Risk detection method and system based on open source gap, and storage medium

Abstract

The invention discloses a risk detection method and system based on an open source gap, and a storage medium. The method comprises the following steps: obtaining operation state parameters of each gap device; wherein multi-device cooperation is established among the gap devices based on an open-source gap; carrying out risk analysis on the operation state parameters of each piece of gap equipment, and determining whether each piece of gap equipment has a risk event or not; and transmitting the detected risk event to each piece of gap equipment in the multi-equipment cooperation so as to call one or more pieces of gap equipment to process the risk event. According to the method disclosed by the invention, by utilizing the design of the swan-gap distributed soft bus, when a risk event occurs in a system of a certain swan-gap device, the risk event can be notified to each swan-gap device in the network through multi-device collaboration, and other swan-gap devices in the network are utilized to quickly process the risk event, so that the data security is ensured, and meanwhile, the data security is improved. And 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 invention relates to the fields of computer science and information technology, and in particular to a risk detection method, system and storage medium based on open source Hongmeng. Background Art

[0002] In the current network environment, terminal devices may be subject to external interference and face risks such as business interruption and data loss. The risk detection method used in the prior art is generally that each device performs risk detection of its internal system separately, and the risk detection components between each device are independent of each other. When the risky device establishes a connection with other devices, it is impossible to send the risks detected in a single terminal device to other terminal devices to achieve global risk perception. Moreover, if a single terminal device cannot handle the risk event in a timely manner, it may cause the same risk to other connected devices, making it impossible to guarantee the security performance of smart devices in the entire network.

[0003] Therefore, the methods in the prior art need to be further improved. Summary of the invention

[0004] In view of the shortcomings in the above-mentioned related technologies, the purpose of the present invention is to provide a risk detection method, system and storage medium based on the Hongmeng operating system, so as to overcome the limitations and poor accuracy of risk detection caused by the independence of risk detection between various devices in the prior art.

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

[0006] In a first aspect, this embodiment discloses a risk detection method based on open source Hongmeng, wherein the risk defense method includes:

[0007] Obtain the operating status parameters of each Hongmeng device; among them, each Hongmeng device establishes multi-device collaboration based on the open source Hongmeng;

[0008] Conduct risk analysis on the operating status parameters of each Hongmeng device to determine whether there are risk events for each Hongmeng device;

[0009] The detected risk events are transmitted to each HarmonyOS device in the multi-device collaboration to call one or more HarmonyOS devices to process the risk events.

[0010] Optionally, before the step of obtaining the operating status parameters of each Hongmeng device, the step further includes:

[0011] Get multiple Hongmeng devices connected to the same network and get a list of devices;

[0012] Filter out the HarmonyOS devices in the device list that are logged in with the same account and meet the requirements of multi-device collaboration;

[0013] Perform collaborative identity authentication on each Hongmeng device, and send a request to obtain the operating status parameters to each Hongmeng device that has passed the identity authentication;

[0014] Receive the operating status parameters returned by each Hongmeng device.

[0015] Optionally, before the step of sending a request for obtaining operating status parameters to each Hongmeng device that has passed identity authentication, the step further includes:

[0016] Get the device information of each Hongmeng device;

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

[0018] Based on the protocol parameters of the collaborative risk detection, a request for obtaining operating status parameters is sent to each Hongmeng device.

[0019] Optionally, the step of performing risk analysis on the operating status parameters of each Hongmeng device to determine whether each Hongmeng device has a risk event includes:

[0020] Determine the status parameter type corresponding to the risk analysis according to the device type of each Hongmeng device;

[0021] Filter the status analysis data corresponding to each Hongmeng device according to the determined status parameter type;

[0022] Analyze and identify the status analysis data to determine whether there are risk events for each Hongmeng device.

[0023] Optionally, the step of analyzing and identifying the status analysis data to determine whether each Hongmeng device has a risk event includes:

[0024] Input the operating status parameters of each Hongmeng device into the trained risk detection model to obtain the 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;

[0025] Determine whether there is a risk event based on the risk level determined by the risk analysis results.

[0026] Optionally, before the step of inputting the operating status parameters of each Hongmeng device into the trained risk detection model, the method further includes:

[0027] Update the data of the running status parameter samples at preset time intervals;

[0028] The parameters of the risk detection model are updated using the updated operating status parameter samples.

[0029] Optionally, 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:

[0030] Each Hongmeng device receives data information corresponding to the risk event, analyzes the data information, and obtains the event type and status abnormality data of the risk event;

[0031] Determine the cause of the risk event based on the event type and status abnormal data, and select the response device for handling the risk event from multiple Hongmeng devices based on 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 Hongmeng devices to process the risk event also includes:

[0034] Determine the warning information based on the cause of the risk event and transmit it to each Hongmeng device.

[0035] In a second aspect, this embodiment further discloses a risk detection system based on open source Hongmeng, which includes:

[0036] The status information acquisition module is used to obtain the operating status parameters of each Hongmeng device; among them, each Hongmeng device establishes multi-device collaboration based on the open source Hongmeng;

[0037] The risk assessment module is used to conduct risk analysis on the operating status parameters of each Hongmeng device to determine whether there are risk events for each Hongmeng device;

[0038] The risk processing module is used to transmit the detected risk events to each HarmonyOS device in the multi-device collaboration, so as to call one or more HarmonyOS devices to process the risk events.

[0039] In a third aspect, this embodiment discloses a computer storage medium, wherein a risk detection program based on open source Hongmeng is stored on the computer-readable storage medium, and when the risk detection program based on open source Hongmeng is executed by a processor, the steps of the risk detection method based on open source Hongmeng are implemented.

[0040] Beneficial effects:

[0041] This embodiment discloses a risk detection method, system and storage medium based on open source Hongmeng, by obtaining the operating status parameters of each Hongmeng device; wherein, each Hongmeng device establishes multi-device collaboration based on open source Hongmeng; performs risk analysis on the operating status parameters of each Hongmeng device respectively to determine whether each Hongmeng device has a risk event; transmits the detected risk event to each Hongmeng device in the multi-device collaboration to call one or more Hongmeng devices to process the risk event. The method and system of this embodiment, using the design of Hongmeng distributed soft bus, can realize that when a risk event occurs in the system of a Hongmeng device, the risk event is notified to each Hongmeng device in the network through multi-device collaboration, and other Hongmeng devices in the network are used to quickly process the risk event, so as to ensure data security while improving the globality of risk event detection, system reliability and timeliness of risk event processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is a flowchart of the steps of the risk detection method based on open source Hongmeng in an embodiment of the present invention;

[0043] Figure 2 This is a system architecture diagram of Hongmeng in an embodiment of the present invention;

[0044] Figure 3 It is a principle architecture diagram of the risk detection system in an embodiment of the present invention. DETAILED DESCRIPTION

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

[0046] In the prior art, terminal devices have become necessary tools in daily life and work. Terminal devices are not only used to process data information, but also to save important data files. Therefore, the security performance of terminal devices is particularly important for users. However, terminal devices often need to access the network when in use. There may be some risk events when accessing the network, such as encountering attacks from unknown sources outside or the information processed in the terminal device being monitored. However, the traditional risk detection method often sets a risk detection module inside each terminal device. The risk detection module in each terminal device only detects whether there is a risk on its own device. Therefore, the risk detection modules in each terminal device are not coordinated, and one terminal device cannot achieve risk detection with the help of the risk detection module set on another terminal device. In addition, due to the differences in the systems on different terminal devices, the risk detection modules installed or running in the terminal devices are also incompatible with each other, which also increases the difficulty of collaboration between risk detection modules.

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

[0048] Open source Hongmeng is an open source distributed operating system that can provide a unified software platform for various devices. Since the various 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 achieve more efficient development and management of system functions and services, while the service-oriented architecture also promotes the reusability and composability of the system. The distributed operating system based on the Hongmeng operating system can realize distributed collaboration of multiple devices to achieve timely detection and timely defense of risk events that occur in the open source Hongmeng system.

[0049] In order to overcome the above problems in the prior art, the present invention discloses a risk detection method, system and storage medium based on open source Hongmeng, and obtains the operating status parameters of each Hongmeng device; wherein, each Hongmeng device establishes multi-device collaboration based on open source Hongmeng; performs risk analysis on the operating status parameters of each Hongmeng device respectively to determine whether each Hongmeng device has a risk event; and transmits the detected risk event to each Hongmeng device in the multi-device collaboration to call one or more Hongmeng devices to process the risk event. The method and system of this embodiment, using the design of Hongmeng distributed soft bus, can realize collaborative risk detection of multiple Hongmeng devices connected to the same network. When a risk event is detected in any of the Hongmeng devices, other Hongmeng devices in the entire network can quickly defend against the detected risk event, and can call other Hongmeng devices in the network to process the risk event. Therefore, it can not only improve the globality of risk detection, but also process the risk event as soon as possible, improve the security of Hongmeng devices and the protection performance against risk events.

[0050] The following is a further description of an industry risk detection method, system and storage medium based on open source Hongmeng disclosed in this embodiment in conjunction with the accompanying drawings.

[0051] like Figure 1 As shown, this embodiment discloses a risk detection method based on open source Hongmeng, and the risk detection method includes:

[0052] Step S1: Obtain the operating status parameters of each Hongmeng device; wherein, each Hongmeng device establishes multi-device collaboration based on the open source Hongmeng.

[0053] All Hongmeng devices in this step are installed with the open source Hongmeng operating system. Since the open source Hongmeng operating system (HarmonyOS) adopts a layered hierarchical design, it can achieve collaboration between multiple devices. Figure 2 As shown, the Hongmeng operating system consists of the kernel layer, system service layer, application framework layer and application layer from bottom to top. This hierarchical design enables the Hongmeng operating system to adapt to the needs of different devices, and can tailor or add corresponding subsystems or functions according to actual needs in multi-device deployment scenarios.

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

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

[0056] Specifically, in order to better obtain the operating status parameters of each Hongmeng device, before obtaining the operating status parameters, it also includes:

[0057] Step S11, obtain multiple Hongmeng devices connected to the same network and get a device list.

[0058] Multiple Hongmeng devices can be smart devices with communication and information processing functions, such as smartphones, tablets, or computers. If Hongmeng devices establish multi-device collaboration, multiple Hongmeng devices need to be connected to the same network. Therefore, in this step, the device information of multiple Hongmeng devices connected to the same local area network is first obtained to obtain a device list consisting of these Hongmeng devices. The device information included in the device list can be the 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 of 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 the specific implementation, this step can be controlled and implemented by any one or more HarmonyOS devices among multiple HarmonyOS devices that are equipped with risk detection function. The one or more HarmonyOS devices can 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: 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.

[0061] Filter the HarmonyOS devices in the device list and determine whether they are logged in under the same account. Only when all HarmonyOS devices are logged in under the same account can multi-device collaboration be achieved. Since the account is the unique identity of the device, after logging in to the same account, cross-device collaboration can be achieved in multiple scenarios. The way to log in to an account can also include multiple methods: face recognition, account password, fingerprint, mobile phone number verification, or scan code login, etc. It can be imagined that in order to obtain an account, you need to register first, and after the registration is completed, you can get the corresponding account information.

[0062] Based on whether the HarmonyOS devices in the same local area network are logged in to the same account, select the HarmonyOS devices that meet the basic conditions for multi-device collaboration.

[0063] Step S13: perform collaborative identity authentication on each HarmonyOS device, and send a request for obtaining operating status parameters to each HarmonyOS device that has passed the identity authentication.

[0064] In order to improve the security of information transmission between HarmonyOS devices, before sending messages to other HarmonyOS devices, it is necessary to authenticate the collaborative identities of other HarmonyOS devices to ensure the correctness of information sending and receiving and the security of access.

[0065] Specifically, when a HarmonyOS device detects the need to request access to one or more other devices, it will trigger identity authentication of other devices, which may be password authentication, fingerprint or facial features, etc.

[0066] Furthermore, in order to achieve smooth communication between various Hongmeng devices of the same account, before sending a request to other Hongmeng devices, it also includes:

[0067] 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; send a request for obtaining operating status parameters to each Hongmeng device based on the protocol parameters for collaborative risk detection.

[0068] The protocol parameters required for collaborative risk detection are configured for each HarmonyOS device respectively. Each HarmonyOS device exchanges information based on the configured protocol parameters, thereby achieving safe and accurate sending and receiving of information.

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

[0070] Step S14: Receive the operating status parameters returned by each Hongmeng device.

[0071] After sending a request for obtaining the operating status parameters to each HarmonyOS device that has passed identity authentication, the HarmonyOS device that receives the request for obtaining the operating status parameters responds to the request, obtains its current operating status parameters, and sends the operating status parameters to the requesting HarmonyOS device.

[0072] Specifically, the operating status parameters include the network connection status parameters of the device and the performance parameters of the hardware. The parameters corresponding to the network connection status parameters of the device include: 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 reception and reflection 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 (adaptability to temperature and humidity or adaptability to noise and temperature rise).

[0073] Each HarmonyOS device that receives the request to send the operating status parameters obtains the operating status parameters of its device and sends the obtained operating status parameters to the HarmonyOS device that issued the request.

[0074] Step S2: Conduct risk analysis on the operating status parameters of each Hongmeng device to determine whether there are risk events for each Hongmeng device.

[0075] After receiving the operating status parameters sent by each Hongmeng device, a risk analysis is performed on the operating status parameters sent by each Hongmeng device to determine whether there is a potential risk. Specifically, the risk analysis of the operating status parameters can be implemented in different ways based on different requirements for analysis accuracy.

[0076] In one implementation, the received operating status parameters can be compared with the standard values ​​to determine whether there are any abnormalities. If there are any abnormalities, it is indicated that there may be risks. For example: if it is determined that the current temperature of the device exceeds the normal range, the device may 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 where data transmission or data processing is impossible, so it is necessary to defend against the current risks. It can be imagined that the standard value is the standard range value of the parameter when the device is operating normally. When the operating status parameter exceeds the standard range value of the parameter, it means that the current device may be in an abnormal operating state, so it is determined that the current system is in a risky state.

[0077] In another implementation, a trained neural network model can be used to assess the risk level. The acquired operating status parameters are input into the trained risk detection model to obtain the risk level output by the risk detection model, and whether a risk event exists is determined based on the risk level.

[0078] In detail, the steps of performing risk analysis on the operating status parameters of each Hongmeng device and determining whether each Hongmeng device has a risk event include:

[0079] Step S21: Determine the status parameter type corresponding to the risk analysis according to the device type of each Hongmeng device.

[0080] Since Hongmeng devices correspond to different device types and have different corresponding risk levels, in order to more accurately analyze the risks corresponding to each Hongmeng device, first determine the type of state parameters that need to be analyzed in this risk analysis based on the device type corresponding to the Hongmeng device. For example: Since mobile phones usually store a large amount of personal privacy and sensitive information, the biggest risks faced by mobile devices are data leakage, malware attacks, phishing, etc. Tablet devices are usually used for entertainment, learning, and office scenes, so the risks they face may include application compatibility issues, insufficient performance, battery life, etc. Computers are often used in life and daily work, so they not only contain a lot of important information, but may also encounter performance problems. Therefore, when the Hongmeng device is a computer, it is necessary not only to focus on data leakage and malware attacks, but also performance problems. Therefore, there are many types of state parameters that need to be considered.

[0081] Step S22: Filter the status analysis data corresponding to each Hongmeng device according to the determined status parameter type.

[0082] After determining the operating status parameters that need to be analyzed for risk analysis of each Hongmeng device, the corresponding status analysis data is filtered out from the received operating status parameters based on the determined status parameter type. For example: when the first Hongmeng device is a mobile phone, the filtered status analysis data may include: CPU usage, memory usage, battery and power consumption parameters, network traffic usage parameters, system logs and error reports and other information. When the second Hongmeng device is a computer, the filtered status analysis data may include: system resource usage related parameters, including: CPU usage, memory usage, hard disk usage status parameters (remaining disk space, read and write speed, whether there are read and write errors, whether the disk space is insufficient), battery and power consumption parameters, network traffic usage parameters (whether there are abnormal network activities), network connection (check whether there are unknown network connections or connected to an unsafe network), application behavior related parameters (check whether the permissions requested by the application are reasonable, whether the processes and services running in the process manager are abnormal or whether there are unknown processes running), system logs and error reports and other information.

[0083] Step S23: Analyze and identify the status analysis data to determine whether there are risk events for each Hongmeng device.

[0084] Based on the above-screened status analysis data, each HarmonyOS device is analyzed to determine whether there are risk events for each HarmonyOS device.

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

[0086] Specifically, when using a neural network for analysis, it is necessary to train the preset neural network to obtain the risk detection model used in this step. The step of training the preset neural network includes: first constructing the structure of the preset neural network model, and different neural networks can be selected according to the accuracy to be predicted; type, such as: fully connected neural network, convolutional neural network (CNN) or recurrent neural network (RNN). Since the operation analysis data processed in this embodiment has trainability and spatial characteristics, the preset neural network type can select 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 network layers, the number of neurons in each layer, the activation function, etc. can be determined, and the training-related hyperparameters can be set, such as: learning rate, batch size, 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 predicted result and the actual result of the final output are calculated using the loss function to adjust the model parameters of the preset neural network. The training data is input into the preset neural network after the model parameters are adjusted, and the model parameters are adjusted according to the output results. The steps are repeated until the difference between the final output result and the actual result meets the requirements.

[0089] Specifically, the training data are historical operating status parameters collected when a risk event occurs in a HarmonyOS device. One or more data in the historical operating status parameters are associated with the corresponding risk level to construct a training sample. 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 for different Hongmeng devices using the historical operating status parameters corresponding to different device types, so that there are appropriate risk detection models for different device types. For example: for mobile phones and tablets, training samples are established from the historical operating status parameters obtained from mobile phones or tablets, and then the corresponding training samples are used to train the corresponding risk detection models for mobile phones and tablets, so that the corresponding risk detection models for mobile phones and tablets can achieve the best risk detection effect for Hongmeng devices that are mobile phones and Hongmeng devices that are tablets.

[0091] Furthermore, in order to obtain more accurate detection results, before the step of inputting the operating status parameters of each Hongmeng device into the trained risk detection model, it also includes:

[0092] The data of the operating status parameter samples are updated at preset intervals; and the parameters of the risk detection model are updated using the updated operating status parameter samples.

[0093] Since the types of risk events may be different in different periods, the operating status parameter samples are updated at preset time intervals, and the parameters of the risk detection model are updated using the updated operating status parameter samples, 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 HarmonyOS device in the multi-device collaboration to call one or more HarmonyOS devices to process the risk event.

[0095] When one or more Hongmeng devices in the LAN are detected to have risks in the above steps, the risk event is transmitted to other Hongmeng devices in the LAN to achieve global perception of risk events. While sensing the risk event, other Hongmeng devices can also work with the Hongmeng device where the risk event occurs to process or defend against the risk event.

[0096] During the implementation of this step, the Hongmeng devices logged in to the same account in the local area network monitor whether there are risk events in the real-time monitoring system for detection. Risk events may include: abnormal equipment operating temperature, interruption of equipment data processing tasks, errors or slow operation, inability of the console to display data information, or abnormal reporting of data processing results, or data loss and other events. A risk detection module can be set up in the Hongmeng device system to obtain the operating status information of the device, and compare the data contained in the operating status parameters with the data of the normal operating status, and determine whether there is a risk event based on the comparison results.

[0097] Furthermore, in specific implementation, one or more Hongmeng devices in the same account in the local area network can also detect risk events in their systems at the same time. When a risk event is detected, a collaborative processing request is sent to other Hongmeng devices, and other Hongmeng devices can analyze the received collaborative processing request to determine whether to collaboratively process the risk event. The Hongmeng device that detects the risk event can also filter the Hongmeng devices that have collaborative processing requests based on the acquired device type. For example, collaborative processing requests are only sent to computer devices in the same local area network and logged in to the same device account to increase the possibility of collaborative processing.

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

[0099] Step S31: Each Hongmeng device receives data information corresponding to the risk event, parses the data information, and obtains the event type and status abnormality data of the risk event.

[0100] When one or more other HarmonyOS devices where a risk event occurs transmit the risk event detected internally to other HarmonyOS devices, each HarmonyOS device that receives the risk event needs to parse the data information to obtain the event type and status abnormal data corresponding to the risk event.

[0101] Step S32: determine the cause of the risk event based on the event type and status abnormal data, and select a response device for processing the risk event from multiple Hongmeng devices based on the cause of the risk event, and control the response device to process the risk event.

[0102] After the event type and abnormal status data corresponding to the risk event are parsed, the specific cause of the risk event can be analyzed based on the event type and abnormal status data, and then the Hongmeng device where the risk event occurs can be coordinated to handle the risk event. For example: when the event type of the risk event is parsed to be an unknown program running, and the unknown program occupies a large amount of memory, the unknown program is controlled to be closed to avoid information loss in the device.

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

[0104] Determine the warning information based on the cause of the risk event and transmit it to each Hongmeng device.

[0105] After the cause of the risk event is determined, whether an early warning is needed will be determined based on the cause of the risk event. When the risk event only involves the hardware or software version of the HarmonyOS device itself and is only related to the device, there is no need to issue an early warning for other HarmonyOS devices. If network security is involved, the risk event needs to be globally perceived by other HarmonyOS devices so that other HarmonyOS devices can take timely defensive measures.

[0106] In addition to the above risk detection, this embodiment also discloses a risk detection system based on open source Hongmeng. Figure 3 As shown, the risk detection system 30 includes:

[0107] The status information acquisition module 310 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; its function is as described in step S1.

[0108] The risk assessment module 320 is used to perform risk analysis on the operating status parameters of each Harmony device to determine whether there is a risk event for each Harmony device; its function is as described in step S2.

[0109] The risk processing module 330 is used to transmit the detected risk events to each HarmonyOS device in the multi-device collaboration to call one or more HarmonyOS devices to process the risk events. Its function is as described in step S3.

[0110] Furthermore, the system also includes: a device authentication module.

[0111] 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 to the same account and meet the requirements for multi-device collaboration, perform collaborative identity authentication on each HarmonyOS device, and send a request for obtaining operating status parameters to each HarmonyOS device that passes the authentication; receive the operating status parameters returned by each HarmonyOS device.

[0112] Furthermore, the system also includes: a protocol configuration module.

[0113] The protocol configuration module is used to obtain the device information of each Hongmeng device; configure the protocol parameters of collaborative risk detection for each Hongmeng device based on the device information of each Hongmeng device; and send an operation status parameter acquisition request to each Hongmeng device based on the protocol parameters of collaborative risk detection.

[0114] Furthermore, the risk assessment module includes: a parameter type analysis unit, a state data extraction unit and a data identification unit.

[0115] A parameter type analysis unit determines the status parameter type corresponding to the risk analysis according to the device type of each Hongmeng device;

[0116] A status data extraction unit, used to filter the status analysis data corresponding to each Hongmeng device according to the determined status parameter type;

[0117] The data identification unit is used to analyze and identify the status analysis data to determine whether there are risk events in each HarmonyOS device.

[0118] Furthermore, the data identification unit includes: a data processing subunit and a risk level determination subunit.

[0119] The data processing sub-unit is used to input the operating status parameters of each Hongmeng device into the trained risk detection model to obtain the risk analysis results 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.

[0120] The risk level determination subunit is used to determine whether a risk event exists based on the risk level determined by the risk analysis result.

[0121] Furthermore, the risk assessment module also includes: a model parameter updating unit.

[0122] The model parameter updating unit is used to update the data of the operating status parameter samples at preset intervals, and to update the parameters of the risk detection model using the updated operating status parameter samples.

[0123] Furthermore, the risk processing module includes: a data analysis unit, a device screening unit and a control processing module.

[0124] A data analysis unit is used for each Hongmeng device to receive data information corresponding to a risk event, analyze the data information, and obtain the event type and status abnormality data of the risk event;

[0125] A device screening unit, used to determine the cause of a risk event based on the event type and status abnormality data, and to screen out a response device for handling the risk event from multiple Hongmeng devices based on the cause of the risk event;

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

[0127] Furthermore, the risk processing module also includes: a global perception unit.

[0128] The global perception unit is used to determine the warning information based on the cause of the risk event and transmit the warning information to each Hongmeng device.

[0129] The method and system provided in this embodiment are aimed at the problem that in the prior art, the operating system usually only relies on independent risk detection components to implement risk detection in the system. The risk detection components in each operating system are not coordinated and compatible with each other. Therefore, there is a problem that risk events cannot be globally perceived in the network in a timely manner, and risk events occurring in the device cannot be handled in a timely manner through multi-device collaboration. A risk detection method and system based on multi-device collaboration are proposed, which utilizes the multi-device collaboration protocol of the open source Hongmeng system to achieve information sharing and collaborative work between different devices.

[0130] This embodiment discloses a computer storage medium, wherein a risk detection control program based on open source Hongmeng is stored on the computer-readable storage medium, and when the risk detection control program based on open source Hongmeng is executed by a processor, the steps of the risk detection method based on open source Hongmeng are implemented.

[0131] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0132] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A risk detection method based on open source Hongmeng, characterized in that: The risk detection method comprises: Obtain the operating status parameters of each Hongmeng device; among them, each Hongmeng device establishes multi-device collaboration based on the open source Hongmeng; Conduct risk analysis on the operating status parameters of each Hongmeng device to determine whether there are risk events for each Hongmeng device; The detected risk events are transmitted to each HarmonyOS device in the multi-device collaboration to call one or more HarmonyOS devices to process the risk events.

2. The risk detection method based on open source Hongmeng according to claim 1 is characterized in that: 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 list of devices; Filter out the HarmonyOS devices in the device list that are logged in with the same account and meet the requirements of multi-device collaboration; Perform collaborative identity authentication on each Hongmeng device, and send a request to obtain the operating status parameters to each Hongmeng device that has passed the identity authentication; Receive the operating status parameters returned by each Hongmeng device.

3. The risk detection method based on open source Hongmeng according to claim 1 is characterized in that: Before the step of sending a request for obtaining operating status parameters to each Hongmeng device that has passed identity authentication, the step further includes: Get the device information of each Hongmeng device; Configure the protocol parameters for collaborative risk detection for each HarmonyOS device based on the device information of each HarmonyOS device; Based on the protocol parameters of the collaborative risk detection, a request for obtaining operating status parameters is sent to each Hongmeng device.

4. 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 Hongmeng device to determine whether each Hongmeng device has a risk event include: Determine the status parameter type corresponding to the risk analysis according to the device type of each Hongmeng device; Filter the status analysis data corresponding to each Hongmeng device according to the determined status parameter type; Analyze and identify the status analysis data to determine whether there are risk events for each Hongmeng device.

5. The risk detection method based on open source Hongmeng according to claim 4 is characterized in that: The step of analyzing and identifying the status analysis data to determine whether each Hongmeng device has a risk event includes: Input the operating status parameters of each Hongmeng device into the trained risk detection model to obtain the 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.

6. The risk detection method based on open source Hongmeng according to claim 5 is characterized in that: Before the step of inputting the operating status parameters of each Hongmeng device into the trained risk detection model, the step further includes: Update the data of the running status parameter samples at preset time intervals; The parameters of the risk detection model are updated using the updated operating status parameter samples.

7. 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 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 abnormal data, and select the response device for handling the risk event from multiple Hongmeng devices based on the cause of the risk event; The response device is controlled to process the risk event.

8. The risk detection method based on open source Hongmeng according to claim 7 is characterized in that: The step of calling one or more Hongmeng devices to process the risk event also includes: Determine the warning information based on the cause of the risk event and transmit it to each Hongmeng device.

9. A risk detection system based on open source Hongmeng, characterized in that: include: The status information acquisition module is used to obtain the operating status parameters of each Hongmeng device; among them, each Hongmeng device establishes multi-device collaboration based on the open source Hongmeng; The risk assessment module is used to conduct risk analysis on the operating status parameters of each Hongmeng device to determine whether there are risk events for each Hongmeng device; The risk processing module is used to transmit the detected risk events to each HarmonyOS device in the multi-device collaboration, so as to call one or more HarmonyOS devices to process the risk events.

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

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