A risk detection method and system based on open source honkong and a storage medium

Abstract

The application discloses a risk detection method and system based on open source Hongmeng and a storage medium, wherein the running state parameters of each Hongmeng device are acquired; wherein multi-device cooperation is established among the Hongmeng devices based on open source Hongmeng; the running state parameters of each Hongmeng device are respectively subjected to risk analysis to determine whether a risk event exists in each Hongmeng device; and the detected risk event is transmitted to each Hongmeng device in the multi-device cooperation to call one or more Hongmeng devices to process the risk event. The method can realize that when a risk event occurs in a system of a certain Hongmeng device, the risk event is informed to each Hongmeng device in the network through multi-device cooperation, and the risk event is quickly processed by using other Hongmeng devices in the network, so that the data security is ensured, the globality of risk event detection, the reliability of the system and the timeliness of risk event processing are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of computer science and information technology, and in particular, relates to a risk detection method and system based on open-source Hongmeng and a storage medium. BACKGROUND

[0002] In the current network environment, terminal devices may be disturbed by the outside world and exist risks such as service interruption and data loss. The risk detection method used in the prior art generally detects the risk of the internal system of each device independently, and the risk detection components between devices are independent of each other. When a device with a risk establishes a connection with other devices, it is impossible to send the risk detected in a single terminal device to other terminal devices to realize the perception of risks in the global range. Moreover, if a single terminal device cannot handle the risk event in time, other connected devices may also have the same risk, so that the security performance of intelligent devices in the entire network cannot be guaranteed.

[0003] Therefore, the method in the prior art needs to be further improved. SUMMARY

[0004] In view of the deficiencies in the above related technology, the purpose of the present application is to provide a risk detection method and system based on a Hongmeng operating system and a storage medium, which overcomes the defects of poor accuracy and limitations of risk detection caused by the independent risk detection between devices in the prior art.

[0005] The technical solution adopted by the present application to solve the technical problems is as follows:

[0006] In a first aspect, the present embodiment discloses a risk detection method based on open-source Hongmeng, wherein the risk detection method comprises:

[0007] obtaining running state parameters of each Hongmeng device; wherein a multi-device cooperation is established between each Hongmeng device based on open-source Hongmeng;

[0008] performing risk analysis on the running state parameters of each Hongmeng device respectively to determine whether each Hongmeng device has a risk event;

[0009] transmitting the detected risk event to each Hongmeng device in the multi-device cooperation to call one or more Hongmeng devices to handle the risk event.

[0010] Optionally, before the step of obtaining the running state parameters of each Hongmeng device, the method further comprises:

[0011] obtaining a plurality of Hongmeng devices connected to the same network to obtain a device list;

[0012] Screening out each Harmony device in the device list under the same account that meets the multi-device collaboration;

[0013] Performing collaborative identity authentication on each Harmony device, and sending a running state parameter acquisition request to each Harmony device that passes the identity authentication;

[0014] Receiving the running state parameters returned by each Harmony device.

[0015] Optionally, before the step of sending a running state parameter acquisition request to each Harmony device that passes the identity authentication, the method further comprises:

[0016] Obtaining device information of each Harmony device;

[0017] Configuring protocol parameters for collaborative risk detection for each Harmony device based on the device information of each Harmony device;

[0018] Sending a running state parameter acquisition request to each Harmony device based on the protocol parameters for collaborative risk detection.

[0019] Optionally, the step of performing risk analysis on the running state parameters of each Harmony device respectively to determine whether each Harmony device has a risk event comprises:

[0020] Determining the type of state parameters corresponding to risk analysis according to the device type of each Harmony device;

[0021] Screening out state analysis data corresponding to each Harmony device according to the determined type of state parameters;

[0022] Analyzing and identifying the state analysis data to determine whether each Harmony device has a risk event.

[0023] Optionally, the step of analyzing and identifying the state analysis data to determine whether each Harmony device has a risk event comprises:

[0024] Inputting the running state parameters of each Harmony device into a trained risk detection model to obtain a risk analysis result output by the risk detection model; the risk detection model is trained based on the corresponding relationship between the running state parameter samples and the risk levels;

[0025] Determining whether there is a risk event according to the risk level determined by the risk analysis result.

[0026] Optionally, before the step of inputting the running state parameters of each Harmony device into a trained risk detection model, the method further comprises:

[0027] Updating the data of the running state parameter samples every predetermined time;

[0028] The parameter of the risk detection model is updated by using the updated running state parameter sample.

[0029] Optionally, the step of transmitting the detected risk event to each of the hommung devices in the multi-device cooperation to call one or more hommung devices to process the risk event comprises:

[0030] Each of the hommung devices receives data information corresponding to the risk event, analyzes the data information, and obtains the event type and state abnormal data of the risk event.

[0031] The cause of the risk event is determined according to the event type and the state abnormal data, and a response device for processing the risk event is selected from the plurality of hommung devices according to the cause of the risk event.

[0032] The response device is controlled to process the risk event.

[0033] Optionally, the step of calling one or more hommung devices to process the risk event further comprises:

[0034] The warning information is determined according to the determined cause of the risk event, and the warning information is transmitted to each of the hommung devices.

[0035] In a second aspect, the embodiment also discloses a risk detection system based on open source hommung, which comprises:

[0036] A state information acquisition module is configured to acquire running state parameters of each of the hommung devices.

[0037] A risk assessment module is configured to perform risk analysis on the running state parameters of each of the hommung devices respectively, and determine whether each of the hommung devices has a risk event.

[0038] A risk processing module is configured to transmit the detected risk event to each of the hommung devices in the multi-device cooperation to call one or more hommung devices to process the risk event.

[0039] In a third aspect, the embodiment discloses a computer storage medium, wherein the computer readable storage medium stores a risk detection program based on open source hommung.

[0040] Advantages:

[0041] The embodiment discloses a risk detection method and system based on open source Hongmeng and a storage medium. The running state parameters of each Hongmeng device are acquired. The open source Hongmeng is used to establish multi-device cooperation among the Hongmeng devices. The running state parameters of each Hongmeng device are analyzed to determine whether a risk event exists in each Hongmeng device. The detected risk event is transmitted to each Hongmeng device in the multi-device cooperation to call one or more Hongmeng devices to process the risk event. The method and system can realize the notification of the risk event to each Hongmeng device in the network through multi-device cooperation when a risk event occurs in a system of a Hongmeng device, and the risk event is quickly processed by other Hongmeng devices in the network, so that the data security is ensured, the globality of risk event detection, the reliability of the system and the timeliness of risk event processing are improved. BRIEF DESCRIPTION OF DRAWINGS

[0042] Figure 1 The step flow chart of the risk detection method based on open source Hongmeng is provided for the embodiment of the application.

[0043] Figure 2 The system architecture diagram of Hongmeng is provided for the embodiment of the application.

[0044] Figure 3 The principle architecture diagram of the risk detection system is provided for the embodiment of the application. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical scheme and advantages of the application clearer, the application is further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the application and not to limit the application.

[0046] In the prior art, terminal devices become necessary tools in daily life and work. The terminal devices are not only used to process data information, but also used to save important data files. Therefore, the security performance of the terminal devices is particularly important to the users. However, the terminal devices need to be connected to a network during use, and some risk events may exist when the terminal devices are connected to the network, such as an attack event from an unknown source or the information processed by the terminal device is monitored. However, the traditional risk detection method often sets a risk detection module in each terminal device, and the risk detection module in each terminal device only detects whether a risk exists on the device itself. Therefore, the risk detection modules in the terminal devices are not cooperative, and one terminal device cannot use the risk detection module set on another terminal device to realize risk detection. In addition, due to the differences in the systems of different terminal devices, the risk detection modules installed or running in the terminal devices are not compatible with each other, which increases the difficulty of cooperation between the risk detection modules.

[0047] Since a single terminal device detects a risk, it is necessary to diagnose the cause of the risk event first, locate the occurrence position of the risk event by analyzing the cause, and make a security defense for the risk event, so that when multiple terminal devices in the prior art process business, if one of the terminal devices detects a risk, the processing efficiency of the entire processing business will be reduced, and other connected terminal devices will also be at risk, so the security and information processing efficiency cannot be guaranteed.

[0048] Open source Hongmeng is an open source distributed operating system, which can provide a unified software platform for various devices. Since the functions and services of the system are divided into independent service units, the operating system is more flexible and efficient. The modular development method can realize more efficient development and management of system functions and services, and the service-oriented architecture also promotes the reusability and combination of the system. Based on the distributed operating system of open source Hongmeng, distributed cooperation of multiple devices can be realized to realize timely detection and timely defense of risk events occurring in the open source Hongmeng system.

[0049] In order to overcome the above problems in the prior art, the present application discloses a risk detection method, system and storage medium based on open source Hongmeng, which obtains the running state parameters of each Hongmeng device; wherein multiple device cooperation is established between each Hongmeng device based on open source Hongmeng; the running state parameters of each Hongmeng device are analyzed respectively to determine whether there is a risk event in each Hongmeng device; the detected risk event is transmitted to each Hongmeng device in the multiple device cooperation to call one or more Hongmeng devices to process the risk event. The method and system of the embodiment can realize risk detection of multiple Hongmeng devices connected to the same network in cooperation by using the design of Hongmeng distributed soft bus. When a risk event is detected in any one of the Hongmeng devices, other Hongmeng devices in the entire network can quickly defend against the detected risk event, and other Hongmeng devices in the network can be called to process the risk event. Therefore, not only the global risk detection can be improved, but also the risk event can be processed as soon as possible to improve the security of the Hongmeng device and the protection performance of the risk event.

[0050] The risk detection method, system and storage medium based on open source Hongmeng disclosed in the embodiment will be further described below with reference to the accompanying drawings.

[0051] As shown in Figure 1 The risk detection method based on open source Hongmeng disclosed in the embodiment comprises:

[0052] Step S1, obtain the running state parameters of each Hongmeng device; wherein multiple device cooperation is established between each Hongmeng device based on open source Hongmeng.

[0053] The Harmony devices in this step are all installed with the open-source Harmony operating system. Since the open-source Harmony operating system (HarmonyOS) adopts a layered hierarchical design, cooperation between multiple devices can be achieved. As shown in Figure 2 The Harmony operating system is sequentially arranged from bottom to top as a kernel layer, a system service layer, an application framework layer, and an application layer. The hierarchical design enables the Harmony operating system to adapt to different device requirements and can tailor or add corresponding subsystems or functions according to actual requirements in a multi-device deployment scenario.

[0054] As shown in Figure 3 The distributed soft bus of Harmony is located in the system service layer 10 of the Harmony operating system. The system service layer is provided with a distributed task scheduling 110, a distributed data management 120, a distributed soft bus 130, and a multi-language runtime subsystem 140, and the like. The above components collectively constitute a basic capability system set of the Harmony operating system, providing basic support for the operation, scheduling, and migration of distributed applications on multiple devices. Specifically, the distributed task scheduling is used to execute tasks on multiple nodes to achieve high-performance task scheduling; the distributed data management is used to manage the access, consistency, and completeness of data in a distributed environment, involving the synchronization of data across different locations; the distributed soft bus is used to shield the protocol differences of various devices to achieve the discovery, connection, and communication between devices; and the multi-language runtime subsystem is used to support the compilation and running of multiple programming languages, thereby supporting multiple languages.

[0055] Based on the above-mentioned distributed task scheduling, distributed data management, and distributed soft bus in the Harmony operating system, when a risk event is detected in a Harmony device, the risk event can be handled based on the above-mentioned distributed arrangement to achieve a timely response to the risk event. In addition, for different types of risk events, the best terminal device for responding to the risk event can be selected from the multiple connected Harmony devices, and the risk event can be responded to by using the best terminal device to achieve the processing of the risk event in the shortest time and restore the normal operation of the Harmony devices in the entire network.

[0056] Specifically, to better obtain the running state parameters in each Harmony device, before obtaining the running state parameters, the method further includes:

[0057] Step S11, obtaining multiple Harmony devices connected to the same network to obtain a device list.

[0058] The plurality of Harmony devices can be smart devices with communication and information processing functions, such as smartphones or tablets or computers, etc. If the Harmony devices establish multi-device collaboration, the plurality of Harmony devices need to be connected to the same network, so in this step, the device information of the plurality of Harmony devices connected to the same local area network is first obtained, and a device list composed of these Harmony devices is obtained. The device information included in the device list can be device name, device type and / or device icon, or other device-related information. Among them, the device type is a mobile phone, a computer or a tablet, the device name can be the name carried by the device when it leaves the factory or the name modified by the user, and the device icon can be an icon corresponding to the device type.

[0059] In specific implementation, this step can be controlled and implemented by any one or more of the plurality of Harmony devices installed with a risk detection function. The one or more Harmony devices obtain the device information connected to the same local area network based on their own device details, and then obtain the device list.

[0060] Step S12, screen out each Harmony device in the device list that logs in the same account and meets multi-device collaboration.

[0061] The Harmony devices in the device list are screened, and it is sequentially determined whether they are logged in the same account. Only when each Harmony device is logged in the same account can multi-device collaboration be achieved. Since the account is the unique identity of the device, logging in the same account can achieve cross-device collaboration in multiple scenarios. The login account can include various ways: face recognition, account password, fingerprint, mobile phone number verification, or scanning code login, etc. It can be thought that in order to obtain the account, registration is needed first. After registration is completed, the corresponding account information is obtained.

[0062] According to whether the Harmony devices in the same local area network are logged in the same account, the Harmony devices that meet the basic condition of multi-device collaboration are screened out.

[0063] Step S13, perform collaborative identity authentication on each Harmony device, and send a running state parameter acquisition request to each Harmony device that passes the identity authentication.

[0064] In order to improve the security of information transmission between Harmony devices, the collaborative identity of other Harmony devices also needs to be authenticated before sending messages to other Harmony devices, to ensure the correctness of information sending and receiving and the security of access.

[0065] Specifically, when the Harmony device detects a request for access to one or more other devices, identity authentication of the other devices will be triggered, which includes password authentication, fingerprint or facial features, etc.

[0066] Further, in order to realize smooth communication between various Hongmeng devices of the same account, before sending a request to other Hongmeng devices, the method further comprises:

[0067] obtaining device information of each Hongmeng device; configuring protocol parameters for collaborative risk detection for each Hongmeng device based on the device information of each Hongmeng device; and sending a running state parameter acquisition request based on the protocol parameters for collaborative risk detection.

[0068] Each Hongmeng device is configured with protocol parameters required for collaborative risk detection, and each Hongmeng device performs information interaction based on the configured protocol parameters, thereby realizing accurate sending and receiving of information security.

[0069] In detail, the protocol parameters required for collaborative risk detection include: configuring the type of communication protocol (such as TCP / IP, HTTP or HTTPS, etc.), configuring the version of the communication protocol (to ensure that each Hongmeng device participating in collaboration supports the same version of the communication protocol), configuring the data exchange format, the security protocol, the synchronous and asynchronous communication mode, and the timeout setting, etc.

[0070] Step S14, receiving the running state parameters returned by each Hongmeng device.

[0071] After sending the running state parameter acquisition request to each Hongmeng device that passes the identity authentication, the Hongmeng device that receives the running state parameter acquisition request responds to the request, obtains its current running state parameter, and sends the running state parameter to the requesting Hongmeng device.

[0072] Specifically, the running state parameter includes the network connection state parameter of the device and the performance parameter of the hardware. The network connection state parameter of the device includes: signal strength, CPU usage and memory usage of the device, remaining space of the device, power, temperature of the device, account information, fault information, etc. The performance indicators include: hardware performance indicators, communication capability performance indicators (network communication capability, information receiving and reflecting capability, data processing capability and data storage capability), security performance indicators (including encryption chip and hardware layer security protection, etc.), business processing data performance indicators or environmental performance indicators (temperature and humidity adaptability or noise and temperature rise adaptability).

[0073] Each Hongmeng device that receives the running state parameter sending request obtains the running state parameter of its device, and sends the obtained running state parameter to the Hongmeng device that issues the request.

[0074] Step S2, performing risk analysis on the running state parameters of each Hongmeng device to determine whether each Hongmeng device has a risk event.

[0075] When receiving the running state parameters sent by each Hongmeng device, the running state parameters sent by each Hongmeng device are analyzed for risks to determine whether there is a potential risk. Specifically, the risk analysis of the running state parameters can have different implementation methods based on different requirements for analysis accuracy.

[0076] In an implementation manner, whether there is an abnormality can be determined by comparing the received running state parameters with standard values, and if there is an abnormality, it is prompted that there may be a risk. For example, if it is determined that the current temperature of the device exceeds the normal range, the device may currently be in a high-temperature environment or the device is in continuous high-power operation. If it is determined that the CPU of the current device system exceeds the standard range value, the current system may be in a state that cannot perform data transmission or data processing, and therefore the current risk needs to be defended. It can be conceived that the standard value is a parameter standard range value of the device in normal operation, and when the running state parameter exceeds the parameter standard range value, it indicates that the current device can be in an abnormal operating state, and therefore it is determined that the current system is in a risk state.

[0077] In another implementation manner, the trained neural network model can be used to evaluate the risk degree. The obtained running state parameters are input into the trained risk detection model to obtain a risk level output by the risk detection model, and whether there is a risk event is determined based on the risk level.

[0078] In detail, the step of respectively analyzing the running state parameters of each Hongmeng device for risks and determining whether each Hongmeng device has a risk event includes:

[0079] In step S21, the state parameter types corresponding to the risk analysis are determined according to the device types of each Hongmeng device.

[0080] Since the device types corresponding to the Hongmeng devices are different, the corresponding risk levels are different, and therefore in order to more accurately analyze the risks corresponding to each Hongmeng device, the state parameter types that need to be analyzed in this risk analysis are determined according to the device types corresponding to the Hongmeng devices. For example, since a large amount of personal privacy and sensitive information is usually stored in a mobile phone, the biggest risk faced by the mobile phone device is data leakage, malicious software attack, phishing, etc. Since a tablet device is usually used for entertainment, learning and office scenarios, the risks it faces may include application compatibility problems, insufficient performance, battery life and other problems. Since a computer is often used in life and daily work, it not only contains a large amount of important data, but also may encounter performance problems. Therefore, when the Hongmeng device is a computer, not only data leakage and malicious software attack need to be considered, but also performance problems may be encountered, and therefore more state parameter types need to be considered.

[0081] Step S22, according to the determined state parameter type, the state analysis data corresponding to each of the various devices is screened out.

[0082] When the running state parameters required for risk analysis of each of the various devices are determined, the corresponding state analysis data is screened out from the received running state parameters based on the determined state parameter type. For example, when the first device is a mobile phone, the screened state analysis data may include: CPU usage, memory usage, battery and power consumption parameters, network traffic usage parameters, system logs and error reports, etc. When the second device is a computer, the screened state analysis data may include: system resource usage related parameters, including: CPU usage, memory usage, hard disk usage state parameters (remaining space of the disk, read / write speed, whether there is a read / write error, whether the disk space is insufficient), battery and power consumption parameters, network traffic usage parameters (whether there is abnormal network activity), network connection (check whether there is an unknown network connection or connection to an insecure network), application behavior related parameters (check whether the permissions requested by the application are reasonable, whether there are abnormal processes and services running in the process manager or whether there are unknown processes running), system logs and error reports, etc.

[0083] Step S23, analyze and identify the state analysis data to determine whether there is a risk event for each of the various devices.

[0084] According to the screened state analysis data, whether there is a risk for each of the various devices is analyzed to determine whether there is a risk event for each of the various devices.

[0085] Further, if a neural network is used to analyze the state analysis data, in order to achieve more accurate analysis results, different analysis results can be performed based on different device types and samples of running analysis data corresponding to each device type.

[0086] Specifically, when a neural network is used for analysis, the preset neural network needs to be trained to obtain the risk detection model used in this step. The steps of training the preset neural network include: first constructing the structure of the preset neural network model, and different neural networks can be selected according to the accuracy to be predicted, such as: fully connected neural network, convolutional neural network (CNN) or recurrent neural network (RNN). Since the running analysis data processed in this embodiment has training and spatial features, the type of the preset neural network can be selected as recurrent neural network (RNN) or LSTM.

[0087] According to the selected neural network type, the network structure of the preset neural network can be constructed, the number of layers of the network, the number of neurons in each layer, the activation function, etc. are determined, and the training-related hyperparameters are set, such as the learning rate, the batch size, the number of iterations, etc.

[0088] After selecting a suitable preset neural network, the training data is input into the constructed preset neural network to obtain the final output of the preset neural network. The loss function is used to calculate the prediction results and actual results of the final output to adjust the model parameters of the preset neural network. The training data is repeatedly input into the preset neural network with adjusted model parameters, and the model parameters are adjusted according to the output results until the difference between the final output results and the actual results meets the requirements.

[0089] Specifically, the training data is the historical running state parameters collected when the risk event of the Hongmeng device occurs. One or more data in the historical running state parameters are associated with the corresponding risk level, a training sample is constructed, and the training sample is input into the constructed preset neural network model. After iterative training, a trained risk detection model is obtained.

[0090] In order to obtain more accurate risk prediction results, different risk detection models can be trained using historical running state parameters corresponding to different device types for different Hongmeng devices, so that there are suitable risk detection models for different device types. For example, training samples are established from historical running state parameters obtained from mobile phones and tablets, and corresponding training samples of mobile phones and tablets are used to train risk detection models corresponding to mobile phones and tablets, respectively, so that the risk detection models corresponding to mobile phones and tablets can achieve the best risk detection effect for Hongmeng devices as mobile phones and Hongmeng devices as tablets.

[0091] Further, before the step of inputting the running state parameters of each Hongmeng device into the trained risk detection model, the method further comprises:

[0092] The data of the running state parameter sample is updated every preset time; and the parameters of the risk detection model are updated using the updated running state parameter sample.

[0093] Since the types of risk events may be different at different times, the running state parameter sample is updated every preset time, and the parameters of the risk detection model are updated using the updated running state parameter sample, so that the updated risk detection model is more suitable for risk detection of Hongmeng devices at the current stage.

[0094] Step S3, transmit the detected risk event to each of the multiple devices in the multi-device coordination to call one or more of the devices to process the risk event.

[0095] When one or more of the devices in the local area network are detected to have a risk in the above steps, the risk event is transmitted to other devices in the local area network to achieve global awareness of the risk event. Other devices can also coordinate with the devices that have the risk event to process or defend against the risk event while being aware of the risk event.

[0096] In this step, the devices in the local area network that log in to the same account in the real-time monitoring system of the devices monitor whether there is a risk event in the system. The risk event can include: abnormal device operating temperature, interruption, error or slow running of device data processing task, failure of the console to display data information, or abnormal reporting of data processing results, or data loss, etc. A risk detection module can be set in the device system to obtain the operating state information of the device, and compare the data contained in the operating state parameters with the data in the normal operating state to determine whether there is a risk event.

[0097] Further, in the specific implementation, one or more devices in the local area network that are in the same account can also detect the risk event in their system. When a risk event is detected, a cooperative processing request is sent to other devices. Other devices can analyze the received cooperative processing request to determine whether to process the risk event cooperatively. The device that detects the risk event can also filter the devices that send the cooperative processing request according to the type of the device. For example, only computer devices in the same local area network and logging in to the same device account are sent the cooperative processing request to improve the possibility of cooperative processing.

[0098] Specifically, the step of transmitting the detected risk event to each of the multiple devices in the multi-device coordination to call one or more of the devices to process the risk event includes:

[0099] Step S31, each device receives data information corresponding to the risk event, analyzes the data information, and obtains the event type and state abnormal data of the risk event.

[0100] When other one or more devices that have a risk event transmit the detected risk event to other devices, each device that receives the risk event needs to analyze the data information to obtain the event type and state abnormal data corresponding to the risk event.

[0101] Step S32, determining the cause of the risk event according to the event type and the state abnormal data, and filtering out a response device for processing the risk event from the plurality of the Harmony devices according to the cause of the risk event, and controlling the response device to process the risk event.

[0102] When the event type and the state abnormal data corresponding to the risk event are parsed, the specific cause of the risk event is analyzed based on the event type and the state abnormal data, and then the Harmony device that appears the risk event can be cooperated to process the risk event. For example, when the event type of the risk event is parsed as running of an unknown program, and the unknown program occupies a large memory, the unknown program is controlled to be closed to avoid loss of information in the device.

[0103] Specifically, the step of calling one or more Harmony devices to process the risk event further includes:

[0104] The warning information is determined according to the cause of the risk event, and the warning information is transmitted to each Harmony device.

[0105] When the cause of the risk event is determined, it is determined whether warning is needed according to the cause of the risk event. When the risk event only involves the hardware or software version of the Harmony device and is only related to the device, no warning is needed for other Harmony devices. If the network security is involved, the risk event needs to be globally perceived to other Harmony devices to enable other Harmony devices to make timely defense.

[0106] Based on the above-mentioned risk detection, the embodiment further discloses a risk detection system based on open-source Harmony, as shown in Figure 3 The risk detection system 30 includes:

[0107] The state information acquisition module 310 is configured to acquire the running state parameters of each Harmony device. The plurality of devices are cooperated based on the open-source Harmony. The function is as described in step S1.

[0108] The risk assessment module 320 is configured to perform risk analysis on the running state parameters of each Harmony device respectively, and determine whether each Harmony device has a risk event. The function is as described in step S2.

[0109] The risk processing module 330 is configured to transmit the detected risk event to each Harmony device in the multi-device cooperation, so as to call one or more Harmony devices to process the risk event. The function is as described in step S3.

[0110] Further, the system further includes a device authentication module.

[0111] The device authentication module is configured to obtain a plurality of Hongmeng devices connected to the same network, obtain a device list, filter out each Hongmeng device in the device list that logs in the same account and meets the multi-device collaboration, perform collaborative identity authentication on each Hongmeng device, and send a running state parameter acquisition request to each Hongmeng device that passes the identity authentication; and receive the running state parameters returned by each Hongmeng device.

[0112] Further, the system further comprises a protocol configuration module.

[0113] The protocol configuration module is configured to obtain device information of each Hongmeng device, configure protocol parameters for collaborative risk detection for each Hongmeng device based on the device information of each Hongmeng device, and send a running state parameter acquisition request to each Hongmeng device based on the protocol parameters for collaborative risk detection.

[0114] Further, the risk assessment module comprises a parameter type analysis unit, a state data extraction unit, and a data recognition unit.

[0115] The parameter type analysis unit determines a state parameter type corresponding to risk analysis according to the device type of each Hongmeng device.

[0116] The state data extraction unit is configured to filter out state analysis data corresponding to each Hongmeng device according to the determined state parameter type.

[0117] The data recognition unit is configured to analyze and recognize the state analysis data to determine whether there is a risk event for each Hongmeng device.

[0118] Further, the data recognition unit comprises a data processing sub-unit and a risk level determination sub-unit.

[0119] The data processing sub-unit is configured to input the running state parameters of each Hongmeng device into a trained risk detection model to obtain a risk analysis result output by the risk detection model; the risk detection model is trained based on the corresponding relationship between the running state parameter samples and the risk levels.

[0120] The risk level determination sub-unit is configured to determine whether there is a risk event according to the risk level determined by the risk analysis result.

[0121] Further, the risk assessment module further comprises a model parameter updating unit.

[0122] The model parameter updating unit is configured to update the data of the running state parameter samples every preset time and update the parameters of the risk detection model using the updated running state parameter samples.

[0123] Further, the risk processing module comprises a data analysis unit, a device screening unit and a control processing module.

[0124] The data analysis unit is configured to receive data information corresponding to the risk event from each of the devices, analyze the data information, and obtain an event type and state abnormal data of the risk event.

[0125] The device screening unit is configured to determine a cause of the risk event according to the event type and the state abnormal data, and screen a response device for processing the risk event from the plurality of devices according to the cause of the risk event.

[0126] The control processing module is configured to control the response device to process the risk event.

[0127] Further, the risk processing module further comprises a global awareness unit.

[0128] The global awareness unit is configured to determine early warning information according to the cause of the risk event, and transmit the early warning information to each of the devices.

[0129] The method and system provided in the embodiment are directed to the fact that the operating system in the prior art usually only relies on an independent risk detection component to implement risk detection in the system, the risk detection components in each of the operating systems are not coordinated and compatible, and therefore, there is a problem that the risk event cannot be globally perceived in the network in a timely manner, and the risk event occurring in the device cannot be processed in a timely manner through multi-device coordination. A risk detection method and system based on multi-device coordination are proposed to implement information sharing and coordinated work between different devices by using a multi-device coordination protocol of an open source Harmony system.

[0130] The embodiment discloses a computer storage medium, wherein the computer readable storage medium stores a risk detection control program based on an open source Harmony, and the risk detection control program based on the open source Harmony is executed by a processor to implement the steps of the risk detection method based on the open source Harmony.

[0131] In the description of the present specification, the description of the terms "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, the person skilled in the art can combine and combine the different embodiments or examples described in the present specification and the features of the different embodiments or examples without contradiction.

[0132] 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 the patent. 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 are all within the scope of the present application. Therefore, the scope of protection of the patent of the present application should be subject to the appended claims.

Claims

1. A risk detection method based on open source Hongmeng, characterized in that: The risk detection method includes: Obtain the operating status parameters of each HarmonyOS device; among them, each HarmonyOS device establishes multi-device collaboration based on the open source HarmonyOS; Conduct risk analysis on the operating status parameters of each HarmonyOS device to determine whether there are any risk events for each HarmonyOS device; Transmitting detected risk events to each HarmonyOS device in a multi-device collaboration, so as to call one or more HarmonyOS devices to process the risk events; Before the step of obtaining the operating status parameters of each Hongmeng device, the method further includes: Get multiple Hongmeng devices connected to the same network and get a device list; Filter out the HarmonyOS devices in the device list that are logged in with the same account and meet the requirements for multi-device collaboration; Perform collaborative identity authentication on each HarmonyOS device and send a request to obtain the operating status parameters to each HarmonyOS device that passes the identity authentication; Receive the operating status parameters returned by each Hongmeng device; Before the step of sending a request for obtaining operating status parameters to each Hongmeng device that has passed identity authentication, the method further includes: Get the device information of each HarmonyOS device; Configure protocol parameters for collaborative risk detection for each HarmonyOS device based on its device information; Based on the protocol parameters of the collaborative risk detection, a request for obtaining operating status parameters is sent to each HarmonyOS device.

2. The risk detection method based on open source Hongmeng according to claim 1 is characterized in that: The steps of performing risk analysis on the operating status parameters of each HarmonyOS device to determine whether a risk event exists for each HarmonyOS device include: Determine the status parameter type corresponding to the risk analysis based on the device type of each HarmonyOS device; Filter the status analysis data corresponding to each HarmonyOS device according to the determined status parameter type; Analyze and identify status analysis data to determine whether there are risk events for each HarmonyOS device.

3. The risk detection method based on open source Hongmeng according to claim 2 is characterized in that: The steps of analyzing and identifying the status analysis data to determine whether each Hongmeng device has a risk event include: Inputting the operating status parameters of each HarmonyOS device into a trained risk detection model to obtain a risk analysis result output by the risk detection model; the risk detection model is trained based on the correspondence between the operating status parameter samples and the risk level; Determine whether there is a risk event based on the risk level determined by the risk analysis results.

4. The risk detection method based on open source Hongmeng according to claim 3 is characterized in that: Before the step of inputting the operating status parameters of each HarmonyOS device into the trained risk detection model, the method further includes: Update the data of the operating status parameter sample at preset time intervals; The updated operating status parameter samples are used to update the parameters of the risk detection model.

5. The risk detection method based on open source Hongmeng according to claim 1 is characterized in that: The step of transmitting the detected risk event to each HarmonyOS device in the multi-device collaboration to call one or more HarmonyOS devices to process the risk event includes: Each Hongmeng device receives the data information corresponding to the risk event, analyzes the data information, and obtains the event type and status abnormality data of the risk event; Determine the cause of the risk event based on the event type and status abnormality data, and select the response device to handle the risk event from multiple Hongmeng devices based on the cause of the risk event; Controlling the response device to process the risk event.

6. The risk detection method based on open source Hongmeng according to claim 5 is characterized in that: The step of calling one or more Hongmeng devices to handle the risk event also includes: Determine the warning information based on the cause of the risk event and transmit it to each HarmonyOS device.

7. A risk detection system based on open source Hongmeng, characterized by: include: A status information acquisition module is used to obtain the operating status parameters of each HarmonyOS device; wherein, each HarmonyOS device establishes multi-device collaboration based on the open source HarmonyOS; The risk assessment module is used to perform risk analysis on the operating status parameters of each HarmonyOS device to determine whether there are risk events for each HarmonyOS device; A risk processing module is used to transmit detected risk events to each HarmonyOS device in a multi-device collaboration, so as to call one or more HarmonyOS devices to process the risk events; The system further includes: a device authentication module and a protocol configuration module; The device authentication module is used to obtain multiple HarmonyOS devices connected to the same network, obtain a device list, filter out the HarmonyOS devices in the device list that are logged in under the same account and meet the requirements of multi-device collaboration, perform collaborative identity authentication on each HarmonyOS device, and send a request to obtain the operating status parameters of each HarmonyOS device that passes the identity authentication; and receive the operating status parameters returned by each HarmonyOS device; The protocol configuration module is used to obtain the device information of each Harmony device; configure the protocol parameters of collaborative risk detection for each Harmony device based on the device information of each Harmony device; and send an operation status parameter acquisition request to each Harmony device based on the protocol parameters of the collaborative risk detection.

8. A computer storage medium, characterized in that The computer storage medium stores a risk detection program based on open source HarmonyOS. When the risk detection program based on open source HarmonyOS is executed by the processor, the steps of the risk detection method based on open source HarmonyOS as described in any one of claims 1 to 6 are implemented.

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