Check-in management system and method based on big data analysis

Abstract

The invention relates to a check-in management system and a check-in management method based on big data analysis, which belong to the technical field of hotel check-in management, can avoid asking for fingerprint recognition permission, face recognition permission and the like of a user through password permission and weight and height matching permission, and can improve payment efficiency in a modern payment environment. Most users are bound through fingerprints, human faces and the like for quick payment, so that the users do not need to worry about fingerprint and human face safety problems, and the experience feeling of hotel check-in users is guaranteed to a great extent; moreover, after the user passes password permission authentication and weight and height matching permission authentication in sequence, contour recognition authentication is triggered to serve as checkout permission authentication of the password permission authentication and the weight and height matching permission authentication, and the safety of the hotel check-in user is further guaranteed. Wherein the matching authority of the body weight and the body height and the contour recognition authentication are not started under the normal condition, so that the energy consumption can be reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of check-in management, and in particular relates to a check-in management system and method based on big data analysis. Background Art

[0002] Most hotels rely on service staff to handle registration at the front desk. Due to the limited number of front desk staff and the time-consuming and laborious registration process, it is easy to bring a bad experience to consumers, especially when there are many guests, which can easily lead to errors in registration information and affect consumers' impression of the hotel. At the same time, the hotel's registration method cannot provide guests with humanized services such as self-service and room selection, thereby reducing the hotel's service efficiency.

[0003] There is a hotel self-service check-in registration management system, which includes a central server, the input end of the central server is electrically connected to the output end of the registration module, the room selection module, the payment module and the room card transceiver module in sequence, and the registration module includes an ID card recognition module, a face recognition module and a fingerprint entry module. The hotel self-service check-in registration management system adopts an ID card recognition module, so that guests can self-register through their ID cards. During the registration process, by entering fingerprints and facial information, the check-in registration management is made more intelligent, accurate and standardized, and self-service processing improves the efficiency of check-in registration, can save users a lot of time, reduce the workload of hotel staff, reduce operating costs, and can provide guests with humanized services for selecting rooms, thereby improving the quality of hotel services.

[0004] However, with this hotel management model, when guests need to go out, they need to carry their room cards with them, and the hotel will experience a power outage due to the lack of room cards. If the user asks the hotel for an extra room card, it will be inconvenient for hotel management and a large-scale use of room cards will lead to a proliferation of room cards and hotel security problems. There are also proposals to set up face recognition or fingerprint recognition at the room door, but users are worried about security issues and are unwilling to give face and fingerprint permissions; there are also proposals to use password locks, but password locks also have the security issue of password leakage.

[0005] Therefore, at this stage, it is necessary to design an accommodation management system and method based on big data analysis to solve the above problems. Summary of the invention

[0006] The purpose of the present invention is to provide a check-in management system and method based on big data analysis, which is used to solve the technical problems existing in the above-mentioned prior art. In this hotel management mode, when the check-in user needs to go out, the room card needs to be carried with him, and the hotel will be powered off due to the lack of the room card. If the user asks the hotel for an extra room card, it will be inconvenient for hotel management and a large-scale occurrence will lead to an overflow of room cards and hotel security problems. There are also proposals to set up face recognition or fingerprint recognition at the door of the room, but users are worried about security issues and are unwilling to give face and fingerprint permissions; there are also proposals to propose password locks, but password locks also have the security problem of password leakage.

[0007] To achieve the above object, the technical solution of the present invention is:

[0008] A check-in management system based on big data analysis, comprising a central server and a room management unit, and also comprising a front desk management unit, wherein the central server interacts with the room management unit and the front desk management unit respectively;

[0009] The room management unit includes a password key module, a contour recognition module, a weight detection module, a height detection module, and a main control module;

[0010] The main control module is respectively connected to the password key module, the contour recognition module, the weight detection module, the height detection module, and the central server;

[0011] The password button module is used to open the room door by setting a password;

[0012] The contour detection module is used to detect the contour information of the user in real time;

[0013] The weight detection module is used to detect the user's weight information in real time;

[0014] The height detection module is used to detect the user's height information in real time;

[0015] The main control module serves as the control core of the room management unit;

[0016] Wherein, the main control module controls the password key module to be in a normally open state, and controls the weight detection module, height detection module, and contour recognition module to be in a normally closed state;

[0017] When the user passes the authentication of the password button module, the main control module controls the weight detection module and the height detection module to be turned on;

[0018] When the user passes the authentication of the weight detection module and the height detection module, the main control module determines that the current user is an authenticated user; when the user fails to pass the authentication of the weight detection module or the height detection module, the main control module controls the contour recognition module to start;

[0019] When the user passes the authentication of the contour recognition module, the main control module determines that the current user is an authenticated user.

[0020] Further, the weight detection module includes a weighing unit, a weighing boundary detection unit, and a control unit;

[0021] The control unit is connected to the weighing unit, the weighing boundary detection unit, and the main control module respectively;

[0022] The weighing unit is arranged at the door of the room and is used to obtain the real-time weight information of the user;

[0023] The weighing boundary detection unit is used to detect whether the user is located within the effective area boundary of the weighing unit;

[0024] The control unit controls the weighing boundary detection unit to be normally open, and controls the weighing unit to be normally closed;

[0025] When it is detected that the user is located within the effective area boundary of the weighing unit, the control unit controls the weighing unit to turn on.

[0026] Furthermore, it also includes an image detection module, the image detection module is connected to the main control module, and the main control module controls the image detection module to be normally closed;

[0027] When the user fails to pass the authentication of the weight detection module but passes the authentication of the height detection module, the main control module controls the image detection module to be turned on;

[0028] The image detection module is used to detect whether the user is carrying an item. If it is detected that the user is carrying an item, the main control module determines that the current user is an authenticated user.

[0029] Further, the image detection module includes an image acquisition unit, an image recognition unit, a data storage unit, an illumination detection unit, an illumination adjustment unit, and a processing unit;

[0030] The processing unit is respectively connected to the image acquisition unit, the image recognition unit, the data storage unit, the lighting detection unit, the lighting adjustment unit, and the main control module;

[0031] The data storage unit stores standard illumination data when the image acquisition unit operates normally;

[0032] The image acquisition unit is used to acquire real-time image data of the user;

[0033] The image recognition unit is used to recognize whether the user carries an item in the real-time image data;

[0034] The lighting detection unit is used to collect real-time lighting data of the environment where the user is located;

[0035] The lighting adjustment unit is used to adjust the real-time lighting data to the standard lighting data;

[0036] Wherein, the processing unit controls the lighting detection unit to be normally open, and controls the image acquisition unit, the image recognition unit, and the lighting adjustment unit to be normally closed;

[0037] When the real-time lighting data matches the standard lighting data, the processing unit controls the image acquisition unit and the image recognition unit to start;

[0038] When the real-time lighting data does not match the standard lighting data, the processing unit controls the lighting adjustment unit to turn on.

[0039] Furthermore, the weight detection module further includes a voice prompt unit, and the voice prompt unit is connected to the control unit;

[0040] The control unit controls the voice prompt unit to be normally closed;

[0041] When it is detected that the user is not within the effective area boundary of the weighing unit, the control unit controls the voice prompt unit to turn on;

[0042] The voice prompting unit is used to prompt the user to stand within the effective area boundary of the weighing unit through voice.

[0043] Furthermore, the weight detection module further includes a timing unit, and the timing unit is connected to the control unit;

[0044] The control unit controls the timing unit to be normally closed;

[0045] When the voice prompt unit is turned on, the timing unit is turned on at the same time;

[0046] When the timing duration of the timing unit reaches a preset duration and the user is still not within the effective area boundary of the weighing unit, the control unit controls the voice prompt unit to be turned off and feeds back to the front-end management unit through the central server.

[0047] A check-in management method based on big data analysis adopts a check-in management system based on big data analysis as mentioned above to perform a check-in management based on big data analysis.

[0048] A computer storage medium having a computer program stored thereon, wherein the computer program, when executed, executes an occupancy management method based on big data analysis as described above.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] One of the beneficial effects of this solution is that, through the password permission and the coordinated permission of weight and height, it is possible to avoid asking the user for fingerprint recognition permission, face recognition permission, etc. In the modern payment environment, most users bind quick payment through fingerprints, faces, etc., so users do not have to worry about fingerprint and face security issues, which greatly guarantees the experience of hotel users; and, after the user has passed the password permission authentication and the weight and height coordinated permission authentication in turn, the contour recognition authentication is also triggered, which is a verification permission authentication of the first two, further ensuring the safety of hotel users; among them, the weight and height coordinated permission and contour recognition authentication are usually not enabled, which can reduce energy consumption; this solution uniquely uses password permission authentication, weight and height coordinated permission, and contour recognition authentication as the three-binding permission authentication, which is one of the core points of the solution, with the advantages of security, convenience, and low energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This is a schematic diagram of the system structure of the implementation scheme.

[0052] Figure 2 This is a schematic diagram of the weight detection principle of the implementation scheme.

[0053] Figure 3 Schematic diagram of the image detection principle of the implementation scheme of this invention. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention, that is, the embodiments described are only part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0055] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative work are within the scope of protection of the present invention. It should be noted that relational terms such as the terms "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0056] Moreover, the terms "comprises", "includes" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or apparatus that includes a list of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or apparatus. In the absence of more restrictions, an element defined by the phrase "comprises a ..." does not exclude the presence of additional identical elements in the process, method, article or apparatus that includes the element.

[0057] This hotel management model requires that guests who stay in the hotel carry their room cards with them when they need to go out, and the hotel will experience a power outage due to the lack of room cards. If the user asks the hotel for an extra room card, it will be inconvenient for hotel management and a large-scale use of room cards will lead to an overflow of room cards and hotel security problems. There are also proposals to set up face recognition or fingerprint recognition at the room door, but users are worried about security issues and are unwilling to give face and fingerprint permissions; there are also proposals to use password locks, but password locks also have the security issue of password leakage.

[0058] like Figure 1 As shown, a check-in management system based on big data analysis is proposed, including a central server and a room management unit, and also a front desk management unit, wherein the central server interacts with the room management unit and the front desk management unit respectively;

[0059] The room management unit includes a password key module, a contour recognition module, a weight detection module, a height detection module, and a main control module;

[0060] The main control module is respectively connected to the password key module, the contour recognition module, the weight detection module, the height detection module, and the central server;

[0061] The password button module is used to open the room door by setting a password;

[0062] The contour detection module is used to detect the contour information of the user in real time;

[0063] The weight detection module is used to detect the user's weight information in real time;

[0064] The height detection module is used to detect the user's height information in real time;

[0065] The main control module serves as the control core of the room management unit;

[0066] Wherein, the main control module controls the password key module to be in a normally open state, and controls the weight detection module, height detection module, and contour recognition module to be in a normally closed state;

[0067] When the user passes the authentication of the password button module, the main control module controls the weight detection module and the height detection module to be turned on;

[0068] When the user passes the authentication of the weight detection module and the height detection module, the main control module determines that the current user is an authenticated user; when the user fails to pass the authentication of the weight detection module or the height detection module, the main control module controls the contour recognition module to start;

[0069] When the user passes the authentication of the contour recognition module, the main control module determines that the current user is an authenticated user.

[0070] In the above scheme, through the password permission and the coordination permission of weight and height, it is possible to avoid asking the user for fingerprint recognition permission, face recognition permission, etc. In the modern payment environment, most users bind quick payment through fingerprints, faces, etc., so users do not have to worry about fingerprint and face security issues, which greatly guarantees the experience of hotel users; and, after the user passes the password permission authentication and the weight and height coordination permission authentication in turn, the contour recognition authentication is also triggered, which is a verification permission authentication of the first two, further ensuring the safety of hotel users; among them, the weight and height coordination permission and contour recognition authentication are usually not enabled, which can reduce energy consumption; this scheme uniquely uses password permission authentication, weight and height coordination permission, and contour recognition authentication as the three-binding permission authentication, which is one of the core points of the scheme, and has the advantages of security, convenience, and low energy consumption.

[0071] like Figure 2 As shown, further, the weight detection module includes a weighing unit, a weighing boundary detection unit, and a control unit;

[0072] The control unit is connected to the weighing unit, the weighing boundary detection unit, and the main control module respectively;

[0073] The weighing unit is arranged at the door of the room and is used to obtain the real-time weight information of the user;

[0074] The weighing boundary detection unit is used to detect whether the user is located within the effective area boundary of the weighing unit;

[0075] The control unit controls the weighing boundary detection unit to be normally open, and controls the weighing unit to be normally closed;

[0076] When it is detected that the user is located within the effective area boundary of the weighing unit, the control unit controls the weighing unit to turn on.

[0077] In the above scheme, in order to prevent the user from being in the target weighing position during the weight detection process, a weighing boundary detection unit is designed for confirmation; at the same time, the weighing unit is opened in an orderly manner to avoid long-term invalid actions.

[0078] Furthermore, it also includes an image detection module, the image detection module is connected to the main control module, and the main control module controls the image detection module to be normally closed;

[0079] When the user fails to pass the authentication of the weight detection module but passes the authentication of the height detection module, the main control module controls the image detection module to be turned on;

[0080] The image detection module is used to detect whether the user is carrying an item. If it is detected that the user is carrying an item, the main control module determines that the current user is an authenticated user.

[0081] In the above scheme, in order to avoid abnormal weight detection caused by users carrying items, an image detection link is designed, and the image detection link is only triggered when the user's weight is abnormal but the height is normal, avoiding long-term invalid actions.

[0082] Further, the image detection module includes an image acquisition unit, an image recognition unit, a data storage unit, an illumination detection unit, an illumination adjustment unit, and a processing unit;

[0083] The processing unit is respectively connected to the image acquisition unit, the image recognition unit, the data storage unit, the lighting detection unit, the lighting adjustment unit, and the main control module;

[0084] The data storage unit stores standard illumination data when the image acquisition unit operates normally;

[0085] The image acquisition unit is used to acquire real-time image data of the user;

[0086] The image recognition unit is used to recognize whether the user carries an item in the real-time image data;

[0087] The lighting detection unit is used to collect real-time lighting data of the environment where the user is located;

[0088] The lighting adjustment unit is used to adjust the real-time lighting data to the standard lighting data;

[0089] Among them, Figure 3 As shown, the processing unit controls the lighting detection unit to be normally open, and controls the image acquisition unit, the image recognition unit, and the lighting adjustment unit to be normally closed;

[0090] When the real-time lighting data matches the standard lighting data, the processing unit controls the image acquisition unit and the image recognition unit to start;

[0091] When the real-time lighting data does not match the standard lighting data, the processing unit controls the lighting adjustment unit to turn on.

[0092] In the above scheme, since the image detection link is affected by the brightness of the user's environment, a lighting detection unit, a lighting adjustment unit, etc. are designed to overcome the interference of brightness on the image detection link.

[0093] Furthermore, the weight detection module further includes a voice prompt unit, and the voice prompt unit is connected to the control unit;

[0094] The control unit controls the voice prompt unit to be normally closed;

[0095] When it is detected that the user is not within the effective area boundary of the weighing unit, the control unit controls the voice prompt unit to turn on;

[0096] The voice prompting unit is used to prompt the user to stand within the effective area boundary of the weighing unit through voice.

[0097] In the above scheme, if the user is not within the effective area boundary of the weighing unit during the weight detection process, the weight detection process will be affected, so the design of voice prompts to the user can overcome this problem.

[0098] Furthermore, the weight detection module further includes a timing unit, and the timing unit is connected to the control unit;

[0099] The control unit controls the timing unit to be normally closed;

[0100] When the voice prompt unit is turned on, the timing unit is turned on at the same time;

[0101] When the timing duration of the timing unit reaches a preset duration and the user is still not within the effective area boundary of the weighing unit, the control unit controls the voice prompt unit to be turned off and feeds back to the front-end management unit through the central server.

[0102] In the above solution, if the user does not receive voice prompts for a long time, the front-end needs to process it to ensure the user experience.

[0103] A check-in management method based on big data analysis adopts a check-in management system based on big data analysis as mentioned above to perform a check-in management based on big data analysis.

[0104] A computer storage medium having a computer program stored thereon, wherein the computer program, when executed, executes an occupancy management method based on big data analysis as described above.

[0105] The above are preferred embodiments of the present invention. Any changes made according to the technical solution of the present invention, as long as the resulting functions do not exceed the scope of the technical solution of the present invention, belong to the protection scope of the present invention.

Claims

1. A check-in management system based on big data analysis, comprising a central server and a room management unit, characterized in that: It also includes a front desk management unit, and the central server interacts with the room management unit and the front desk management unit respectively; The room management unit includes a password key module, a contour recognition module, a weight detection module, a height detection module, and a main control module; The main control module is respectively connected to the password key module, the contour recognition module, the weight detection module, the height detection module, and the central server; The password button module is used to open the room door by setting a password; The contour detection module is used to detect the contour information of the user in real time; The weight detection module is used to detect the user's weight information in real time; The height detection module is used to detect the user's height information in real time; The main control module serves as the control core of the room management unit; Wherein, the main control module controls the password key module to be in a normally open state, and controls the weight detection module, height detection module, and contour recognition module to be in a normally closed state; When the user passes the authentication of the password button module, the main control module controls the weight detection module and the height detection module to be turned on; When the user passes the authentication of the weight detection module and the height detection module, the main control module determines that the current user is an authenticated user; when the user fails to pass the authentication of the weight detection module or the height detection module, the main control module controls the contour recognition module to start; When the user passes the authentication of the contour recognition module, the main control module determines that the current user is an authenticated user.

2. The occupancy management system based on big data analysis according to claim 1, characterized in that: The weight detection module includes a weighing unit, a weighing boundary detection unit, and a control unit; The control unit is connected to the weighing unit, the weighing boundary detection unit, and the main control module respectively; The weighing unit is arranged at the door of the room and is used to obtain the real-time weight information of the user; The weighing boundary detection unit is used to detect whether the user is located within the effective area boundary of the weighing unit; The control unit controls the weighing boundary detection unit to be normally open, and controls the weighing unit to be normally closed; When it is detected that the user is located within the effective area boundary of the weighing unit, the control unit controls the weighing unit to turn on.

3. The occupancy management system based on big data analysis according to claim 1 is characterized in that: It also includes an image detection module, which is connected to the main control module, and the main control module controls the image detection module to be normally closed; When the user fails to pass the authentication of the weight detection module but passes the authentication of the height detection module, the main control module controls the image detection module to be turned on; The image detection module is used to detect whether the user is carrying an item. If it is detected that the user is carrying an item, the main control module determines that the current user is an authenticated user.

4. The occupancy management system based on big data analysis according to claim 3 is characterized in that: The image detection module includes an image acquisition unit, an image recognition unit, a data storage unit, an illumination detection unit, an illumination adjustment unit, and a processing unit; The processing unit is respectively connected to the image acquisition unit, the image recognition unit, the data storage unit, the lighting detection unit, the lighting adjustment unit, and the main control module; The data storage unit stores standard illumination data when the image acquisition unit operates normally; The image acquisition unit is used to acquire real-time image data of the user; The image recognition unit is used to recognize whether the user carries an item in the real-time image data; The lighting detection unit is used to collect real-time lighting data of the environment where the user is located; The lighting adjustment unit is used to adjust the real-time lighting data to the standard lighting data; Wherein, the processing unit controls the lighting detection unit to be normally open, and controls the image acquisition unit, the image recognition unit, and the lighting adjustment unit to be normally closed; When the real-time lighting data matches the standard lighting data, the processing unit controls the image acquisition unit and the image recognition unit to start; When the real-time lighting data does not match the standard lighting data, the processing unit controls the lighting adjustment unit to turn on.

5. The occupancy management system based on big data analysis according to claim 2 is characterized in that: The weight detection module also includes a voice prompt unit, which is connected to the control unit; The control unit controls the voice prompt unit to be normally closed; When it is detected that the user is not within the effective area boundary of the weighing unit, the control unit controls the voice prompt unit to turn on; The voice prompting unit is used to prompt the user to stand within the effective area boundary of the weighing unit through voice.

6. The occupancy management system based on big data analysis according to claim 5, characterized in that: The weight detection module further includes a timing unit, which is connected to the control unit; The control unit controls the timing unit to be normally closed; When the voice prompt unit is turned on, the timing unit is turned on at the same time; When the timing duration of the timing unit reaches a preset duration and the user is still not within the effective area boundary of the weighing unit, the control unit controls the voice prompt unit to be turned off and feeds back to the front-end management unit through the central server.

7. A check-in management method based on big data analysis, characterized in that: A check-in management system based on big data analysis as described in any one of claims 1 to 6 is used to perform check-in management based on big data analysis.

8. A computer storage medium, characterized in that: The storage medium stores a computer program, which, when executed, executes the occupancy management method based on big data analysis as described in claim 7.

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