cost-effective, and personalized accommodations. As a result, it has become a preferred choice for travel accommodations for an increasing number of people.

[0003] However, due to the inconsistent and diverse configurations of smart door locks in online-booked accommodations, and the varying configurations of near-field communication devices on guests' mobile devices, mismatches between the NFC device configuration and the smart door lock can result in problems unlocking the door, impacting the guest's stay experience. Furthermore, there are deficiencies in the management of online-booked accommodations. Therefore, an internet-based intelligent check-in method and system for the accommodation industry is needed to address these technical issues. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide an Internet-based intelligent check-in method and system for the accommodation industry to solve the above technical problems.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is: An internet-based intelligent check-in method for accommodation industry comprises the following steps: S1. Obtain an online room booking order generated by a user, obtain the model information of the user's mobile terminal based on the online room booking order, obtain corresponding near-field communication parameters based on the model information of the user's mobile terminal, obtain the online room booking information of the smart door lock information paired with the near-field communication parameters, and complete the online room booking operation; S2. After offline real-name check-in registration based on the online room reservation results, check-in information is generated; specifically, it includes: S21, receiving an unlocking request from a mobile terminal held by a user, determining whether the positioning information of the mobile terminal is within the preset area of the online booking room corresponding to the paired smart door lock information, and if so, proceeding to step S22; S22, obtaining facial data and matching the obtained facial data with the facial data uploaded during registration to determine whether the match is successful. If so, proceed to step S23; S23. Obtain video data at the outer door of the online booking room corresponding to the paired smart door lock information, and determine whether the portrait data contained in the video data is equal to the number of people staying at the time of registration. If so, the smart door lock of the online booking room is opened, the check-in operation is completed, and check-in information is generated.

[0006] Furthermore, step S23 is specifically as follows: Obtain video data from the outside door of the online booking room corresponding to the paired smart door lock information, and determine whether the portrait data contained in the video data is equal to the number of people staying in the room at the time of registration. If so, the smart door lock of the online booking room is unlocked; After a preset time interval, the number of people detected by the human number sensing device in the online booking room is obtained, and it is determined whether the number of people is equal to the number of people staying at the time of registration. If so, the check-in operation is completed and the check-in information is generated.

[0007] Furthermore, step S2 further includes step S3, which specifically includes: S31. According to the check-in information of the online-booked room, obtain the check-in information of the female occupant from all the check-in information of the online-booked room, and obtain the first check-in information; S32. Obtain historical check-in records of the occupant in the first check-in information and calculate the historical check-in frequencies. When the calculated historical check-in frequencies are greater than a first preset threshold, determine whether the online-booked room belongs to a preset high-frequency area. If so, obtain the door lock opening records corresponding to the online-booked room during the occupant's stay. S33. Determine, based on the door lock opening record, whether the number of times the door is opened from inside the online booking room within a preset time range exceeds a second preset threshold; if so, obtain video data of the exterior door of the online booking room collected by the occupant during their stay; S34. Determine whether the portrait data contained in the video data is greater than the number of people staying in the room at the time of registration. If so, obtain the number of people detected by the human number sensing device in the room; S35. Determine whether the number of people is greater than the number of people registered. If so, send an abnormal check-in warning message to the online room management terminal.

[0008] Furthermore, step S35 is specifically as follows: Determine whether the number of people is greater than the number of people staying in the room at the time of registration. If so, obtain the number of networked wireless networks configured in the online-booked room. Determine whether the number of networked networks is greater than the number of people staying in the room at the time of registration. If so, obtain the number of power switches configured in the online-booked room and the power-on duration. When the number of power switches is less than the third preset threshold and the power-on duration is greater than the fourth preset threshold, send an abnormal occupancy warning message to the online-booked room management terminal.

[0009] Furthermore, step S2 further includes step S4, which specifically includes: S41. Obtain all check-in information of the online-booked accommodation, extract the check-in information in which the number of registered guests exceeds a fifth preset threshold from all the check-in information, and obtain first check-in information; S42. Obtain the occupant's historical check-in records and calculate the historical check-in frequency. When the calculated historical check-in frequency is greater than a sixth preset threshold, determine whether the online-booked room belongs to a preset high-frequency area. If so, obtain the door lock opening record corresponding to the online-booked room during the occupant's stay. S43. Determine whether the number of times the door is opened from the inside is not higher than a seventh preset threshold based on the door lock opening record. If so, obtain video data of the outside door of the online booking room collected by the occupant during the occupant's stay. S44. Determine whether the number of portrait data contained in the video data is greater than an eighth preset threshold. If not, obtain the number of people detected by the human number sensing device in the online booking room. S45. 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 room management terminal.

[0010] Furthermore, step S45 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, obtain the number of power switches configured in the online booking room and the power-on duration. When the number of power switches is less than the ninth preset threshold and the power-on duration is greater than the tenth preset threshold, send an abnormal behavior warning message of multiple people gathering to the online booking room management end.

[0011] Furthermore, step S2 further includes step S5, which specifically includes: S51. Obtain all check-in information of the online-booked accommodation, extract the check-in information of the minors from all the check-in information, and obtain the first check-in information; S52: Determine whether the check-in duration corresponding to the first check-in information reaches a preset duration and there is no record of other people checking in during the check-in period. If so, obtain the door lock opening record corresponding to the online-booked room during the check-in period; S53. Determine whether the number of times the door is opened from the inside is not higher than a preset value based on the door lock opening record. If so, obtain video data of the outside door of the online booking room collected by the occupant during the occupant's stay. S54: Determine whether the video data contains human portrait data. If not, obtain the number of people detected by the human number sensing device in the online booking room; S55. Determine whether the number of people exceeds the number of occupants. If so, send a warning message about minors living in cramped conditions to the online room management terminal.

[0012] Furthermore, step S55 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-booked room. Determine whether the number of networked networks exceeds the number of occupants. If so, obtain the number of power switches configured in the online-booked room and the length of power supply time. When the number of power switches is less than the eleventh preset threshold and the length of power supply time is greater than the twelfth preset threshold, send a warning message about minors living in cramped conditions to the online-booked room management end.

[0013] Furthermore, step S51 is specifically as follows: All check-in information of online-booked houses is obtained, check-in information of minors is extracted from all check-in information, and check-in information of minors with historical cramped living behavior data is extracted based on historical cramped living behavior data in a preset database to obtain first check-in information; the preset database is used to store information of minors with historical cramped living behavior data.

[0014] The present invention also provides an Internet-based intelligent check-in system for the accommodation industry, 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 an online room booking order generated by a user, obtain the model information of the user's mobile terminal based on the online room booking order, obtain corresponding near-field communication parameters based on the model information of the user's mobile terminal, obtain the online room booking information of the smart door lock information paired with the near-field communication parameters, and complete the online room booking operation; S2. After offline real-name check-in registration based on the online room reservation results, check-in information is generated; specifically, it includes: S21, receiving an unlocking request from a mobile terminal held by a user, determining whether the positioning information of the mobile terminal is within the preset area of the online booking room corresponding to the paired smart door lock information, and if so, proceeding to step S22; S22, obtaining facial data and matching the obtained facial data with the facial data uploaded during registration to determine whether the match is successful. If so, proceed to step S23; S23. Obtain video data at the outer door of the online booking room corresponding to the paired smart door lock information, and determine whether the portrait data contained in the video data is equal to the number of people staying at the time of registration. If so, the smart door lock of the online booking room is opened, the check-in operation is completed, and check-in information is generated.

[0015] The beneficial effects of the present invention are: The present invention provides an Internet-based intelligent check-in method and system for the accommodation industry. The method obtains an online room booking order generated by a user online, obtains the model information of the user's mobile terminal based on the online room booking order, obtains the corresponding near-field communication parameters based on the model information of the user's mobile terminal, obtains the online room booking information of the smart door lock information paired with it based on the near-field communication parameters, and completes the online room booking operation; performs offline real-name check-in registration based on the online room booking result to generate check-in information; during the check-in process, determines the positioning information of the mobile terminal by receiving an unlocking request sent by the user's mobile terminal Whether it is within the preset area of the online booking room corresponding to the paired smart door lock information; if so, obtain facial data and match the acquired facial data with the facial data uploaded during registration to determine whether the match is successful; if so, obtain video data at the outer door of the online booking room corresponding to the paired smart door lock information, and determine whether the portrait data contained in the video data is equal to the number of guests at the time of registration; if so, the smart door lock of the online booking room is opened, the check-in operation is completed and the check-in information is generated, realizing a judgment method that combines "real site", "real person" and "real number", thereby improving the effectiveness of intelligent check-in management. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 Shown is a flowchart of the steps of an Internet-based intelligent check-in method for accommodation industry of the present invention; Figure 2 Shown is a structural block diagram of an Internet-based intelligent check-in system for accommodation industry according to the present invention; Description of Figure Numbers: 1-Processor; 2-Memory. DETAILED DESCRIPTION

[0017] The present invention will be further described below with reference to the accompanying drawings and specific embodiments: like Figure 1 As shown, the present invention provides an Internet-based intelligent check-in method for accommodation industry, comprising the following steps: S1. Obtain an online room booking order generated by a user, obtain the model information of the user's mobile terminal based on the online room booking order, obtain corresponding near-field communication parameters based on the model information of the user's mobile terminal, obtain the online room booking information of the smart door lock information paired with the near-field communication parameters, and complete the online room booking operation; S2. After offline real-name check-in registration based on the online room reservation results, check-in information is generated; specifically, it includes: S21, receiving an unlocking request from a mobile terminal held by a user, determining whether the positioning information of the mobile terminal is within the preset area of the online booking room corresponding to the paired smart door lock information, and if so, proceeding to step S22; S22, obtaining facial data and matching the obtained facial data with the facial data uploaded during registration to determine whether the match is successful. If so, proceed to step S23; S23. Obtain video data at the outer door of the online booking room corresponding to the paired smart door lock information, and determine whether the portrait data contained in the video data is equal to the number of people staying at the time of registration. If so, the smart door lock of the online booking room is opened, the check-in operation is completed, and check-in information is generated.

[0018] The beneficial effects of the present invention are: The present invention provides an intelligent check-in method for the accommodation industry based on the Internet. The method obtains an online room booking order generated by a user online, obtains the model information of the mobile terminal held by the user according to the online room booking order, obtains the corresponding near-field communication parameters according to the model information of the mobile terminal held by the user, obtains the online room booking information of the smart door lock information matched with it according to the near-field communication parameters, and completes the online room booking operation; performs offline real-name check-in registration according to the online room booking result, and generates check-in information; during the check-in process, determines whether the positioning information of the mobile terminal is correct by receiving an unlocking request sent by the mobile terminal held by the user. If the smart door lock information paired with it is within the preset area of the online booking room corresponding to the paired smart door lock information, facial data is obtained and matched with the facial data uploaded during registration to determine whether the match is successful. If so, video data of the outer door of the online booking room corresponding to the paired smart door lock information is obtained to determine whether the portrait data contained in the video data is equal to the number of guests at the time of registration. If so, the smart door lock of the online booking room is opened, the check-in operation is completed and check-in information is generated, realizing a judgment method that combines "real site", "real person" and "real number", thereby improving the effectiveness of intelligent check-in management.

[0019] Furthermore, step S23 is specifically as follows: Obtain video data from the outside door of the online booking room corresponding to the paired smart door lock information, and determine whether the portrait data contained in the video data is equal to the number of people staying in the room at the time of registration. If so, the smart door lock of the online booking room is unlocked; After a preset time interval, the number of people detected by the human number sensing device in the online booking room is obtained, and it is determined whether the number of people is equal to the number of people staying at the time of registration. If so, the check-in operation is completed and the check-in information is generated.

[0020] From the above description, it can be seen that the method of determining "real number" is to combine video data and human body number sensing equipment, and the two determine to improve the determination accuracy.

[0021] Furthermore, step S2 further includes step S3, which specifically includes: S31. According to the check-in information of the online-booked room, obtain the check-in information of the female occupant from all the check-in information of the online-booked room, and obtain the first check-in information; S32. Obtain historical check-in records of the occupant in the first check-in information and calculate the historical check-in frequencies. When the calculated historical check-in frequencies are greater than a first preset threshold, determine whether the online-booked room belongs to a preset high-frequency area. If so, obtain the door lock opening records corresponding to the online-booked room during the occupant's stay. S33. Determine, based on the door lock opening record, whether the number of times the door is opened from inside the online booking room within a preset time range exceeds a second preset threshold; if so, obtain video data of the exterior door of the online booking room collected by the occupant during their stay; S34. Determine whether the portrait data contained in the video data is greater than the number of people staying in the room at the time of registration. If so, obtain the number of people detected by the human number sensing device in the room; S35. Determine whether the number of people is greater than the number of people registered. If so, send an abnormal check-in warning message to the online room management terminal.

[0022] From the above description, it can be seen that by first filtering out the check-in information of female occupants, and then calculating the historical check-in frequency and whether the online booking room belongs to the preset high-frequency area as the basis for whether to obtain the door lock opening record. If it meets the requirements, the door lock opening record is then used to determine whether the number of times the door is opened from inside the online booking room within the preset time range exceeds the second preset threshold. Once the second preset threshold is exceeded, the portrait data in the video data collected at the outer door of the online booking room and the number of human bodies detected by the human body number sensing device in the online booking room are further combined to make judgments in sequence. Finally, a conclusion of suspected abnormal check-in is drawn and an abnormal check-in warning information is sent to the online booking room management end. The online booking room management end can then report to the networked security management platform to avoid adverse situations.

[0023] Furthermore, step S35 is specifically as follows: Determine whether the number of people is greater than the number of people staying in the room at the time of registration. If so, obtain the number of networked wireless networks configured in the online-booked room. Determine whether the number of networked networks is greater than the number of people staying in the room at the time of registration. If so, obtain the number of power switches configured in the online-booked room and the power-on duration. When the number of power switches is less than the third preset threshold and the power-on duration is greater than the fourth preset threshold, send an abnormal occupancy warning message to the online-booked room management terminal.

[0024] From the above description, it can be seen that the number of people in the online booking room is detected by the human body sensing equipment in the online booking room, and then combined with the number of wireless network connections configured in the online booking room, the number of power switches configured in the online booking room, and the length of power supply, to comprehensively determine the conclusion of suspected abnormal check-in, which is more accurate.

[0025] Furthermore, the third preset threshold is set to 2 times, and the fourth preset threshold is set to 2 hours. The first preset threshold is set to 2 times per week and 6 times per month, the preset time range is set to 10 pm to 1 am the next day, and the second preset threshold is set to no less than 5 times within the preset time range or no less than 2 times within a two-hour period.

[0026] Furthermore, step S2 further includes step S4, which specifically includes: S41. Obtain all check-in information of the online-booked accommodation, extract the check-in information in which the number of registered guests exceeds a fifth preset threshold from all the check-in information, and obtain first check-in information; S42. Obtain the occupant's historical check-in records and calculate the historical check-in frequency. When the calculated historical check-in frequency is greater than a sixth preset threshold, determine whether the online-booked room belongs to a preset high-frequency area. If so, obtain the door lock opening record corresponding to the online-booked room during the occupant's stay. S43. Determine whether the number of times the door is opened from the inside is not higher than a seventh preset threshold based on the door lock opening record. If so, obtain video data of the outside door of the online booking room collected by the occupant during the occupant's stay. S44. Determine whether the number of portrait data contained in the video data is greater than an eighth preset threshold. If not, obtain the number of people detected by the human number sensing device in the online booking room. S45. 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 room management terminal.

[0027] From the above description, it can be seen that through the above specific method, the check-in information is first used to screen out large gatherings of people, and then a preliminary judgment is made based on the historical check-in frequency of the occupants and whether the online booking room belongs to the preset high-frequency area. Then, the door lock opening record, the video data collected at the outer door of the online booking room, and the number of people detected by the human body sensing equipment in the online booking room are further combined to make judgments in sequence. Finally, a conclusion is drawn that there is suspected abnormal behavior of large gatherings of people, and by sending an early warning information on abnormal behavior of large gatherings of people to the online booking room management end, an early warning of abnormal behavior of large gatherings of people in the online booking room is realized. The online booking room management end can subsequently report to the networked public security management platform to avoid adverse situations.

[0028] Furthermore, step S45 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, obtain the number of power switches configured in the online booking room and the power-on duration. When the number of power switches is less than the ninth preset threshold and the power-on duration is greater than the tenth preset threshold, send an abnormal behavior warning message of multiple people gathering to the online booking room management end.

[0029] From the above description, it can be seen that the number of people is detected by the human body sensing equipment in the online booking room, and then combined with the number of wireless network connections 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.

[0030] Furthermore, the ninth preset threshold is set to 2 times, the tenth preset threshold is set to 15 hours, the seventh preset threshold is set to zero, the fifth preset threshold and the eighth preset threshold are both 2 people, and the sixth preset threshold is 2 times per week and 6 times per month.

[0031] Furthermore, step S2 further includes step S5, which specifically includes: S51. Obtain all check-in information of the online-booked accommodation, extract the check-in information of the minors from all the check-in information, and obtain the first check-in information; S52: Determine whether the check-in duration corresponding to the first check-in information reaches a preset duration and there is no record of other people checking in during the check-in period. If so, obtain the door lock opening record corresponding to the online-booked room during the check-in period; S53. Determine whether the number of times the door is opened from the inside is not higher than a preset value based on the door lock opening record. If so, obtain video data of the outside door of the online booking room collected by the occupant during the occupant's stay. S54: Determine whether the video data contains human portrait data. If not, obtain the number of people detected by the human number sensing device in the online booking room; S55. Determine whether the number of people exceeds the number of occupants. If so, send a warning message about minors living in cramped conditions to the online room management terminal.

[0032] From the above description, it can be seen that through the above specific method, minors are first screened out by the check-in information, and then a preliminary judgment is made whether there is a situation of cramped living based on the length of their stay and whether there are other people's check-in records. Then, the door lock opening record, the video data collected at the outer door of the online rental house, and the number of people detected by the human body sensing equipment in the online rental house are further combined to make judgments in sequence. Finally, a comprehensive conclusion is drawn that minors are suspected of living in cramped living, and an early warning message is sent to the online rental house management end, thereby realizing an early warning of cramped living of minors in the online rental house. The online rental house management end can subsequently report to the networked public security management platform, and the public security management platform will notify their guardians to avoid adverse situations.

[0033] Furthermore, step S55 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-booked room. Determine whether the number of networked networks exceeds the number of occupants. If so, obtain the number of power switches configured in the online-booked room and the length of power supply time. When the number of power switches is less than the eleventh preset threshold and the length of power supply time is greater than the twelfth preset threshold, send a warning message about minors living in cramped conditions to the online-booked room management end.

[0034] From the above description, it can be seen that the number of people in the online booking room is detected by the human body sensing equipment in the room, and then combined with the number of wireless network connections configured in the room, the number of power switches configured in the room, and the length of time of power supply, to comprehensively determine the conclusion that suspected minors are living in a cramped environment, with higher accuracy.

[0035] Furthermore, step S51 is specifically as follows: All check-in information of online-booked houses is obtained, check-in information of minors is extracted from all check-in information, and check-in information of minors with historical cramped living behavior data is extracted based on historical cramped living behavior data in a preset database to obtain first check-in information; the preset database is used to store information of minors with historical cramped living behavior data.

[0036] From the above description, it can be seen that when the check-in information of a minor is extracted, it is first determined through the preset database whether the minor has a history of cramped living behavior. If so, special attention is paid to the minor, as there is a high probability that the minor has engaged in cramped living behavior.

[0037] Furthermore, the eleventh preset threshold is set to 2 times, the twelfth preset threshold is set to 11 hours, and the preset value in step S53 is set to zero.

[0038] See Figure 2 The present invention also provides an Internet-based intelligent check-in system for the accommodation industry, comprising 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 an online room booking order generated by a user, obtain the model information of the user's mobile terminal based on the online room booking order, obtain corresponding near-field communication parameters based on the model information of the user's mobile terminal, obtain the online room booking information of the smart door lock information paired with the near-field communication parameters, and complete the online room booking operation; S2. After offline real-name check-in registration based on the online room reservation results, check-in information is generated; specifically, it includes: S21, receiving an unlocking request from a mobile terminal held by a user, determining whether the positioning information of the mobile terminal is within the preset area of the online booking room corresponding to the paired smart door lock information, and if so, proceeding to step S22; S22, obtaining facial data and matching the obtained facial data with the facial data uploaded during registration to determine whether the match is successful. If so, proceed to step S23; S23. Obtain video data at the outer door of the online booking room corresponding to the paired smart door lock information, and determine whether the portrait data contained in the video data is equal to the number of people staying at the time of registration. If so, the smart door lock of the online booking room is opened, the check-in operation is completed, and check-in information is generated.

[0039] The beneficial effects of the present invention are: The present invention provides an Internet-based intelligent check-in system for the accommodation industry. The system obtains an online room booking order generated by a user online, obtains the model information of the user's mobile terminal based on the online room booking order, obtains the corresponding near-field communication parameters based on the model information of the user's mobile terminal, obtains the online room booking information of the smart door lock information paired with it based on the near-field communication parameters, and completes the online room booking operation; performs offline real-name check-in registration based on the online room booking result to generate check-in information; during the check-in process, upon receiving an unlocking request sent by the user's mobile terminal, determines whether the positioning information of the mobile terminal is correct. If the smart door lock information paired with it is within the preset area of the online booking room corresponding to the paired smart door lock information, facial data is obtained and matched with the facial data uploaded during registration to determine whether the match is successful. If so, video data of the outer door of the online booking room corresponding to the paired smart door lock information is obtained to determine whether the portrait data contained in the video data is equal to the number of guests at the time of registration. If so, the smart door lock of the online booking room is opened, the check-in operation is completed and check-in information is generated, realizing a judgment method that combines "real site", "real person" and "real number", thereby improving the effectiveness of intelligent check-in management.

[0040] Furthermore, when the program or instruction is executed by the processor, the following steps are specifically implemented: Obtain video data from the outside door of the online booking room corresponding to the paired smart door lock information, and determine whether the portrait data contained in the video data is equal to the number of people staying in the room at the time of registration. If so, the smart door lock of the online booking room is unlocked; After a preset time interval, the number of people detected by the human number sensing device in the online booking room is obtained, and it is determined whether the number of people is equal to the number of people staying at the time of registration. If so, the check-in operation is completed and the check-in information is generated.

[0041] From the above description, it can be seen that the method of determining "real number" is to combine video data and human body number sensing equipment, and the two determine to improve the determination accuracy.

[0042] Furthermore, when the program or instruction is executed by the processor, the following steps are also implemented: S31. According to the check-in information of the online-booked room, obtain the check-in information of the female occupant from all the check-in information of the online-booked room, and obtain the first check-in information; S32. Obtain historical check-in records of the occupant in the first check-in information and calculate the historical check-in frequencies. When the calculated historical check-in frequencies are greater than a first preset threshold, determine whether the online-booked room belongs to a preset high-frequency area. If so, obtain the door lock opening records corresponding to the online-booked room during the occupant's stay. S33. Determine, based on the door lock opening record, whether the number of times the door is opened from inside the online booking room within a preset time range exceeds a second preset threshold; if so, obtain video data of the exterior door of the online booking room collected by the occupant during their stay; S34. Determine whether the portrait data contained in the video data is greater than the number of people staying in the room at the time of registration. If so, obtain the number of people detected by the human number sensing device in the room; S35. Determine whether the number of people is greater than the number of people registered. If so, send an abnormal check-in warning message to the online room management terminal.

[0043] From the above description, it can be seen that by first filtering out the check-in information of female occupants, and then calculating the historical check-in frequency and whether the online booking room belongs to the preset high-frequency area as the basis for whether to obtain the door lock opening record. If it meets the requirements, the door lock opening record is then used to determine whether the number of times the door is opened from inside the online booking room within the preset time range exceeds the second preset threshold. Once the second preset threshold is exceeded, the portrait data in the video data collected at the outer door of the online booking room and the number of human bodies detected by the human body number sensing device in the online booking room are further combined to make judgments in sequence. Finally, a conclusion of suspected abnormal check-in is drawn and an abnormal check-in warning information is sent to the online booking room management end. The online booking room management end can then report to the networked security management platform to avoid adverse situations.

[0044] Furthermore, when the program or instruction is executed by the processor, the following steps are specifically implemented: Determine whether the number of people is greater than the number of people staying in the room at the time of registration. If so, obtain the number of networked wireless networks configured in the online-booked room. Determine whether the number of networked networks is greater than the number of people staying in the room at the time of registration. If so, obtain the number of power switches configured in the online-booked room and the power-on duration. When the number of power switches is less than the third preset threshold and the power-on duration is greater than the fourth preset threshold, send an abnormal occupancy warning message to the online-booked room management terminal.

[0045] From the above description, it can be seen that the number of people in the online booking room is detected by the human body sensing equipment in the online booking room, and then combined with the number of wireless network connections configured in the online booking room, the number of power switches configured in the online booking room, and the length of power supply, to comprehensively determine the conclusion of suspected abnormal check-in, which is more accurate.

[0046] Furthermore, the third preset threshold is set to 2 times, and the fourth preset threshold is set to 2 hours. The first preset threshold is set to 2 times per week and 6 times per month, the preset time range is set to 10 pm to 1 am the next day, and the second preset threshold is set to no less than 5 times within the preset time range or no less than 2 times within a two-hour period.

[0047] Furthermore, when the program or instruction is executed by the processor, the following steps are also implemented: S41. Obtain all check-in information of the online-booked accommodation, extract the check-in information in which the number of registered guests exceeds a fifth preset threshold from all the check-in information, and obtain first check-in information; S42. Obtain the occupant's historical check-in records and calculate the historical check-in frequency. When the calculated historical check-in frequency is greater than a sixth preset threshold, determine whether the online-booked room belongs to a preset high-frequency area. If so, obtain the door lock opening record corresponding to the online-booked room during the occupant's stay. S43. Determine whether the number of times the door is opened from the inside is not higher than a seventh preset threshold based on the door lock opening record. If so, obtain video data of the outside door of the online booking room collected by the occupant during the occupancy period. S44. Determine whether the number of portrait data contained in the video data is greater than an eighth preset threshold. If not, obtain the number of people detected by the human number sensing device in the online booking room. S45. 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 room management terminal.

[0048] From the above description, it can be seen that through the above specific method, the check-in information is first used to screen out large gatherings of people, and then a preliminary judgment is made based on the historical check-in frequency of the occupants and whether the online booking room belongs to the preset high-frequency area. Then, the door lock opening record, the video data collected at the outer door of the online booking room, and the number of people detected by the human body sensing equipment in the online booking room are further combined to make judgments in sequence. Finally, a conclusion is drawn that there is suspected abnormal behavior of large gatherings of people, and by sending an early warning information on abnormal behavior of large gatherings of people to the online booking room management end, an early warning of abnormal behavior of large gatherings of people in the online booking room is realized. The online booking room management end can subsequently report to the networked public security management platform to avoid adverse situations.

[0049] 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, obtain the number of power switches configured in the online booking room and the power-on duration. When the number of power switches is less than the ninth preset threshold and the power-on duration is greater than the tenth preset threshold, send an abnormal behavior warning message of multiple people gathering to the online booking room management end.

[0050] From the above description, it can be seen that the number of people is detected by the human body sensing equipment in the online booking room, and then combined with the number of wireless network connections 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.

[0051] Furthermore, the ninth preset threshold is set to 2 times, the tenth preset threshold is set to 15 hours, the seventh preset threshold is set to zero, the fifth preset threshold and the eighth preset threshold are both 2 people, and the sixth preset threshold is 2 times per week and 6 times per month.

[0052] Furthermore, when the program or instruction is executed by the processor, the following steps are also implemented: S51. Obtain all check-in information of the online-booked accommodation, extract the check-in information of the minors from all the check-in information, and obtain the first check-in information; S52: Determine whether the check-in duration corresponding to the first check-in information reaches a preset duration and there is no record of other people checking in during the check-in period. If so, obtain the door lock opening record corresponding to the online-booked room during the check-in period; S53. Determine whether the number of times the door is opened from the inside is not higher than a preset value based on the door lock opening record. If so, obtain video data of the outside door of the online booking room collected by the occupant during the occupant's stay. S54: Determine whether the video data contains human portrait data. If not, obtain the number of people detected by the human number sensing device in the online booking room; S55. Determine whether the number of people exceeds the number of occupants. If so, send a warning message about minors living in cramped conditions to the online room management terminal.

[0053] From the above description, it can be seen that through the above specific method, minors are first screened out by the check-in information, and then a preliminary judgment is made whether there is a situation of cramped living based on the length of their stay and whether there are other people's check-in records. Then, the door lock opening record, the video data collected at the outer door of the online rental house, and the number of people detected by the human body sensing equipment in the online rental house are further combined to make judgments in sequence. Finally, a comprehensive conclusion is drawn that minors are suspected of living in cramped living, and an early warning message is sent to the online rental house management end, thereby realizing an early warning of cramped living of minors in the online rental house. The online rental house management end can subsequently report to the networked public security management platform, and the public security management platform will notify their guardians to avoid adverse situations.

[0054] 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-booked room. Determine whether the number of networked networks exceeds the number of occupants. If so, obtain the number of power switches configured in the online-booked room and the length of power supply time. When the number of power switches is less than the eleventh preset threshold and the length of power supply time is greater than the twelfth preset threshold, send a warning message about minors living in cramped conditions to the online-booked room management end.

[0055] From the above description, it can be seen that the number of people in the online booking room is detected by the human body sensing equipment in the room, and then combined with the number of wireless network connections configured in the room, the number of power switches configured in the room, and the length of time of power supply, to comprehensively determine the conclusion that suspected minors are living in a cramped environment, with higher accuracy.

[0056] Furthermore, when the program or instruction is executed by the processor, the following steps are specifically implemented: All check-in information of online-booked houses is obtained, check-in information of minors is extracted from all check-in information, and check-in information of minors with historical cramped living behavior data is extracted based on historical cramped living behavior data in a preset database to obtain first check-in information; the preset database is used to store information of minors with historical cramped living behavior data.

[0057] From the above description, it can be seen that when the check-in information of a minor is extracted, it is first determined through the preset database whether the minor has a history of cramped living behavior. If so, special attention is paid to the minor, as there is a high probability that the minor has engaged in cramped living behavior.

[0058] Furthermore, the eleventh preset threshold is set to 2 times, the twelfth preset threshold is set to 11 hours, and the preset value in step S53 is set to zero.

[0059] 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 Figure 1 The present invention provides an Internet-based intelligent check-in method for accommodation industry, comprising the following steps: The present invention provides an Internet-based intelligent check-in method for accommodation, comprising the following steps: S1. Obtain an online room booking order generated by a user, obtain the model information of the user's mobile terminal based on the online room booking order, obtain corresponding near-field communication parameters based on the model information of the user's mobile terminal, obtain the online room booking information of the smart door lock information paired with the near-field communication parameters, and complete the online room booking operation; In this embodiment, step S1 is specifically as follows: Obtain an online room booking order generated by a user, where the online room booking order can be obtained from a third-party online platform. Based on the online room booking order, obtain the model information of the user's mobile terminal, and obtain corresponding near-field communication parameters based on the model information of the user's mobile terminal. In actual practice, if the user places an online order using a mobile terminal, the system can automatically obtain the mobile terminal model information. If the user places an online order using a PC, the user can enter the model information themselves.

[0060] Obtaining a first housing list, the first housing list consisting of information on online-booked houses currently available for check-in, and obtaining corresponding configured smart door lock information based on the online-booked house information in the first housing list; Matching the near-field communication parameters with the smart door lock information one by one, obtaining the online booking information corresponding to the successfully matched smart door lock information and generating a corresponding second housing list; Sending the second housing listing to the user's mobile terminal; If the online booking information selected from the second housing list is received from 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.

[0061] Furthermore, step S1 further includes: If the booker information contained in the online room booking order is consistent with the occupant information, the model information of the mobile terminal held by the user is obtained according to the online room 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 contained in the online room booking order is inconsistent with the check-in information, the historical order information corresponding to the booker information is obtained, and the historical order information is checked to see whether the check-in information is contained. If so, the model information of the mobile terminal held by the check-in information is obtained based on 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 check-in information is contained. If so, the model information of the mobile terminal held by the check-in information is obtained based on the historical check-in information; if not, the near-field communication parameters of the check-in information are set to the near-field communication parameters of the smart door lock information corresponding to the lowest version.

[0062] Through the above methods, it is possible to first determine whether the check-in person and the booker are the same. If they are, the NFC parameters corresponding to the model information of the user's mobile terminal are obtained for subsequent matching operations, thereby improving the efficiency of data processing. If the booker information included in the online room booking order is inconsistent with the check-in person information, the above three methods can be used to complete the NFC parameter acquisition, thus realizing intelligent operation.

[0063] Specifically, there are three categories: 1. A user has placed an order but not checked in; 2. A user has not placed an order but has checked in; and 3. A user has neither placed an order nor checked in. Each of these three methods is used to obtain near-field communication parameters, enabling intelligent operation without further verification and confirmation with the user, reducing intermediate steps and improving system processing efficiency. It should be noted that this solution assumes that all users currently have mobile devices. If a user does not have a mobile device, this solution is not applicable.

[0064] Step S1 further includes: The NFC parameters are matched one-to-one with the smart door lock information. The smart door lock information with a version lower than the NFC parameters is extracted from all smart door lock information and used as the successfully paired smart door lock information. The online room information corresponding to the successfully paired smart door lock information is used to generate a corresponding second housing list. The online room information includes supporting information such as room type information and amenities. The amenities primarily include smart door lock information, such as door locks that support various NFC technologies, such as Bluetooth door locks and NFC door locks, as well as smart door locks that work with gateways.

[0065] Through the above method, the pairing operation of the near-field communication parameters and the smart door lock information is completed, and the smart door lock information with a version lower than the near-field communication parameters in all smart door lock information is extracted and used as the successfully paired smart door lock information, thereby ensuring that the version of the user's mobile terminal is higher than that of the smart door lock, and thus ensuring that the unlocking operation can be completed.

[0066] Step S1 is specifically as follows: Comprehensively scoring the online booking housing information in the second housing source list, sorting the information from high to low according to the comprehensive scores, extracting a preset number of online booking housing information from high to low according to the comprehensive scores to form a new second housing source list and sending 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, through big data analysis, the user is usually entangled in making choices, the preset number can be dynamically set to be smaller, such as 5, to avoid causing difficulty in user selection; if it is analyzed that the user is usually decisive in 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.

[0067] The above-mentioned comprehensive score is calculated based on the online room's supporting information and historical evaluation information. Supporting information, such as room type and amenities, can be assigned corresponding scores and weights. Historical evaluation information, such as the scores and weights obtained from reviews of the online room after staying there for three months, six months, or a year, is used to calculate the comprehensive score. Specifically, each score is multiplied by the corresponding weight and then accumulated to obtain the comprehensive score. Of course, other calculation methods can also be used to obtain the comprehensive score.

[0068] If a selected online booking information from the new second listing is received from the user's mobile terminal within the preset time period, the selected online booking information is bound to the user's mobile terminal, completing the room reservation operation. The preset time is generally set to 1-10 minutes, but can also be dynamically set based on the user's personal preferences, as described above and will not be repeated here.

[0069] Through the above method, the data in the second housing list can be further optimized, which not only reduces unnecessary data transmission and improves transmission efficiency, but also enables more reasonable screening for users, helping users to choose more satisfactory online housing, thereby further improving user experience.

[0070] S2. After offline real-name check-in registration based on the online room reservation results, check-in information is generated; specifically, it includes: S21, receiving an unlocking request from a mobile terminal held by a user, determining whether the positioning information of the mobile terminal is within the preset area of the online booking room corresponding to the paired smart door lock information, and if so, proceeding to step S22; S22, obtaining facial data and matching the obtained facial data with the facial data uploaded during registration to determine whether the match is successful. If so, proceed to step S23; S23. Obtain video data at the outer door of the online booking room corresponding to the paired smart door lock information, and determine whether the portrait data contained in the video data is equal to the number of people staying at the time of registration. If so, the smart door lock of the online booking room is opened, the check-in operation is completed, and check-in information is generated.

[0071] Furthermore, step S23 is specifically as follows: The system obtains video data from the exterior entrance of the online booking room corresponding to the paired smart door lock information, and determines whether the portrait data contained in the video data is equal to the number of people staying in the room at the time of registration. If so, the smart door lock of the online booking room is unlocked. After a preset interval, the system obtains the number of people detected by the human body number sensing device in the online booking room, and determines whether the number of people in the room is equal to the number of people staying in the room at the time of registration. If so, the check-in operation is completed and check-in information is generated. By combining video data and human body number sensing devices as a method for determining "real numbers", the two methods improve the accuracy of the determination. The preset time period can be set according to actual needs.

[0072] In this embodiment, step S2 is followed by step S3, which specifically includes: S31. According to the check-in information of the online-booked room, obtain the check-in information of the room in which the occupant is a female from all the check-in information of the online-booked room, and obtain the first check-in information.

[0073] S32. Obtain the historical check-in records of the occupant in the first check-in information and calculate the historical check-in frequencies respectively. When the calculated historical check-in frequencies are greater than a first preset threshold, determine whether the online-booked room belongs to a preset high-frequency area. If so, obtain the door lock opening records corresponding to the online-booked room during the occupant's stay; wherein the first preset threshold is set to 2 times per week and 6 times per month; the above-mentioned preset high-frequency areas are areas with a high incidence of bad behavior distributed by the public security management platform.

[0074] S33. Based on the door lock opening record, determine whether the number of times the door is opened from inside the online-booked room within a preset time range exceeds a second preset threshold. If so, obtain video data of the occupant's external door of the online-booked room collected during the occupant's stay; wherein the preset time range is set to 10 pm to 1 am the next day, and the second preset threshold is set to no less than 5 times within the preset time range or no less than 2 times within two hours.

[0075] S34. Determine whether the human portrait data contained in the video data exceeds the number of people registered in the room. If so, obtain the number of people detected by the human presence sensing device in the online room. Determine the human portrait data using existing human portrait recognition technology. By identifying and determining the human portrait data, it is possible to determine whether anyone entered the room from outside during the stay. Furthermore, the human presence sensing device is an existing product and meets the requirements for legal installation.

[0076] S35. Determine whether the number of people is greater than the number of people registered. If so, send an abnormal check-in warning message to the online room management terminal.

[0077] Step S35 is specifically as follows: Determine whether the number of people is greater than the number of people registered at the time of occupancy. If so, obtain the number of wireless network connections configured in the online booking room. Determine whether the number of connections is greater than the number of people registered at the time of occupancy. If so, obtain the number of power on / off switches configured in the online booking room and the power supply duration. When the number of power on / off switches is less than a third preset threshold and the power supply duration is greater than a fourth preset threshold, send an abnormal occupancy warning message to the online booking room management terminal. The third preset threshold is set to 2 times, and the fourth preset threshold is set to 2 hours.

[0078] By first filtering out the check-in information of female occupants, and then calculating the historical check-in frequency and whether the online booking room belongs to a preset high-frequency area as the basis for whether to obtain the door lock opening record, if it meets the requirements, the door lock opening record is then used to determine whether the number of times the door is opened from inside the online booking room within the preset time range exceeds a second preset threshold. Once the second preset threshold is exceeded, the human portrait data in the video data collected at the outside door of the online booking room and the number of people detected by the human body number sensing device in the online booking room are further combined to make judgments in sequence. Finally, a conclusion of suspected abnormal check-in is drawn and an abnormal check-in warning information is sent to the online booking room management terminal. The online booking room management terminal can then report it to the networked public security management platform to avoid adverse situations. 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 wireless networks configured in the online booking room, the number of power switches configured in the online booking room, and the length of time of power supply, the conclusion of suspected abnormal check-in is comprehensively determined with higher accuracy.

[0079] In this embodiment, step S2 is followed by step S4, which specifically includes: S41. Obtain all occupancy information of the online-booked accommodation, extract occupancy information in which the number of registered occupants exceeds a fifth preset threshold from all occupancy information, and obtain first occupancy information; the fifth preset threshold is 2 people; S42. Obtain the occupant's historical check-in records and calculate the historical check-in frequency. If the calculated historical check-in frequency is greater than a sixth preset threshold, determine whether the online-booked room belongs to a preset high-frequency area. If so, obtain the occupant's corresponding door unlocking record during their stay. The sixth preset threshold is 2 times per week and 6 times per month. The preset high-frequency area is a high-frequency area where multiple people gather to commit crimes, as determined by the public security management platform.

[0080] S43. Determine, based on the door lock opening record, whether the number of times the door is opened from the inside is not greater than a seventh preset threshold. If so, obtain video data of the outside door of the online booking room collected by the occupant during the occupancy period; the seventh preset threshold is set to zero. S44. Determine whether the number of people in the video data exceeds an eighth preset threshold. If not, obtain the number of people detected by the human count sensing device within the online room; the eighth preset threshold is 2. Determine the human count using existing human count recognition technology. By identifying and determining the human count data, it is possible to determine whether anyone entered the room from outside during the stay. Furthermore, the human count sensing device is an existing product and meets the requirements for legal installation of related products.

[0081] S45. 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 room management terminal.

[0082] Step S45 is specifically as follows: Determine whether the number of people exceeds the number of occupants. If so, obtain the number of wireless network connections configured in the online booking room. Determine whether the number of connections exceeds the number of occupants. If so, obtain the number of power on / off times configured in the online booking room and the power supply duration. When the number of power on / off times is less than a ninth preset threshold and the power supply duration is greater than a tenth preset threshold, send an abnormal multi-person gathering behavior warning message to the online booking room management terminal. The ninth preset threshold is set to 2 times, and the tenth preset threshold is set to 15 hours.

[0083] Through the above-mentioned specific method, the check-in information is first used to screen out large gatherings of people. Then, a preliminary judgment is made based on the historical check-in frequency of the occupants and whether the online booking room belongs to a preset high-frequency area. Then, the door lock opening record, the video data collected at the outside door of the online booking room, and the number of people detected by the human body number sensing device in the online booking room are further combined to make judgments in sequence. Finally, a conclusion is drawn that there is suspected abnormal behavior of large gatherings of people and an early warning of abnormal behavior of large gatherings of people is sent to the online booking room management terminal, thereby realizing an early warning of abnormal behavior of large gatherings of people in the online booking room. The online booking room management terminal can then report to the networked public security management platform to avoid adverse situations. 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 wireless networks configured in the online booking room, the number of power switches configured in the online booking room, and the length of time of power supply, to further comprehensively determine the conclusion of suspected abnormal behavior of large gatherings of people, with higher accuracy.

[0084] In this embodiment, step S2 is followed by step S5, which specifically includes: S51. Obtain all check-in information of the online-booked accommodation, extract the check-in information of the minors from all the check-in information, and obtain the first check-in information; Step S51 is specifically as follows: All check-in information for online-booked accommodations is obtained, and check-in information for minors is extracted from all check-in information. Based on historical data on cramped living behavior in a preset database, the check-in information for minors with historical data on cramped living behavior is extracted to obtain first check-in information; the preset database is used to store information on minors with historical data on cramped living behavior. When check-in information for a minor is extracted, the preset database is first used to determine whether the minor has a history of cramped living behavior. If so, the minor is given special attention, as there is a high probability of the minor engaging in cramped living behavior.

[0085] S52: Determine whether the check-in duration corresponding to the first check-in information reaches a preset duration and there is no record of other people checking in during the check-in period. If so, obtain the door lock opening record corresponding to the online-booked room during the check-in period; S53. Determine whether the number of times the door is opened from the inside is not higher than a preset value based on the door lock opening record. If so, obtain video data of the outside door of the online booking room collected by the occupant during the occupant's stay; the preset value in step S53 is set to zero.

[0086] S54. Determine whether the video data contains human portrait data. If not, obtain the number of people detected by the human presence sensing device within the online room. Determine the human presence data using existing human presence recognition technology. By identifying and determining the human presence data, it is possible to determine whether anyone entered the room from outside during the stay. Furthermore, the human presence sensing device is an existing product and meets the requirements for legal installation.

[0087] S55. Determine whether the number of people exceeds the number of occupants. If so, send a warning message about minors living in cramped conditions to the online room management terminal.

[0088] Step S55 is specifically as follows: Determine whether the number of people exceeds the number of occupants. If so, obtain the number of wireless network connections configured in the online room. Determine whether the number of connections exceeds the number of occupants. If so, obtain the number of power on / off switches configured in the online room and the power supply duration. When the number of power on / off switches is less than the eleventh preset threshold and the power supply duration is greater than the twelfth preset threshold, send a warning message about minors living in a cramped environment to the online room management terminal. The eleventh preset threshold is set to 2 times, and the twelfth preset threshold is set to 11 hours.

[0089] Through the above specific method, first, minors are screened out by check-in information, and then a preliminary judgment is made based on the length of their stay and whether there are other people staying there to determine whether there is a situation of slumping. Then, further judgments are made in sequence based on the door lock opening records, the video data collected at the outside door of the online rental house, and the number of people detected by the human body number sensing equipment in the online rental house. Finally, a comprehensive conclusion is drawn that a minor is suspected of living in a slump and an early warning message is sent to the online rental house management end, thereby realizing an early warning of slumping of minors in the online rental house. The online rental house management end can then report to the networked public security management platform, which will notify their guardians to avoid adverse situations. The number of people is detected by the human body number sensing equipment in the online rental house, and then combined with the number of wireless networks configured in the online rental house, the number of power switches configured in the online rental house, and the length of time the power is taken, to comprehensively determine the conclusion that a minor is suspected of living in a slump, with higher accuracy.

[0090] In addition to the above-mentioned step S3 for determining suspected abnormal check-in, step S4 for determining suspected abnormal behavior of multiple people gathering, and step S5 for determining suspected minors living in a small house, the method further includes: a missed registration warning method, specifically the following step S6, which specifically includes: S61. Acquire check-in information of the online-booked room, wherein the check-in information includes the first number of guests; further, step S61 is specifically as follows: Obtain occupancy information for the online-booked accommodation, including the first number of occupants, and determine whether the occupant is a non-local resident. If so, obtain the occupant's recent check-in records within a preset time range and determine whether the number of occupants in the check-in records is equal to the first number of occupants. If not, proceed to step S2. This can be done by first determining whether the occupant is a non-local resident. If so, obtain the occupant's recent check-in records within a preset time range and determine whether the number of occupants in the check-in records is equal to the first number of occupants. If not, there is a possibility of missed registration, and further comprehensive determination can then be made.

[0091] S62: Obtain the door lock opening records corresponding to the online-booked room during the occupant's stay, and calculate the difference between the number of times the door was opened from the inside and the number of times the door was opened from the outside within a preset time range based on the door lock opening records; S63. Determine whether the difference is greater than a preset difference threshold. If so, obtain video data of the occupant at the outer door of the online-booked room collected during the occupant's stay. In this embodiment, the preset difference threshold is 5 times.

[0092] S64: Determine whether the human portrait data contained in the video data exceeds the first number of occupants. If so, obtain the number of people detected by the human presence sensing device within the online room. Determine the human portrait data using existing human portrait recognition technology. By identifying and determining the human portrait data, it is possible to determine whether anyone entered the room from outside during the stay. Furthermore, the human presence sensing device is an existing product and meets the requirements for legal installation.

[0093] S65. Determine whether the number of people is greater than the first number of people staying in the room. If so, send a missed registration warning message to the online room management terminal.

[0094] Furthermore, step S65 is specifically as follows: The system determines whether the number of people is greater than the first number of occupants. If so, it obtains the number of wireless network connections configured within the online room and determines whether the number of connections is greater than the first number of occupants. If so, it sends a missed registration warning message to the online room management terminal. By detecting the number of people using the room's human number sensing device and further combining it with the number of wireless network connections configured within the room, a comprehensive conclusion can be drawn regarding suspected missed registration.

[0095] To further improve the accuracy, step S65 is specifically as follows: Determine whether the number of people is greater than the first number of people staying in the room. If so, obtain the number of wireless network connections configured in the online booking room. Determine whether the number of connections is greater than the first number of people staying in the room. If so, obtain the number of power switches configured in the online booking room and the duration of power supply. When the number of power switches is less than the thirteenth preset threshold and the duration of power supply is greater than the fourteenth preset threshold, send a missed registration warning message to the online booking room management terminal. The number of people is detected by the human number sensing device in the online booking room, and then combined with the number of wireless networks configured in the online booking room, the number of power switches configured in the online booking room, and the duration of power supply, to further comprehensively determine the conclusion of suspected missed registration behavior, with higher accuracy. In this embodiment, the thirteenth preset threshold is set to 2 times, and the fourteenth preset threshold is set to 2 hours.

[0096] It should be noted that the above four determination methods are in parallel relationship and there is no limit on the order.

[0097] Preferred embodiment 2: See Figure 2 The present invention also provides an Internet-based intelligent check-in system for the accommodation industry, comprising 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 an online room booking order generated by a user, obtain the model information of the user's mobile terminal based on the online room booking order, obtain corresponding near-field communication parameters based on the model information of the user's mobile terminal, obtain the online room booking information of the smart door lock information paired with the near-field communication parameters, and complete the online room booking operation; S2. After offline real-name check-in registration based on the online room reservation results, check-in information is generated; specifically, it includes: S21, receiving an unlocking request from a mobile terminal held by a user, determining whether the positioning information of the mobile terminal is within the preset area of the online booking room corresponding to the paired smart door lock information, and if so, proceeding to step S22; S22, obtaining facial data and matching the obtained facial data with the facial data uploaded during registration to determine whether the match is successful. If so, proceed to step S23; S23. Obtain video data at the outer door of the online booking room corresponding to the paired smart door lock information, and determine whether the portrait data contained in the video data is equal to the number of people staying at the time of registration. If so, the smart door lock of the online booking room is opened, the check-in operation is completed, and check-in information is generated.

[0098] Furthermore, when the program or instruction is executed by the processor, the following steps are specifically implemented: Obtain video data from the outside door of the online booking room corresponding to the paired smart door lock information, and determine whether the portrait data contained in the video data is equal to the number of people staying in the room at the time of registration. If so, the smart door lock of the online booking room is unlocked; After a preset time interval, the number of people detected by the human number sensing device in the online booking room is obtained, and it is determined whether the number of people is equal to the number of people staying at the time of registration. If so, the check-in operation is completed and the check-in information is generated.

[0099] Furthermore, when the program or instruction is executed by the processor, the following steps are also implemented: S31. According to the check-in information of the online-booked room, obtain the check-in information of the female occupant from all the check-in information of the online-booked room, and obtain the first check-in information; S32. Obtain historical check-in records of the occupant in the first check-in information and calculate the historical check-in frequencies. When the calculated historical check-in frequencies are greater than a first preset threshold, determine whether the online-booked room belongs to a preset high-frequency area. If so, obtain the door lock opening records corresponding to the online-booked room during the occupant's stay. S33. Determine, based on the door lock opening record, whether the number of times the door is opened from inside the online booking room within a preset time range exceeds a second preset threshold; if so, obtain video data of the exterior door of the online booking room collected by the occupant during their stay; S34. Determine whether the portrait data contained in the video data is greater than the number of people staying in the room at the time of registration. If so, obtain the number of people detected by the human number sensing device in the room; S35. Determine whether the number of people is greater than the number of people registered. If so, send an abnormal check-in warning message to the online room management terminal.

[0100] Furthermore, when the program or instruction is executed by the processor, the following steps are specifically implemented: Determine whether the number of people is greater than the number of people staying in the room at the time of registration. If so, obtain the number of networked wireless networks configured in the online-booked room. Determine whether the number of networked networks is greater than the number of people staying in the room at the time of registration. If so, obtain the number of power switches configured in the online-booked room and the power-on duration. When the number of power switches is less than the third preset threshold and the power-on duration is greater than the fourth preset threshold, send an abnormal occupancy warning message to the online-booked room management terminal.

[0101] Furthermore, the third preset threshold is set to 2 times, and the fourth preset threshold is set to 2 hours. The first preset threshold is set to 2 times per week and 6 times per month, the preset time range is set to 10 pm to 1 am the next day, and the second preset threshold is set to no less than 5 times within the preset time range or no less than 2 times within a two-hour period.

[0102] Furthermore, when the program or instruction is executed by the processor, the following steps are also implemented: S41. Obtain all check-in information of the online-booked accommodation, extract the check-in information in which the number of registered guests exceeds a fifth preset threshold from all the check-in information, and obtain first check-in information; S42. Obtain the occupant's historical check-in records and calculate the historical check-in frequency. When the calculated historical check-in frequency is greater than a sixth preset threshold, determine whether the online-booked room belongs to a preset high-frequency area. If so, obtain the door lock opening record corresponding to the online-booked room during the occupant's stay. S43. Determine whether the number of times the door is opened from the inside is not higher than a seventh preset threshold based on the door lock opening record. If so, obtain video data of the outside door of the online booking room collected by the occupant during the occupancy period. S44. Determine whether the number of portrait data contained in the video data is greater than an eighth preset threshold. If not, obtain the number of people detected by the human number sensing device in the online booking room. S45. 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 room management terminal.

[0103] 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, obtain the number of power switches configured in the online booking room and the power-on duration. When the number of power switches is less than the ninth preset threshold and the power-on duration is greater than the tenth preset threshold, send an abnormal behavior warning message of multiple people gathering to the online booking room management end.

[0104] Furthermore, the ninth preset threshold is set to 2 times, the tenth preset threshold is set to 15 hours, the seventh preset threshold is set to zero, the fifth preset threshold and the eighth preset threshold are both 2 people, and the sixth preset threshold is 2 times per week and 6 times per month.

[0105] Furthermore, when the program or instruction is executed by the processor, the following steps are also implemented: S51. Obtain all check-in information of the online-booked accommodation, extract the check-in information of the minors from all the check-in information, and obtain the first check-in information; S52: Determine whether the check-in duration corresponding to the first check-in information reaches a preset duration and there is no record of other people checking in during the check-in period. If so, obtain the door lock opening record corresponding to the online-booked room during the check-in period; S53. Determine whether the number of times the door is opened from the inside is not higher than a preset value based on the door lock opening record. If so, obtain video data of the outside door of the online booking room collected by the occupant during the occupant's stay. S54: Determine whether the video data contains human portrait data. If not, obtain the number of people detected by the human number sensing device in the online booking room; S55. Determine whether the number of people exceeds the number of occupants. If so, send a warning message about minors living in cramped conditions to the online room management terminal.

[0106] 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-booked room. Determine whether the number of networked networks exceeds the number of occupants. If so, obtain the number of power switches configured in the online-booked room and the length of power supply time. When the number of power switches is less than the eleventh preset threshold and the length of power supply time is greater than the twelfth preset threshold, send a warning message about minors living in cramped conditions to the online-booked room management end.

[0107] Furthermore, when the program or instruction is executed by the processor, the following steps are specifically implemented: All check-in information of online-booked houses is obtained, check-in information of minors is extracted from all check-in information, and check-in information of minors with historical cramped living behavior data is extracted based on historical cramped living behavior data in a preset database to obtain first check-in information; the preset database is used to store information of minors with historical cramped living behavior data.

[0108] Furthermore, the eleventh preset threshold is set to 2 times, the twelfth preset threshold is set to 11 hours, and the preset value in step S53 is set to zero.

[0109] The present invention has been described with reference to the above embodiments and accompanying drawings. However, the above embodiments are merely exemplary embodiments of the present invention. It should be noted that the disclosed embodiments do not limit the scope of the present invention. On the contrary, modifications and equivalents falling within the spirit and scope of the claims are intended to be within the scope of the present invention.

Claims

1. An intelligent check-in method for accommodation industry based on the Internet, characterized in that: The following steps are involved: S1. Obtain an online room booking order generated by a user, obtain the model information of the user's mobile terminal based on the online room booking order, obtain corresponding near-field communication parameters based on the model information of the user's mobile terminal, obtain the online room booking information of the smart door lock information paired with the near-field communication parameters, and complete the online room booking operation; S2. After offline real-name check-in registration based on the online room reservation results, check-in information is generated; specifically, it includes: S21, receiving an unlocking request from a mobile terminal held by a user, determining whether the positioning information of the mobile terminal is within the preset area of the online booking room corresponding to the paired smart door lock information, and if so, proceeding to step S22; S22, obtaining facial data and matching the obtained facial data with the facial data uploaded during registration to determine whether the match is successful. If so, proceed to step S23; S23. Obtain video data at the outer door of the online booking room corresponding to the paired smart door lock information, and determine whether the portrait data contained in the video data is equal to the number of people staying at the time of registration. If so, the smart door lock of the online booking room is opened, the check-in operation is completed, and check-in information is generated.

2. The Internet-based intelligent check-in method for accommodation industry according to claim 1, characterized in that: Step S23 is specifically as follows: Obtain video data from the outside door of the online booking room corresponding to the paired smart door lock information, and determine whether the portrait data contained in the video data is equal to the number of people staying in the room at the time of registration. If so, the smart door lock of the online booking room is unlocked; After a preset time interval, the number of people detected by the human number sensing device in the online booking room is obtained, and it is determined whether the number of people is equal to the number of people staying at the time of registration. If so, the check-in operation is completed and the check-in information is generated.

3. The intelligent check-in method for accommodation industry based on the Internet according to claim 1, characterized in that: Step S2 is followed by step S3, which specifically includes: S31. According to the check-in information of the online-booked room, obtain the check-in information of the female occupant from all the check-in information of the online-booked room, and obtain the first check-in information; S32. Obtain historical check-in records of the occupant in the first check-in information and calculate the historical check-in frequencies. When the calculated historical check-in frequencies are greater than a first preset threshold, determine whether the online-booked room belongs to a preset high-frequency area. If so, obtain the door lock opening records corresponding to the online-booked room during the occupant's stay. S33. Determine, based on the door lock opening record, whether the number of times the door is opened from inside the online booking room within a preset time range exceeds a second preset threshold; if so, obtain video data of the exterior door of the online booking room collected by the occupant during their stay; S34. Determine whether the portrait data contained in the video data is greater than the number of people staying in the room at the time of registration. If so, obtain the number of people detected by the human number sensing device in the room; S35. Determine whether the number of people is greater than the number of people registered. If so, send an abnormal check-in warning message to the online room management terminal.

4. The Internet-based intelligent check-in method for accommodation industry according to claim 3, characterized in that: Step S35 is specifically as follows: Determine whether the number of people is greater than the number of people staying in the room at the time of registration. If so, obtain the number of networked wireless networks configured in the online-booked room. Determine whether the number of networked networks is greater than the number of people staying in the room at the time of registration. If so, obtain the number of power switches configured in the online-booked room and the power-on duration. When the number of power switches is less than the third preset threshold and the power-on duration is greater than the fourth preset threshold, send an abnormal occupancy warning message to the online-booked room management terminal.

5. The Internet-based intelligent check-in method for accommodation industry according to claim 1, characterized in that: Step S2 is followed by step S4, which specifically includes: S41. Obtain all check-in information of the online-booked accommodation, extract the check-in information in which the number of registered guests exceeds a fifth preset threshold from all the check-in information, and obtain first check-in information; S42. Obtain the occupant's historical check-in records and calculate the historical check-in frequency. When the calculated historical check-in frequency is greater than a sixth preset threshold, determine whether the online-booked room belongs to a preset high-frequency area. If so, obtain the door lock opening record corresponding to the online-booked room during the occupant's stay. S43. Determine whether the number of times the door is opened from the inside is not higher than a seventh preset threshold based on the door lock opening record. If so, obtain video data of the outside door of the online booking room collected by the occupant during the occupancy period. S44. Determine whether the number of portrait data contained in the video data is greater than an eighth preset threshold. If not, obtain the number of people detected by the human number sensing device in the online booking room. S45. 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 room management terminal.

6. The Internet-based intelligent check-in method for accommodation industry according to claim 5, characterized in that: Step S45 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, obtain the number of power switches configured in the online booking room and the power-on duration. When the number of power switches is less than the ninth preset threshold and the power-on duration is greater than the tenth preset threshold, send an abnormal behavior warning message of multiple people gathering to the online booking room management end.

7. The Internet-based intelligent check-in method for accommodation industry according to claim 1, characterized in that: Step S2 is followed by step S5, which specifically includes: S51. Obtain all check-in information of the online-booked accommodation, extract the check-in information of the minors from all the check-in information, and obtain the first check-in information; S52: Determine whether the check-in duration corresponding to the first check-in information reaches a preset duration and there is no record of other people checking in during the check-in period. If so, obtain the door lock opening record corresponding to the online-booked room during the check-in period; S53. Determine whether the number of times the door is opened from the inside is not higher than a preset value based on the door lock opening record. If so, obtain video data of the outside door of the online booking room collected by the occupant during the occupant's stay. S54: Determine whether the video data contains human portrait data. If not, obtain the number of people detected by the human number sensing device in the online booking room; S55. Determine whether the number of people exceeds the number of occupants. If so, send a warning message about minors living in cramped conditions to the online room management terminal.

8. The Internet-based intelligent check-in method for accommodation industry according to claim 7, characterized in that: Step S55 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-booked room. Determine whether the number of networked networks exceeds the number of occupants. If so, obtain the number of power switches configured in the online-booked room and the length of power supply time. When the number of power switches is less than the eleventh preset threshold and the length of power supply time is greater than the twelfth preset threshold, send a warning message about minors living in cramped conditions to the online-booked room management end.

9. The Internet-based intelligent check-in method for accommodation industry according to claim 7, characterized in that: Step S51 is specifically as follows: All check-in information of online-booked houses is obtained, check-in information of minors is extracted from all check-in information, and check-in information of minors with historical cramped living behavior data is extracted based on historical cramped living behavior data in a preset database to obtain first check-in information; the preset database is used to store information of minors with historical cramped living behavior data.

10. An Internet-based intelligent check-in system for accommodation industry, characterized in that: The system comprises 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 an online room booking order generated by a user, obtain the model information of the user's mobile terminal based on the online room booking order, obtain corresponding near-field communication parameters based on the model information of the user's mobile terminal, obtain the online room booking information of the smart door lock information paired with the near-field communication parameters, and complete the online room booking operation; S2. After offline real-name check-in registration based on the online room reservation results, check-in information is generated; specifically, it includes: S21, receiving an unlocking request from a mobile terminal held by a user, determining whether the positioning information of the mobile terminal is within the preset area of the online booking room corresponding to the paired smart door lock information, and if so, proceeding to step S22; S22, obtaining facial data and matching the obtained facial data with the facial data uploaded during registration to determine whether the match is successful. If so, proceed to step S23; S23. Obtain video data at the outer door of the online booking room corresponding to the paired smart door lock information, and determine whether the portrait data contained in the video data is equal to the number of people staying at the time of registration. If so, the smart door lock of the online booking room is opened, the check-in operation is completed, and check-in information is generated.

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