Anomaly behavior detection method, device and system

Abstract

The application relates to an abnormal behavior detection method, device and system. The method comprises the following steps: a network accommodation management platform sends check-in registration information and network accommodation access information to a video special network portrait platform; the video special network portrait platform receives historical personnel information related to abnormal behavior sent by the video special network portrait platform, wherein the historical personnel information related to abnormal behavior comprises second identity information; if the similarity of the first identity information and the second identity information is greater than a first preset threshold, access personnel abnormal information is sent to a multi-dimensional big data platform; when the multi-dimensional big data platform receives the access personnel abnormal information sent by the video special network portrait platform, it is judged whether abnormal behavior is involved in the network accommodation; if abnormal behavior is involved in the network accommodation, abnormal behavior alarm information is sent to the video special network portrait platform; and the video special network portrait platform sends the abnormal behavior alarm information to the network accommodation management platform. The problem that whether abnormal behavior is involved in the network accommodation cannot be detected is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of detection, in particular to an abnormal behavior detection method, device and system. BACKGROUND

[0002] Online house booking refers to a new form of accommodation emerging in the context of mass entrepreneurship and innovation, with the booming of internet industry innovation and sharing economy. Users can provide idle housing resources online, conduct house rental transactions, and enjoy the same convenience as online car booking. This model is known as online house booking.

[0003] Online house booking brings convenience and comfort to people, but it also cannot be ignored that it brings various problems. In the prior art, online house booking management is to verify the check-in information of the check-in user and manage the check-in registration, but it cannot manage whether abnormal behavior is involved in the online house booking.

[0004] In view of the problem in the related art that it cannot be detected whether abnormal behavior is involved in the online house booking, no effective solution has been proposed so far. SUMMARY

[0005] An abnormal behavior detection method, device and system are provided in the embodiment to solve the problem in the related art that it cannot be detected whether abnormal behavior is involved in the online house booking.

[0006] In a first aspect, an abnormal behavior detection method is provided in the embodiment, which is used to detect whether abnormal behavior is involved in the online house booking, and the method comprises,

[0007] An online house booking management platform acquires check-in registration information and online house booking access information of the online house booking, and sends the check-in registration information and the online house booking access information to a video private network portrait platform; the online house booking access information includes first identity information of a person accessing the online house booking;

[0008] The video private network portrait platform receives historical abnormal behavior-involved personnel information sent by the video private network portrait platform, the historical abnormal behavior-involved personnel information includes second identity information; the first identity information and the second identity information are compared; if the similarity of the first identity information and the second identity information is greater than a first preset threshold, access personnel abnormal information is sent to a multi-dimensional big data platform;

[0009] When the multi-dimensional big data platform receives the access personnel abnormal information sent by the video private network portrait platform, it judges whether abnormal behavior is involved in the online house booking according to the check-in registration information, the online house booking access information and a preset judgment rule; if abnormal behavior is involved in the online house booking, abnormal behavior alarm information is sent to the video private network portrait platform;

[0010] The video private network portrait platform sends the abnormal behavior alarm information to the online house management platform.

[0011] In some embodiments, the online house access information further includes a time of accessing the online house and an online house identifier.

[0012] In some embodiments, the determining whether the online house involves abnormal behavior according to the check-in registration information, the online house access information, and a preset determination rule includes:

[0013] The multi-dimensional big data platform determines a first time of staying in the online house of a person accessing the online house according to the time of accessing the online house.

[0014] The multi-dimensional big data platform performs abnormal behavior detection scoring on the online house according to third identity information in the check-in registration information, first identity information in the online house access information, and the first time according to a preset scoring rule; when the abnormal behavior detection score is greater than a second preset threshold, it is determined that the online house involves abnormal behavior.

[0015] In some embodiments, before the online house management platform obtains the check-in registration information and the online house access information of the online house, the method further includes:

[0016] The online house management platform obtains first check-in confirmation information submitted by a user terminal; verifies the check-in confirmation information with second check-in confirmation information stored in advance, and after verification, sends a check-in registration instruction to the user terminal; the user terminal completes a check-in registration process according to the check-in registration instruction.

[0017] In some embodiments, after the online house management platform sends the check-in registration instruction to the user terminal, the method further includes:

[0018] The online house management platform obtains identity verification information of a person checking in the online house, and completes verification of the identity verification information according to encrypted information in the identity verification information; after completing the verification of the identity verification information, sends a face collection instruction to the user terminal to instruct the person checking in the online house to input a face image according to the face collection instruction; receives the face image input by the user, matches the face image with the identity verification information, completes identity verification of the user, and generates check-in registration information of the online house.

[0019] In some embodiments, after the online house management platform completes the identity verification of the user, the method further includes:

[0020] The network accommodation management platform sends a password setting instruction to the user terminal, receives and stores the password sent by the user terminal.

[0021] In some embodiments, before the video private network portrait platform receives the personnel information related to abnormal behavior sent by the information network private network portrait platform, the method further comprises,

[0022] The multi-dimensional big data platform obtains personnel information related to abnormal behavior from the private network information database, and sends the personnel information to the information network private network portrait platform; and the information network private network portrait platform sends the personnel information to the video private network portrait platform.

[0023] In some embodiments, the network accommodation management platform obtains the network accommodation access information through the access control.

[0024] In a second aspect, an abnormal behavior detection method is provided in the present embodiment, which is used to detect whether there is abnormal behavior in a network accommodation, and the method comprises,

[0025] obtaining personnel information related to abnormal behavior from a private network information database, sending the personnel information to an information network private network portrait platform, and sending the personnel information to a video private network portrait platform through the information network private network portrait platform;

[0026] When receiving the abnormal information of the occupant sent by the video private network portrait platform, obtaining the check-in registration information and the network accommodation access information of the network accommodation, and determining whether there is abnormal behavior in the network accommodation according to the check-in registration information, the network accommodation access information and a preset judgment rule;

[0027] If the network accommodation involves abnormal behavior, an abnormal behavior alarm information is sent to the video private network portrait platform.

[0028] In a third aspect, an abnormal behavior detection device is provided in the present embodiment, which is used to detect whether there is abnormal behavior in a network accommodation, and the device comprises,

[0029] An information obtaining module is configured to obtain personnel information related to abnormal behavior from a private network information database, send the personnel information to an information network private network portrait platform, and send the personnel information to a video private network portrait platform through the information network private network portrait platform;

[0030] A judgment module is configured to, when receiving the abnormal information of the occupant sent by the video private network portrait platform, obtain the check-in registration information and the network accommodation access information of the network accommodation, and determine whether there is abnormal behavior in the network accommodation according to the check-in registration information, the network accommodation access information and a preset judgment rule;

[0031] The sending module is configured to send the abnormal behavior alarm information to the video private network portrait platform if the abnormal behavior is involved in the online house.

[0032] In a fourth aspect, an abnormal behavior detection system is provided in the present embodiment. The system is configured to detect whether an abnormal behavior exists in an online house. The system comprises an online house management platform, a video private network portrait platform, a multi-dimensional big data platform, an information network private network portrait platform, and an access control.

[0033] The online house management platform is connected with the access control. The online house management platform acquires online house access information of a user through the access control. The online house management platform receives abnormal behavior alarm information sent by the online house management platform.

[0034] The video private network portrait platform is connected with the online house management platform. The video private network portrait platform is connected with the information network private network portrait platform. The video private network portrait platform receives online house access information sent by the online house management platform. The video private network portrait platform receives historical personnel information related to abnormal behavior sent by the information network private network portrait platform. The video private network receives abnormal behavior alarm information sent by the multi-dimensional big data platform.

[0035] The multi-dimensional big data platform is connected with the video private network portrait platform. The multi-dimensional big data platform receives abnormal information of a resident and online house access information sent by the video private network portrait platform. The multi-dimensional big data platform acquires historical personnel information related to abnormal behavior from a private network information database. The multi-dimensional big data platform determines whether the online house involves abnormal behavior according to the online house access information.

[0036] The information network private network portrait platform is connected with the multi-dimensional big data platform. The information network private network portrait platform receives historical personnel information related to abnormal behavior sent by the multi-dimensional big data platform.

[0037] Compared with the related art, the abnormal behavior detection method, device, and system provided in the present embodiment complete the detection of abnormal behavior by transmitting the check-in registration information and online house access information to the information network private network through the video private network, and determining whether the online house involves abnormal behavior according to the check-in registration information, online house access information, and preset judgment rules. The problem that the prior art cannot detect whether an abnormal behavior is involved in an online house is solved.

[0038] The details of one or more embodiments of the present application are presented in the following drawings and description to make other features, objects, and advantages of the present application more apparent. BRIEF DESCRIPTION OF DRAWINGS

[0039] The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application. In the drawings:

[0040] Figure 1 is a flow chart of an abnormal behavior detection method of the embodiment;

[0041] Figure 2 is a flow chart of another abnormal behavior detection method of the embodiment;

[0042] Figure 3 is a structural block diagram of an abnormal behavior detection device of the embodiment;

[0043] Figure 4 is a schematic diagram of an abnormal behavior detection system of the embodiment. DETAILED DESCRIPTION

[0044] In order to more clearly understand the purpose, technical solutions and advantages of the application, the application is described and explained in detail below with reference to the drawings and embodiments.

[0045] Unless otherwise defined, technical terms or scientific terms used in the present application shall have the general meaning understood by a person skilled in the art to which the present application pertains. In the present application, "one", "a", "an", "the", "these" and similar words do not represent a quantitative limitation, but can be singular or plural. In the present application, the terms "include", "contain", "have" and any variants thereof are intended to cover non-exclusive inclusion; for example, a process, method and system, product or device containing a series of steps or modules (units) are not limited to the listed steps or modules (units), but can include steps or modules (units) not listed, or can include other steps or modules (units) inherent to the process, method, product or device. In the present application, the terms "connected", "connected", "coupled" and similar words do not limit to physical or mechanical connection, but can include electrical connection, whether direct or indirect. In the present application, "multiple" means two or more. The association between the associated objects is described by "and / or", which means that there can be three relationships, for example, "A and / or B" can mean that A exists alone, A and B exist together, and B exists alone. In general, the character " / " represents an "or" relationship between the associated objects. In the present application, the terms "first", "second", "third" and the like are only used to distinguish similar objects, and do not represent a specific order of the objects.

[0046] The method embodiment provided in the embodiment can be applied to an abnormal behavior detection system, which comprises a network accommodation management platform, a video private network portrait platform, a multi-dimensional big data platform, an information network private network portrait platform and an access control.

[0047] The system involves three networks, an operator private network, a video private network and an information network private network. The operator private network is a general Internet. The video private network is mainly a network for processing video or image information. The information network private network saves important information, such as sensitive information of citizens, and cannot be directly connected with the operator private network, but must be connected with the operator private network through the video private network. The operator private network and the video private network are isolated by a firewall, and the video private network and the information network private network are isolated by a security boundary.

[0048] The network accommodation management platform is connected with the access control, the network accommodation management platform accesses the operator private network, the operator private network is connected with the video private network through a firewall, the video private network portrait platform accesses the video private network, the video private network and the information network private network are connected through a security boundary, the multi-dimensional big data platform and the information network private network portrait platform access the information network private network, and the multi-dimensional big data platform is connected with the private network information database through a database docking gateway.

[0049] The access control is used for collecting network accommodation access information. The network accommodation management platform is used for registering and storing accommodation information and transmitting the network accommodation access information collected by the access control. The video private network portrait platform is used for comparing the pictures transmitted by the network accommodation management platform and the pictures transmitted by the information network private network portrait platform, and generating corresponding alarm information. The multi-dimensional big data platform is used for judging whether the network accommodation involves abnormal behavior and obtaining picture information from the private network information database. The information network private network portrait platform is used for obtaining picture information from the multi-dimensional big data platform and transmitting the picture information to the video private network portrait platform.

[0050] An abnormal behavior detection method is provided in the embodiment, which is used for detecting whether there is abnormal behavior in a network accommodation, Figure 1 is a flowchart of an abnormal behavior detection method of the embodiment, as Figure 1 shown, the flowchart comprises the following steps:

[0051] In step S102, the network accommodation management platform obtains accommodation registration information of the network accommodation and network accommodation access information, and sends the accommodation registration information and the network accommodation access information to the video private network portrait platform. The network accommodation access information comprises first identity information of personnel accessing the network accommodation.

[0052] The network accommodation management platform obtains the check-in registration information of the network accommodation according to the registration of the user, and the check-in registration information includes the identity information of the check-in registration personnel, such as the gender, age and occupation of the check-in registration personnel. The network accommodation management platform obtains the network accommodation access information of the network accommodation needing to be detected according to the access control system, and the network accommodation access information includes the first identity information of the personnel accessing the network accommodation, and the first identity information includes the face information of the personnel accessing the network accommodation needing to be detected, which can be obtained through the camera on the access control system. The network accommodation management platform accesses the operator private network, and after the network accommodation management platform obtains the check-in registration information and the network accommodation access information of the network accommodation, the network accommodation management platform sends the check-in registration information and the network accommodation access information of the network accommodation to the video private network portrait platform in the video private network.

[0053] In some embodiments, the network accommodation access information further includes the time of accessing the network accommodation and the network accommodation identifier, the time of accessing the network accommodation can be used to determine the time of the personnel staying in the network accommodation, and the network accommodation identifier can be the room number or the room name of the network accommodation.

[0054] In step S104, the video private network portrait platform receives the historical personnel information related to abnormal behavior sent by the information network private network portrait platform, the historical personnel information related to abnormal behavior includes the second identity information; the first identity information and the second identity information are compared; and if the similarity of the first identity information and the second identity information is greater than the first preset threshold, the access personnel abnormal information is sent to the multi-dimensional big data platform.

[0055] The video private network portrait platform receives the historical personnel information related to abnormal behavior sent by the information network private network portrait platform, and the historical personnel information related to abnormal behavior includes the second identity information, which includes the face information of the historical personnel related to abnormal behavior. The video private network portrait platform compares the face information of the first identity information with the face information in the second identity information, and when the similarity of the two is greater than the first preset threshold, it means that there is historical personnel related to abnormal behavior among the personnel accessing the network accommodation needing to be detected, and at this time the video private network portrait platform sends the access personnel abnormal information to the multi-dimensional big data platform. The multi-dimensional big data platform obtains the historical personnel information related to abnormal behavior from the private network information library, sends the personnel information to the information network private network portrait platform, and the information network private network portrait platform sends the personnel information to the video private network portrait platform.

[0056] In step S106, when the multi-dimensional big data platform receives the access personnel abnormal information sent by the video private network portrait platform, it determines whether there is abnormal behavior in the network accommodation according to the check-in registration information, the network accommodation access information and the preset judgment rule; if there is abnormal behavior in the network accommodation, the abnormal behavior alarm information is sent to the video private network portrait platform.

[0057] When the multi-dimensional big data platform receives abnormal information about people entering and leaving the video private network facial recognition platform, the multi-dimensional big data platform determines whether there is abnormal behavior in the online rental room based on the check-in registration information, the online rental room entry and exit information and the preset judgment rules. If there is abnormal behavior in the online rental room, the multi-dimensional big data platform sends abnormal behavior alarm information to the video private network facial recognition platform.

[0058] In some embodiments, the multi-dimensional big data platform determines the initial stay time and number of times individuals enter and exit the online rental room based on their entry and exit times. It also determines the gender and number of individuals entering and exiting based on their primary identity information and the entry and exit times. Furthermore, the platform uses a preset scoring system to assign an abnormal behavior detection score to the online rental room based on a third identity information from the check-in registration information, the primary identity information from the online rental room entry and exit information, the initial stay time, the number of entries and exits, and the gender and number of individuals entering and exiting. When the abnormal behavior detection score exceeds a second preset threshold, it is determined that abnormal behavior is involved in the online rental room, and the multi-dimensional big data platform sends an abnormal behavior alarm to the video private network facial recognition platform.

[0059] In step S108, the video private network facial recognition platform sends abnormal behavior alarm information to the online room management platform.

[0060] After receiving an abnormal behavior alert, the video surveillance platform forwards the alert to the ride-hailing management platform. The management platform then notifies staff to verify and review the ride-hailing service involved in the abnormal behavior.

[0061] Through the above steps, the check-in registration information and the entry and exit information of the online rental room are transmitted to the information network via a dedicated video network. The multi-dimensional big data platform uses the check-in registration information, the entry and exit information of the online rental room, and the preset judgment rules to determine whether the online rental room is suspected of abnormal behavior, thus completing the detection of abnormal behavior and solving the problem of not being able to detect whether there is abnormal behavior in the online rental room.

[0062] In some embodiments, the online accommodation management platform obtains the first check-in confirmation information submitted by the user terminal, verifies the check-in confirmation information with the pre-stored second check-in confirmation information, and sends a check-in registration instruction to the user terminal after verification. The user terminal completes the check-in registration process according to the check-in registration instruction. After sending the check-in registration instruction to the user terminal, the online accommodation management platform obtains the identity verification information of the online accommodation occupant, verifies the identity verification information according to the encrypted information in the identity verification information, and sends a face collection instruction to the user terminal after completing the verification of the identity verification information, to instruct the online accommodation occupant to input a face image according to the face collection instruction. The face image input by the user is received, and the face image is matched with the identity verification information to complete the identity verification of the user and generate the check-in registration information of the online accommodation. The online accommodation management platform sends a password setting instruction to the user terminal, receives the password sent by the user terminal, and stores the password.

[0063] In this embodiment, an abnormal behavior detection method is also provided, which is used to detect whether there is abnormal behavior in the online accommodation. Figure 2 FIG. 2 is a flowchart of another abnormal behavior detection method of this embodiment, as shown in the figure, the flow includes the following steps: Figure 2

[0064] Step S202, obtaining the historical personnel information related to abnormal behavior from the special network information base, sending the personnel information to the information network special network portrait platform, and sending the personnel information to the video special network portrait platform through the information network special network portrait platform.

[0065] Step S204, when receiving the abnormal information of the check-in personnel sent by the video special network portrait platform, obtaining the check-in registration information of the online accommodation and the online accommodation access information, and judging whether there is abnormal behavior in the online accommodation according to the check-in registration information, the online accommodation access information and the preset judgment rule.

[0066] Step S206, if there is abnormal behavior in the online accommodation, sending an abnormal behavior alarm information to the video special network portrait platform.

[0067] Through the above steps, the check-in registration information and the online accommodation access information are transmitted to the information network special network through the video special network, and the multi-dimensional big data platform judges whether the online accommodation is suspected of abnormal behavior through the check-in registration information, the online accommodation access information and the preset judgment rule, completes the detection of abnormal behavior, and solves the problem that the online accommodation cannot be detected whether it involves abnormal behavior.

[0068] The preferred embodiments will be described and explained below.

[0069] An abnormal behavior detection method of this preferred embodiment includes the following steps:

[0070] ​Step S01, information about people involved in abnormal behavior and engaged in the entertainment industry, including face photos, names and ID cards, is obtained from the information library and sent to the portrait big data platform of the information network through the multi-dimensional big data platform of the information network.

[0071] Step S02, the tenant fills in the mobile phone number and check-in code through the tenant check-in APP or applet, and the APP or applet automatically uploads the above check-in information to the online accommodation management platform for verification. The online accommodation management platform verifies the check-in information submitted by the tenant with the check-in information stored in the platform. After successful verification and confirmation of the order, the online accommodation management platform will guide the tenant to complete the identity verification and check-in registration process through the APP or applet. The tenant first swipes the ID card on the lock, and the lock reads the encrypted data in the tenant's ID card and uploads it to the online accommodation management platform. After the online accommodation management platform completes the identity verification and encrypted data decryption, it guides the tenant to complete the face comparison facing the mobile phone screen, and completes all the processes of identity verification. After the tenant's identity verification is successful, the APP or applet will guide the user to set his own lock password on the mobile phone, and then the tenant can use the password he set to open the lock during the check-in period.

[0072] Step S03, the online accommodation management platform uploads the check-in registration information and check-out information through the firewall from the operator's private network to the video private network, deploys a portrait big data application platform in the video private network, and accesses the multi-dimensional big data platform of the information network through the border gateway. The check-in registration information and check-out information include the ID number, name, check-in or check-out time, online accommodation name and room number of the check-in personnel.

[0073] Step S04, the information of the personnel entering and leaving the online accommodation is obtained through the cat eye, which includes the snapshot face photo, door opening and closing time, online accommodation name and room number. The cat eye is set on the access control system, and the information is transmitted from the operator's private network to the video private network through the firewall, and then from the video private network to the information network through the border gateway. The information is recognized by the portrait big data application platform in the video private network, and compared with the photos of people involved in abnormal behavior and engaged in the entertainment industry. The comparison result is accessed to the multi-dimensional big data platform of the information network through the border gateway.

[0074] Step S05, the multi-dimensional big data platform adopts data analysis points to automatically judge abnormal behaviors. The multi-dimensional big data platform carries out points according to the check-in registration information, the information of the personnel entering and leaving the online house and the preset point rules. When the points reach the preset value, it is judged that the online house involves abnormal behaviors, and an alarm signal is generated. For example, the personnel registered to check-in the online house is of a specific gender, more than or equal to A times of checking-in different online houses within a week, the age of the personnel registered to check-in is B-C years old, and more than D different sexes are captured by Tomcat eye on the same day to enter and leave the online house, and E points are accumulated, wherein A can be 3, B can be 20, C can be 40, D can be 3, and E can be 1; the number of different sexes entering and leaving the online house within a day is more than or equal to F people during the period when each specific gender personnel registers to check-in the online house, and G points are accumulated, wherein F can be 3 and G can be 1; the number of times of opening and closing the door is more than J times within a day during the period when the personnel not engaged in the entertainment service industry or the personnel involved in abnormal behaviors registers to check-in the online house, and K points are accumulated, wherein J can be 10 and K can be 1; the number of times of opening and closing the door is more than or equal to L times during the period when the personnel engaged in the entertainment service industry or the personnel involved in abnormal behaviors registers to check-in the online house, and M points are accumulated, wherein L can be 1 and M can be 2; the person opening and closing the door is the personnel engaged in the entertainment service industry through face comparison captured by the Tomcat eye, and the registration information of the room at this time is not registered by himself, and N points are accumulated, wherein N can be 2; the number of times of opening and closing the door is more than or equal to O times during the period when the personnel involved in abnormal behaviors registers to check-in the online house, and P points are accumulated, wherein O can be 2 and P can be 2; the person opening and closing the door is the personnel involved in abnormal behaviors through face comparison captured by the Tomcat eye, and the registration information of the room at this time is not registered by himself, and Q points are accumulated, wherein Q can be 2; the time difference between the registration check-in time of two people is within R hours, and the age difference is more than S years, and T points are accumulated, wherein R can be 2, S can be 8, and T can be 1; the points are counted within a certain period of time, and when the points are greater than U, it is judged that the online house involves abnormal behaviors, and an alarm signal is generated, wherein U can be 2. The multi-dimensional big data platform sends the alarm signal to the online house management platform of the operator's private network through the video private network. The above parameters can be modified as needed.

[0075] S06 After the online house management platform receives the alarm signal, the on-duty personnel is notified. After the on-duty personnel receives the alarm of the abnormal behaviors, the alarm is checked, and the personnel involved in abnormal behaviors is arranged to check and review.

[0076] It should be noted that the steps shown in the above process or the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from here.

[0077] In the embodiment, an abnormal behavior detection device is also provided, which is used to implement the above-mentioned embodiments and preferred embodiments, and will not be described again. The terms "module", "unit", "sub-unit" and the like used below can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware, or a combination of software and hardware is also possible and is contemplated.

[0078] Figure 3 is a structural block diagram of an abnormal behavior detection device of the embodiment, as Figure 3 shown, the device comprises:

[0079] An information acquisition module 10 is configured to acquire historical personnel information related to abnormal behavior from a private network information base, send the personnel information to an information network private network portrait platform, and send the personnel information to a video private network portrait platform through the information network private network portrait platform.

[0080] A judgment module 20 is configured to, when receiving abnormal information of an occupant sent by the video private network portrait platform, acquire check-in registration information and entry and exit information of the online house, and judge whether the online house involves abnormal behavior according to the check-in registration information, the entry and exit information of the online house, and a preset judgment rule.

[0081] A sending module 30 is configured to, if the online house involves abnormal behavior, send abnormal behavior alarm information to the video private network portrait platform.

[0082] It should be noted that each of the above modules can be a functional module or a program module, which can be implemented by software or hardware. For the modules implemented by hardware, each of the above modules can be located in the same processor; or each of the above modules can also be located in different processors in any combination.

[0083] In the embodiment, an abnormal behavior detection system is also provided, which is used to detect whether there is abnormal behavior in the online house. Figure 4 is a schematic diagram of an abnormal behavior detection system of the embodiment, as Figure 4 shown, the system comprises an online house management platform, a video private network portrait platform, a multi-dimensional big data platform, an information network private network portrait platform, an access control, a first boundary interface gateway, a second boundary interface gateway, a warehouse interface gateway, a firewall and a security boundary.

[0084] The system involves three networks, an operator private network, a video private network and an information network private network. The operator private network is a general Internet, for example, a general network accessed by a housekeeping operator. The video private network is mainly a network for processing video or image information. The information network private network saves important information, such as sensitive information of citizens, and cannot be directly connected with the operator private network, and must be connected through the video private network and the operator private network. The operator private network and the video private network are isolated by a firewall, and the video private network and the information network private network are isolated by a security boundary.

[0085] The housekeeping management platform is connected with the access control, the housekeeping management platform accesses the operator private network, the operator private network is connected with the video private network through a firewall, the video private network personage platform accesses the video private network, the video private network and the information network private network are connected through a first boundary docking gateway and a second boundary docking gateway, a security boundary is further arranged between the video private network and the information network private network, the multi-dimensional big data platform and the information network private network personage platform access the information network private network, and the multi-dimensional big data platform is connected with the private network information database through a database docking gateway.

[0086] The housekeeping management platform is connected with the access control, a peephole or a camera is arranged on the access control, and the face image of a person entering or leaving the housekeeping is obtained through the peephole or the camera. The housekeeping management platform obtains the housekeeping access information of a user through the access control, and the housekeeping management platform receives the abnormal behavior alarm information sent by the housekeeping management platform. The housekeeping management platform accesses the operator private network. The housekeeping management platform in the operator private network communicates with the video private network personage platform in the video private network through a firewall. The firewall plays a role in network isolation.

[0087] The video private network personage platform is connected with the housekeeping management platform and the information network private network personage platform; the video private network personage platform receives the housekeeping access information sent by the housekeeping management platform, the video private network personage platform receives the historical personnel information related to abnormal behavior sent by the information network private network personage platform, and the video private network receives the abnormal behavior alarm information sent by the multi-dimensional big data platform. The video private network personage platform accesses the video private network. The video private network and the information network private network are connected through a first boundary docking gateway and a second boundary docking gateway, and a security boundary is further arranged between the video private network and the information network private network. The first boundary docking gateway, the second boundary docking gateway and the security boundary play a role in network isolation.

[0088] The multi-dimensional big data platform and the video private network personage platform are connected, the multi-dimensional big data platform receives the abnormal information of the person staying and the housekeeping access information sent by the video private network personage platform, and the multi-dimensional big data platform obtains the historical personnel information related to abnormal behavior from a private network information database. The multi-dimensional big data platform judges whether the housekeeping is related to abnormal behavior according to the housekeeping access information. The multi-dimensional big data platform and the information network private network personage platform are arranged in the information network private network.

[0089] The information network private network portrait platform is connected with the multi-dimensional big data platform, the information network private network portrait platform receives personnel information related to historical abnormal behaviors sent by the multi-dimensional big data platform, and the multi-dimensional big data platform obtains the personnel information related to historical abnormal behaviors from the private network information database through the database connection gateway.

[0090] The abnormal behavior detection system of the online house is provided with a peephole for capturing the faces of people entering and leaving the online house, the check-in registration information and the check-out information are transmitted from the operator private network to the video private network through the firewall, the portrait big data application platform is deployed in the video private network, and the multi-dimensional big data platform is connected to the multi-dimensional big data platform of the information network private network through the boundary connection gateway, the multi-dimensional big data platform is connected to the information database through the database connection gateway, and the information related to abnormal criminal records, the faces of people engaged in the service industry, the names and ID cards is called.

[0091] It should be noted that the specific examples in the present embodiment can refer to the examples described in the above embodiments and optional implementation manners, and will not be described herein again.

[0092] It should be understood that the specific embodiments described herein are only used to explain the application, but not to limit it. According to the embodiments provided in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0093] Obviously, the drawings are only some examples or embodiments of the present application, and those skilled in the art can also apply the present application to other similar situations according to the drawings without creative labor. In addition, it can be understood that although the work done in the development process may be complex and long, some design, manufacture or production changes according to the technical content disclosed in the present application are only routine technical means for those skilled in the art, and should not be regarded as insufficient disclosure of the present application.

[0094] The term "embodiment" in the present application means that the specific features, structures or characteristics described in combination with the embodiment can be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily mean the same embodiment, nor does it mean independence or alternative to other embodiments. It can be clearly or implicitly understood by those skilled in the art that the embodiments described in the present application can be combined with other embodiments without conflict.

[0095] The above-described embodiments are merely illustrative of several embodiments of the present application, which are described in more detail and in a specific manner, but should not be construed as limiting the scope of patent protection. It should be noted that, for those skilled in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. An abnormal behavior detection method characterized by, The method is used for detecting whether an abnormal behavior exists in a network accommodation, and the method comprises, The network accommodation management platform acquires check-in registration information and network accommodation access information of the network accommodation, and sends the check-in registration information and the network accommodation access information to a video private network portrait platform; the network accommodation access information comprises first identity information of a person accessing the network accommodation and a time of accessing the network accommodation; The video private network portrait platform receives historical personnel information related to an abnormal behavior sent by the information network private network portrait platform, the historical personnel information related to the abnormal behavior comprises second identity information; the first identity information and the second identity information are compared; if a similarity of the first identity information and the second identity information is greater than a first preset threshold, access personnel abnormal information is sent to a multi-dimensional big data platform; When the multi-dimensional big data platform receives the access personnel abnormal information sent by the video private network portrait platform, a first time of staying in the network accommodation of a person accessing the network accommodation and a number of times of accessing the network accommodation are determined according to the time of accessing the network accommodation, a gender and a number of times of the person accessing the network accommodation are determined according to the first identity information in the network accommodation access information and the time of accessing the network accommodation, and an abnormal behavior detection score of the network accommodation is determined according to a third identity information in the check-in registration information, the first identity information in the network accommodation access information, the first time, the number of times of accessing the network accommodation, the gender and the number of times of the person accessing the network accommodation, and a preset score rule; if the abnormal behavior detection score is greater than a second preset threshold, it is judged that the network accommodation is related to an abnormal behavior; if the network accommodation is related to the abnormal behavior, abnormal behavior alarm information is sent to the video private network portrait platform; The score rule comprises, when it is detected that a check-in time difference of two persons is within a preset first number of hours and an age difference is greater than a preset second number of years, score processing is performed. The video private network portrait platform sends the abnormal behavior alarm information to the network accommodation management platform.

2. The abnormal behavior detection method of claim 1, wherein, The network accommodation access information further comprises a network accommodation identifier.

3. The abnormal behavior detection method according to any one of claims 1 to 2, characterized in that, Before the network accommodation management platform acquires the check-in registration information and the network accommodation access information of the network accommodation, the network accommodation management platform comprises, The network accommodation management platform acquires first check-in confirmation information submitted by a user terminal; the check-in confirmation information is verified with second check-in confirmation information stored in advance, and after verification, a check-in registration instruction is sent to the user terminal; the user terminal completes a check-in registration process according to the check-in registration instruction.

4. The abnormal behavior detection method according to claim 3, characterized in that, After the network accommodation management platform sends the check-in registration instruction to the user terminal, the network accommodation management platform comprises, The network accommodation management platform acquires identity verification information of the network accommodation occupant, verifies the identity verification information according to encrypted information in the identity verification information, and sends a face collection instruction to the user terminal after the verification of the identity verification information is completed, so as to instruct the network accommodation occupant to input a face image according to the face collection instruction. The face image input by the user is received, the face image is matched with the identity verification information, the identity verification of the user is completed, and the check-in registration information of the network accommodation is generated.

5. The abnormal behavior detection method of claim 4, wherein, After the identity verification of the user is completed, the method further includes, The network accommodation management platform sends a password setting instruction to the user terminal, receives and stores the password sent by the user terminal.

6. The abnormal behavior detection method of claim 1, wherein, Before the video private network portrait platform receives the historical personnel information related to abnormal behaviors sent by the information network private network portrait platform, the method further includes, The multi-dimensional big data platform acquires the historical personnel information related to abnormal behaviors from the private network information database, and sends the personnel information to the information network private network portrait platform; the information network private network portrait platform sends the personnel information to the video private network portrait platform.

7. The abnormal behavior detection method of claim 1, wherein, The network accommodation management platform acquires the network accommodation access information through access control.

8. An abnormal behavior detection method characterized by, The method is used for detecting whether abnormal behaviors exist in the network accommodation, and the method includes, acquiring historical personnel information related to abnormal behaviors from a private network information database, sending the personnel information to an information network private network portrait platform, and sending the personnel information to a video private network portrait platform through the information network private network portrait platform; When receiving the abnormal information of the occupant sent by the video private network portrait platform, the check-in registration information and the network accommodation access information of the network accommodation are acquired, the first time of staying in the network accommodation and the number of times of accessing the network accommodation of a person accessing the network accommodation are determined according to the time of accessing the network accommodation, the gender and the number of times of the person accessing the network accommodation are determined according to the first identity information in the network accommodation access information and the time of accessing the network accommodation, the third identity information in the check-in registration information, the first identity information in the network accommodation access information, the first time, the number of times of accessing the network accommodation, the gender and the number of times of the person accessing the network accommodation are used to perform abnormal behavior detection scoring of the network accommodation according to a preset scoring rule, and when the abnormal behavior detection score is greater than a second preset threshold, it is judged that the network accommodation involves abnormal behaviors. The network accommodation access information includes the time of accessing the network accommodation. The scoring rule includes that when the check-in time difference of two persons is within a preset first value of hours and the age difference is greater than a preset second value of years, scoring processing is performed. If the network accommodation involves abnormal behaviors, an abnormal behavior alarm information is sent to the video private network portrait platform.

9. An abnormal behavior detection apparatus characterized by comprising: The device is used for detecting whether abnormal behaviors exist in the network accommodation, and the device includes, an information acquisition module, configured to acquire historical personnel information related to abnormal behaviors from a private network information database, send the personnel information to an information network private network portrait platform, and send the personnel information to a video private network portrait platform through the information network private network portrait platform; The judgment module is configured to, when receiving the abnormal information of the occupant sent by the video private network portrait platform, acquire the check-in registration information and the information of entering and leaving the online house, determine the first time of staying in the online house and the number of times of entering and leaving the online house according to the time of entering and leaving the online house, determine the gender and the number of times of the personnel entering and leaving the online house according to the first identity information in the information of entering and leaving the online house and the time of entering and leaving the online house, and perform abnormal behavior detection scoring on the online house according to the third identity information in the check-in registration information, the first identity information in the information of entering and leaving the online house, the first time, the number of times of entering and leaving the online house, the gender and the number of times of the personnel entering and leaving the online house, and the preset scoring rule; when the abnormal behavior detection score is greater than a second preset threshold, it is determined that the online house involves abnormal behavior; the information of entering and leaving the online house includes the time of entering and leaving the online house. The scoring rule includes that when it is detected that the check-in time difference of two people is within a preset first number of hours and the age difference is greater than a preset second number of years, the scoring processing is performed. The sending module is configured to, if the online house involves abnormal behavior, send the abnormal behavior alarm information to the video private network portrait platform.

10. An anomaly behavior detection system characterized by, The system is used for detecting whether there is abnormal behavior in the online house, and includes an online house management platform, a video private network portrait platform, a multi-dimensional big data platform, an information network private network portrait platform, and an access control; The online house management platform is connected with the access control; the online house management platform acquires the information of entering and leaving the online house of a user through the access control; the online house management platform receives the abnormal behavior alarm information sent by the online house management platform; The video private network portrait platform is connected with the online house management platform, and is connected with the information network private network portrait platform; the video private network portrait platform receives the information of entering and leaving the online house sent by the online house management platform; the video private network portrait platform receives the personnel information related to abnormal behavior sent by the information network private network portrait platform; and the video private network receives the abnormal behavior alarm information sent by the multi-dimensional big data platform. The multi-dimensional big data platform and the video private network portrait platform are connected, the multi-dimensional big data platform receives the abnormal information of the person in the room and the information of the entry and exit of the online house sent by the video private network portrait platform, the multi-dimensional big data platform obtains the information of the person related to the abnormal behavior from the private network information base; the information of the entry and exit of the online house includes the time of the entry and exit of the online house; the multi-dimensional big data platform determines the first time of the person staying in the online house and the number of times of the entry and exit of the online house according to the time of the entry and exit of the online house, determines the gender and the number of times of the person according to the first identity information in the information of the entry and exit of the online house and the time of the entry and exit of the online house, determines the abnormal behavior detection score of the online house according to the third identity information in the check-in information, the first identity information in the information of the entry and exit of the online house, the first time, the number of times of the entry and exit of the online house, the gender and the number of times of the person, and the preset score rule; when the abnormal behavior detection score is greater than the second preset threshold, it is judged that the online house is related to the abnormal behavior; The score rule includes, when it is detected that the check-in time difference of two people is within a preset first value of hours and the age difference is more than a preset second value of years, the score processing is carried out; The information network private network portrait platform and the multi-dimensional big data platform are connected, and the information network private network portrait platform receives the information of the person related to the abnormal behavior sent by the multi-dimensional big data platform.

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