CN113535653B - Intelligent equipment risk identification method, device and IOT-based risk control system - Google Patents
Intelligent equipment risk identification method, device and IOT-based risk control system Download PDFInfo
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Abstract
本发明提供了一种智能设备风险识别方法、装置及基于IOT的风控系统，该方法包括：获取智能设备发送的设备日志；根据设备日志及预先建立的风控行为模型，确定智能设备的风险度信息；风控行为模型基于历史设备日志建立，包括以下至少一种识别子模型：设备注册频率风险识别模型、设备注册地风险识别模型、设备绑定风险识别模型、设备控制频率风险识别模型、设备信息风险识别模型。本发明基于历史设备日志预先建立风控行为模型，可以对智能设备实时上传的设备日志进行识别分析，确定其风险度信息，从而实现设备运行风险的及时监控和防范。
The present invention provides a method and device for identifying risks of smart devices and a risk control system based on IOT. The method includes: obtaining device logs sent by smart devices; determining the risk information of smart devices according to the device logs and a pre-established risk control behavior model; the risk control behavior model is established based on historical device logs, and includes at least one of the following identification sub-models: device registration frequency risk identification model, device registration location risk identification model, device binding risk identification model, device control frequency risk identification model, and device information risk identification model. The present invention pre-establishes a risk control behavior model based on historical device logs, and can identify and analyze the device logs uploaded by smart devices in real time, determine their risk information, and thus realize timely monitoring and prevention of device operation risks.
Description
Technical Field
The invention relates to the technical field of air conditioners, in particular to an intelligent equipment risk identification method and device and an air control system based on an IOT.
Background
At present, most intelligent devices report device logs to an IOT (Internet of Things ) platform of an enterprise after networking, and the device logs generally can comprise device registration information, device binding information, device control information, device state (fault) information and the like.
The IOT platform generally simply stores the log of the device, queries the log after a problem or failure occurs, and cannot monitor and prevent the running risk of the device caused by the abnormality of the device or the abnormality of the user in time.
Disclosure of Invention
The invention solves the problem that the existing IOT platform can not monitor and prevent the running risk of the equipment in time caused by the abnormality of the equipment or the abnormality of the user.
The invention provides an intelligent equipment risk identification method, which comprises the steps of obtaining equipment logs sent by intelligent equipment, wherein the equipment logs comprise at least one of equipment registration time information, equipment registration address information, equipment binding information, equipment control time information and equipment hardware information, determining risk degree information of the intelligent equipment according to the equipment logs and a pre-established wind control behavior model, and establishing the wind control behavior model based on historical equipment logs, wherein the wind control behavior model comprises at least one identification sub-model comprising an equipment registration frequency risk identification model, an equipment registration place risk identification model, an equipment binding risk identification model, an equipment control frequency risk identification model and an equipment information risk identification model.
According to the method, the wind control behavior model is built in advance based on the historical equipment logs, the equipment logs uploaded by the intelligent equipment in real time can be identified and analyzed, and the risk degree information of the equipment logs is determined, so that the running risk of the equipment is monitored and prevented in time.
Optionally, if the pre-established wind control behavior model is a device registration frequency risk identification model, determining risk degree information of the intelligent device according to the device log and the pre-established risk identification model includes determining registration frequency in a first preset period according to the device registration time information, calculating a difference value between the registration frequency and a historical registration frequency corresponding to the intelligent device based on the registration frequency risk identification model, judging whether the intelligent device is abnormal according to a comparison result of the difference value and a first difference value threshold, and determining risk degree information of the intelligent device according to a judgment result.
The device log real-time uploading method and device log real-time uploading device log identifying method and device log identifying device are based on the device registration frequency risk identifying model, and device registration frequency risk can be identified.
Optionally, if the pre-established wind control behavior model is a device registration place risk identification model, determining risk degree information of the intelligent device according to the device log and the pre-established risk identification model includes calculating a distance between the device registration address information and historical registration address information corresponding to the intelligent device based on the registration place risk identification model, judging whether the intelligent device is abnormal according to a comparison result of the distance and a first distance threshold, and determining the risk degree information of the intelligent device according to a judgment result.
The method and the system can be used for identifying and analyzing the equipment log uploaded by the intelligent equipment in real time based on the equipment registration place risk identification model, and identifying the equipment registration place risk.
Optionally, a calculation formula of the distance D2 between the device registration address information and the intelligent device corresponding history registration address information is as follows:
D2=∑(0.8)T1d(D,D1)
Wherein T1 represents the number of weeks from the current registration time to the historical registration time, D (D, D1) represents the distance difference between the historical registration place D1 and the current registration place D, D (D, D1) is 1 if the historical registration place D1 is the same as the current registration place D, otherwise D (D, D1) is 0.
Optionally, if the pre-established wind control behavior model is a device binding risk identification model, the device binding information comprises a bound user network address, the determining risk degree information of the intelligent device according to the device log and the pre-established risk identification model comprises calculating a distance between a geographic position corresponding to the bound user network address and a geographic position corresponding to the historical binding network address based on the registration binding risk identification model, judging whether the intelligent device is abnormal according to whether the distance is similar, and determining the risk degree information of the intelligent device according to a judging result.
The device binding risk identification method and device binding risk identification system based on the device binding risk identification model can identify and analyze the device log uploaded by the intelligent device in real time, and identify the device binding risk.
Optionally, if the pre-established wind control behavior model is a device control frequency risk identification model, determining risk degree information of the intelligent device according to the device log and the pre-established risk identification model includes determining a control frequency in a second preset period according to the device control time information, calculating a difference value between the control frequency and a historical control frequency corresponding to the intelligent device based on the device control frequency risk identification model, judging whether the intelligent device is abnormal according to a comparison result of the difference value and a second difference value threshold, and determining risk degree information of the intelligent device according to a judgment result.
The device control frequency risk identification method and the device control frequency risk identification system based on the device control frequency risk identification model can identify and analyze the device log uploaded by the intelligent device in real time.
Optionally, if the pre-established wind control behavior model is a device information risk identification model, the device hardware information comprises at least one of unique identification information, device module model information and device function information, the risk degree information of the intelligent device is determined according to the device log and the pre-established risk identification model, the method comprises the steps of inquiring historical device module model information and historical device function information corresponding to the unique identification information based on the device information risk identification model, judging whether the intelligent device is abnormal according to whether the device module model information is matched with the historical device module model information and whether the device function information is matched with the historical device function information, and determining the risk degree information of the intelligent device according to a judging result.
The device information risk identification method and the device information risk identification system can identify and analyze the device log uploaded by the intelligent device in real time based on the device information risk identification model, and identify the device information risk.
Optionally, the pre-established wind control behavior model comprises a plurality of recognition sub-models, and the method further comprises determining risk degree information of the intelligent device according to the number of abnormal results of the intelligent device judged by each recognition sub-model.
According to the invention, the risk degree information of the intelligent equipment is commonly determined and obtained according to the abnormal judgment results of the various recognition sub-models of the wind control behavior model, so that the accuracy of risk prediction can be improved.
The invention provides an intelligent equipment risk identification device which comprises a log acquisition module, a risk identification module and a history equipment log-based identification sub-model, wherein the log acquisition module is used for acquiring equipment logs sent by intelligent equipment, the equipment logs comprise at least one of equipment registration time information, equipment registration address information, equipment binding information, equipment control time information and equipment hardware information, the risk identification module is used for determining risk degree information of the intelligent equipment according to the equipment logs and a pre-established wind control behavior model, and the wind control behavior model is established based on the history equipment logs and comprises at least one identification sub-model including an equipment registration frequency risk identification model, an equipment registration place risk identification model, an equipment binding risk identification model, an equipment control frequency risk identification model and an equipment information risk identification model.
The invention provides an air control system based on an IOT (Internet of things), which comprises a computer readable storage medium and a processor, wherein the computer readable storage medium stores a computer program, and the computer program realizes the method when being read and run by the processor.
The present invention provides a computer readable storage medium storing a computer program which, when read and run by a processor, implements the above method.
The intelligent equipment risk identification device, the air control system based on the IOT and the computer readable storage medium can achieve the same technical effect as the intelligent equipment risk identification method.
Drawings
FIG. 1 is a schematic flow chart of a smart device risk identification method in one embodiment of the invention;
FIG. 2 is a flow chart of the wind control of smart device behavior in one embodiment of the invention;
Fig. 3 is a schematic structural diagram of a risk identification device for a smart device according to an embodiment of the present invention.
Reference numerals illustrate:
301-a log acquisition module and 302-a risk identification module.
Detailed Description
In order that the above objects, features and advantages of the invention will be readily understood, a more particular description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings.
FIG. 1 is a schematic flow chart of a smart device risk identification method in one embodiment of the invention, the method comprising:
S102, obtaining a device log sent by the intelligent device.
The intelligent equipment can be intelligent air conditioner, intelligent television and other equipment. The device log may include at least one of device registration time information, device registration address information, device binding information, device control time information, device hardware information.
The device registration address information comprises an IP (Internet Protocol ) address and a device unique identifier MAC (MEDIA ACCESS Control, physical address) of intelligent device registration, the device binding information comprises device information and user information, the device information mainly comprises the device MAC and the device IP, the user information mainly comprises the unique identifier of a user in a platform and the current IP address of the user, and the device hardware information comprises the MAC address, the device module model, the device function and the like.
S104, determining risk degree information of the intelligent equipment according to the equipment log and a pre-established wind control behavior model.
By analyzing historical equipment logs reported by intelligent equipment in advance, wind control behavior models suitable for equipment registration frequency risk identification, equipment registration place risk identification, equipment binding risk identification, equipment control frequency risk identification and equipment information identification of the intelligent equipment are constructed, and risk degree information of the intelligent equipment, such as risk early warning values, is determined through the five models.
Specifically, the pre-established wind control behavior model can comprise a device registration frequency risk identification model, a device registration place risk identification model, a device binding risk identification model, a device control frequency risk identification model and a device information risk identification model.
If the pre-established wind control behavior model comprises a plurality of recognition sub-models, risk judging results of the recognition sub-models for the intelligent equipment can be fused to improve accuracy of model to risk prediction, and based on the method, the method can further comprise the steps of judging the number of the intelligent equipment as abnormal results according to the recognition sub-models, and determining risk degree information of the intelligent equipment.
Taking 5 kinds of recognition sub-models as examples, determining that the risk value of the intelligent equipment is low risk when the judging result output by 3 kinds of recognition sub-models is abnormal, determining that the risk value of the intelligent equipment is medium risk when the judging result output by 4 kinds of recognition sub-models is abnormal, and determining that the risk value of the intelligent equipment is high risk when the judging result output by 5 kinds of recognition sub-models is abnormal.
According to the intelligent equipment risk identification method, the wind control behavior model is built in advance based on the historical equipment logs, equipment logs uploaded by the intelligent equipment in real time can be identified and analyzed, risk degree information of the equipment logs is determined, and therefore timely monitoring and prevention of equipment operation risks are achieved.
The process of risk identification for each identification sub-model is described in detail below.
(1) The device registers frequency risk identification. And establishing a device registration frequency risk identification model through calculating the change of the device registration frequency and the device history registration frequency to carry out risk identification. Specifically, the step of determining risk degree information of the smart device includes:
1. And determining the registration frequency in the first preset period according to the equipment registration time information. For example, the IOT platform obtains the number of registrations (i.e., the current registration frequency) of the smart device every 10 minutes.
2. And calculating a difference value between the registration frequency and the historical registration frequency corresponding to the intelligent device based on the registration frequency risk identification model, and judging whether the intelligent device is abnormal or not according to a comparison result of the difference value and a first difference value threshold. The method comprises the steps of determining corresponding intelligent equipment according to a unique equipment identifier (MAC), acquiring historical registration frequency of current intelligent equipment in an equipment registration historical library, and calculating a difference value R2 between the current equipment registration frequency and the historical registration frequency. The calculation formula is as follows:
R2=R(R,R1);
wherein R (R, R1) represents a difference between the historical registration frequency and the current registration frequency, and if R, R1 are equal, R (R, R1) =0.
If R2>0.2, then the device registration is determined to be abnormal, otherwise, no abnormality exists.
3. And determining risk degree information of the intelligent equipment according to the judging result. For example, if the determination result is abnormal, the risk degree information of the smart device may be determined to be medium risk or high risk.
(2) Device registry risk identification. And establishing a model through the change of the equipment registration place to perform risk identification. Specifically, the step of determining risk degree information of the smart device includes:
1. And calculating the distance between the equipment registration address information and the historical registration address information corresponding to the intelligent equipment based on the registration place risk identification model, and judging whether the intelligent equipment is abnormal according to the comparison result of the distance and the first distance threshold value. Optionally, the IOT platform obtains the IP address of the device registration and the device unique identifier MAC, determines the corresponding intelligent device according to the MAC, obtains the historical IP address of the current intelligent device from the device registration IP historic base, and calculates the distance D2 between the current device registration location and the historical device registration location. The calculation formula is as follows:
D2=∑(0.8)T1d(D,D1);
Wherein T1 represents the number of weeks from the current registration time to the historical registration time, D (D, D1) represents the distance difference between the historical registration place D1 and the current registration place D, D (D, D1) is 1 if the historical registration place D1 is the same as the current registration place D, otherwise D (D, D1) is 0.
If D2<0.6, then the device registration is determined to be abnormal, otherwise, no abnormality exists.
2. And determining risk degree information of the intelligent equipment according to the judging result.
(3) Device binding risk identification. And performing risk identification through the calculation device binding information and the reported user information building model, wherein the device binding information comprises the bound user network address. Specifically, the step of determining risk degree information of the smart device includes:
1. And calculating the distance between the geographic position corresponding to the bound user network address and the geographic position corresponding to the historical binding network address based on the registration binding risk identification model, and judging whether the intelligent device is abnormal or not according to whether the distances are similar. Optionally, the IOT platform obtains device information and user information, such as a device Mac, a device IP, a unique identifier of the user in the platform, and a currently bound user IP address, and finds the corresponding device according to the device unique identifier (Mac).
The corresponding geographic positions are obtained through historical binding IP and user IP of the intelligent equipment, whether the geographic positions are relatively recent or not is used as a basis for determining binding risks, if the geographic positions are close, whether the binding is abnormal is judged, and if the geographic positions are far away, the binding is abnormal is judged.
2. And determining risk degree information of the intelligent equipment according to the judging result.
(4) The device controls frequency risk identification. And establishing a model through calculating the change of the equipment control frequency and the equipment historical control frequency to perform risk identification. Specifically, the step of determining risk degree information of the smart device includes:
1. And determining the control frequency in the second preset period according to the equipment control time information. For example, the IOT platform obtains the number of controls (i.e., control frequency) of the device per 10 minutes.
2. And calculating a difference value between the control frequency and the corresponding historical control frequency of the intelligent device based on the device control frequency risk identification model, and judging whether the intelligent device is abnormal or not according to a comparison result of the difference value and a second difference value threshold. Optionally, a corresponding device is found according to the device MAC, the history control frequency of the current device is obtained from a device control history library, and the difference C2 between the current device control frequency and the history control frequency is calculated. Wherein the calculation formula is as follows:
C2=C(C,C1);
c (C, C1) represents the difference between the historical control frequency and the current device control frequency, and if C, C1 are equal, C (C, C1) =0.
If C2>0.5, then the device control determines that it is abnormal, otherwise, there is no abnormality.
3. And determining risk degree information of the intelligent equipment according to the judging result.
(5) And identifying equipment information. And performing risk identification by establishing a model for the change of the equipment hardware information reported by the equipment, wherein the equipment hardware information can comprise at least one item of unique identification information, equipment module model information and equipment function information. Specifically, the step of determining risk degree information of the smart device includes:
1. Inquiring historical equipment module type information and historical equipment function information corresponding to the unique identification information based on the equipment information risk identification model, and judging whether the intelligent equipment is abnormal according to whether the equipment module type information is matched with the historical equipment module type information and whether the equipment function information is matched with the historical equipment function information. Optionally, the IOT platform obtains device information, including an MAC address, a device module model, a device function, etc., searches for a corresponding device according to the device MAC, searches for information of a current device in a device information history library, matches the device MAC address, the device module model, the device function, etc., and if the matching is successful, determines that the device information is not abnormal, otherwise, is abnormal.
2. And determining risk degree information of the intelligent equipment according to the judging result.
By combining the data reported by the history of the equipment to construct an equipment registration and control wind control model and utilizing the real-time data reported by the IOT wind control platform and the equipment to calculate the risk early warning value in real time, the result can be displayed on the display interface of the IOT wind control platform in real time.
Referring to the wind control flow chart of intelligent device behaviors in fig. 2, the wind control behavior model is shown to comprise 5 recognition sub-models, namely a device registration frequency risk recognition model, a device registration place risk recognition model, a device binding risk recognition model, a device control frequency risk recognition model and a device information risk recognition model.
The identification submodels are also connected with databases corresponding to the historical equipment logs, and the databases comprise an equipment information base, an equipment history registry, an equipment history control base and an IP address base.
The wind control behavior model can output risk degree information of the intelligent equipment, including low risk, medium risk and high risk, by comparing the database data with the equipment log uploaded by the intelligent equipment in real time.
Fig. 3 is a schematic structural diagram of a risk identification device for a smart device according to an embodiment of the present invention, where the device includes:
the log obtaining module 301 is configured to obtain a device log sent by an intelligent device, where the device log includes at least one of device registration time information, device registration address information, device binding information, device control time information, and device hardware information;
The risk identification module 302 is configured to determine risk degree information of the intelligent device according to the device log and a pre-established wind control behavior model, where the wind control behavior model is established based on the historical device log, and includes at least one identification sub-model including a device registration frequency risk identification model, a device registration place risk identification model, a device binding risk identification model, a device control frequency risk identification model, and a device information risk identification model.
According to the intelligent equipment risk identification device, the wind control behavior model is built in advance based on the historical equipment logs, equipment logs uploaded by the intelligent equipment in real time can be identified and analyzed, risk degree information of the equipment logs is determined, and therefore timely monitoring and prevention of equipment operation risks are achieved.
Optionally, as a possible implementation manner, if the pre-established wind control behavior model is a device registration frequency risk identification model, the risk identification module is specifically configured to determine a registration frequency in a first preset period according to the device registration time information, calculate a difference value between the registration frequency and a historical registration frequency corresponding to the intelligent device based on the registration frequency risk identification model, determine whether the intelligent device is abnormal according to a comparison result of the difference value and a first difference value threshold, and determine risk degree information of the intelligent device according to a determination result.
Optionally, as a possible implementation manner, if the pre-established wind control behavior model is a risk identification model of a device registration place, the risk identification module is specifically configured to calculate a distance between the device registration address information and the historical registration address information corresponding to the intelligent device based on the risk identification model of the registration place, determine whether the intelligent device is abnormal according to a comparison result of the distance and a first distance threshold, and determine risk degree information of the intelligent device according to a determination result.
Alternatively, as a possible implementation manner, a calculation formula of the distance D2 between the device registration address information and the corresponding historical registration address information of the intelligent device is as follows:
D2=∑(0.8)T1d(D,D1)
Wherein T1 represents the number of weeks from the current registration time to the historical registration time, D (D, D1) represents the distance difference between the historical registration place D1 and the current registration place D, D (D, D1) is 1 if the historical registration place D1 is the same as the current registration place D, otherwise D (D, D1) is 0.
Optionally, as a possible implementation manner, if the pre-established wind control behavior model is a device binding risk identification model, the risk identification module is specifically configured to calculate a distance between a geographic location corresponding to the bound user network address and a geographic location corresponding to the historical binding network address based on the registration binding risk identification model, determine whether the intelligent device is abnormal according to whether the distance is similar, and determine risk degree information of the intelligent device according to a determination result.
Optionally, as a possible implementation manner, if the pre-established wind control behavior model is a device control frequency risk identification model, the risk identification module is specifically configured to determine a control frequency in a second preset period according to the device control time information, calculate a difference value between the control frequency and a historical control frequency corresponding to the intelligent device based on the device control frequency risk identification model, determine whether the intelligent device is abnormal according to a comparison result of the difference value and a second difference value threshold, and determine risk degree information of the intelligent device according to a determination result.
Optionally, as a possible implementation manner, if the pre-established wind control behavior model is a device information risk identification model, the device hardware information includes at least one of unique identification information, device module model information, and device function information, and the risk identification module is specifically configured to query historical device module model information and historical device function information corresponding to the unique identification information based on the device information risk identification model, and determine whether the intelligent device is abnormal according to whether the device module model information is matched with the historical device module model information and whether the device function information is matched with the historical device function information, and determine risk degree information of the intelligent device according to a determination result.
Optionally, as a feasible implementation manner, the pre-established wind control behavior model comprises a plurality of identification sub-models, and the device further comprises a fusion module, wherein the fusion module is used for determining risk degree information of the intelligent equipment according to the number of abnormal results of the intelligent equipment judged by the identification sub-models.
The embodiment of the invention also provides an air control system based on the IOT, which comprises a computer readable storage medium and a processor, wherein the computer readable storage medium stores a computer program, and the computer program realizes the method when being read and run by the processor.
The embodiment of the invention also provides a computer readable storage medium, which stores a computer program, and when the computer program is read and run by a processor, the method provided by the embodiment is realized, the same technical effect can be achieved, and the repetition is avoided, so that the description is omitted. The computer readable storage medium is, for example, a Read-Only Memory (ROM), a random access Memory (Random Access Memory RAM), a magnetic disk or an optical disk.
Of course, it will be appreciated by those skilled in the art that implementing all or part of the above-described methods in the embodiments may be implemented by a computer level to instruct a control device, where the program may be stored in a computer readable storage medium, and the program may include the above-described methods in the embodiments when executed, where the storage medium may be a memory, a magnetic disk, an optical disk, or the like.
Although the present invention is disclosed above, the present invention is not limited thereto. Various changes and modifications may be made by one skilled in the art without departing from the spirit and scope of the invention, and the scope of the invention should be assessed accordingly to that of the appended claims.
Finally, it is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises an element.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the intelligent equipment risk identification device and the air control system based on the IOT disclosed in the embodiments, the description is relatively simple because the intelligent equipment risk identification device and the air control system based on the IOT correspond to the intelligent equipment risk identification method disclosed in the embodiments, and relevant parts refer to the description of the method section.
Although the present invention is disclosed above, the present invention is not limited thereto. Various changes and modifications may be made by one skilled in the art without departing from the spirit and scope of the invention, and the scope of the invention should be assessed accordingly to that of the appended claims.
Claims (8)
1. A method for risk identification of an intelligent device, the method comprising:
Acquiring an equipment log sent by intelligent equipment, wherein the equipment log comprises at least one of equipment registration time information, equipment registration address information, equipment binding information, equipment control time information and equipment hardware information;
The risk degree information of the intelligent equipment is determined according to the equipment log and a pre-established wind control behavior model, the wind control behavior model is established based on the historical equipment log and comprises an equipment registration frequency risk identification model, an equipment registration place risk identification model, an equipment binding risk identification model, an equipment control frequency risk identification model and an equipment information risk identification model, and if the pre-established wind control behavior model is the equipment registration frequency risk identification model, the risk degree information of the intelligent equipment is determined according to the equipment log and the pre-established risk identification model, and the method comprises the following steps:
determining a registration frequency in a first preset period according to the equipment registration time information;
Calculating a difference value between the registration frequency and the historical registration frequency corresponding to the intelligent device based on the registration frequency risk identification model, and judging whether the intelligent device is abnormal or not according to a comparison result of the difference value and a first difference value threshold;
determining risk degree information of the intelligent equipment according to the judging result;
If the pre-established wind control behavior model is a device control frequency risk identification model, determining risk degree information of the intelligent device according to the device log and the pre-established risk identification model includes:
Determining a control frequency in a second preset period according to the equipment control moment information;
Calculating a difference value between the control frequency and the historical control frequency corresponding to the intelligent device based on the device control frequency risk identification model, and judging whether the intelligent device is abnormal or not according to a comparison result of the difference value and a second difference value threshold;
determining risk degree information of the intelligent equipment according to the judging result;
The pre-established wind control behavior model comprises a plurality of recognition sub-models, and the method further comprises the steps of judging the quantity of the intelligent equipment as abnormal results according to each recognition sub-model, and determining risk degree information of the intelligent equipment.
2. The method of claim 1, wherein if the pre-established wind-controlled behavior model is a risk identification model of a device registry, the determining risk information of the smart device according to the device log and the pre-established risk identification model comprises:
Calculating the distance between the equipment registration address information and the historical registration address information corresponding to the intelligent equipment based on the registration place risk identification model, and judging whether the intelligent equipment is abnormal or not according to the comparison result of the distance and a first distance threshold value;
And determining risk degree information of the intelligent equipment according to the judging result.
3. The method of claim 2, wherein a calculation formula of a distance D2 between the device registration address information and the intelligent device corresponding history registration address information is as follows:
D2=∑(0.8)T1d(D,D1)
Wherein T1 represents the number of weeks from the current registration time to the historical registration time, D (D, D1) represents the distance difference between the historical registration place D1 and the current registration place D, D (D, D1) is 1 if the historical registration place D1 is the same as the current registration place D, otherwise D (D, D1) is 0.
4. The method of claim 1, wherein if the pre-established wind-controlled behavior model is a device-binding risk identification model, the device-binding information includes a bound user network address, and determining risk information of the intelligent device according to the device log and the pre-established risk identification model comprises:
Calculating the distance between the geographic position corresponding to the bound user network address and the geographic position corresponding to the historical binding network address based on the registration binding risk recognition model, and judging whether the intelligent device is abnormal according to whether the distances are similar;
And determining risk degree information of the intelligent equipment according to the judging result.
5. The method of claim 1, wherein if the pre-established wind control behavior model is a device information risk identification model, the device hardware information includes at least one of unique identification information, device module model information, and device function information, and determining risk information of the intelligent device according to the device log and the pre-established risk identification model includes:
Inquiring historical equipment module type information and historical equipment function information corresponding to the unique identification information based on the equipment information risk identification model, and judging whether the intelligent equipment is abnormal according to whether the equipment module type information is matched with the historical equipment module type information and whether the equipment function information is matched with the historical equipment function information;
And determining risk degree information of the intelligent equipment according to the judging result.
6. An intelligent device risk identification apparatus, the apparatus comprising:
The log acquisition module is used for acquiring a device log sent by the intelligent device, wherein the device log comprises at least one of device registration time information, device registration address information, device binding information, device control time information and device hardware information;
The risk identification module is used for determining risk degree information of the intelligent equipment according to the equipment log and a pre-established wind control behavior model, wherein the wind control behavior model is established based on the historical equipment log and comprises at least one identification sub-model, namely an equipment registration frequency risk identification model, an equipment registration place risk identification model, an equipment binding risk identification model, an equipment control frequency risk identification model and an equipment information risk identification model;
if the pre-established wind control behavior model is a device registration frequency risk identification model, the risk identification module is specifically configured to:
Calculating the difference value between the registration frequency and the historical registration frequency corresponding to the intelligent equipment based on the registration frequency risk identification model, judging whether the intelligent equipment is abnormal according to the comparison result of the difference value and a first difference value threshold;
if the pre-established wind control behavior model is a device control frequency risk identification model, the risk identification module is specifically configured to:
The intelligent equipment risk degree information processing device comprises a device control time information acquisition module, a device control time information acquisition module and a risk degree information acquisition module, wherein the device control time information acquisition module is used for acquiring device control time information, determining control frequency in a second preset period according to the device control time information, calculating a difference value between the control frequency and a historical control frequency corresponding to the intelligent equipment based on the device control frequency risk identification module, judging whether the intelligent equipment is abnormal according to a comparison result of the difference value and a second difference value threshold, determining risk degree information of the intelligent equipment according to the judgment result, and a fusion module is used for determining the risk degree information of the intelligent equipment according to the quantity of judging that the intelligent equipment is abnormal result according to each identification submodel.
7. An IOT-based wind control system comprising a computer readable storage medium storing a computer program and a processor, which when read and executed by the processor, implements the method of any of claims 1-5.
8. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program which, when read and run by a processor, implements the method according to any of claims 1-5.
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