A risk monitoring method and device

Abstract

The application discloses a risk monitoring method and device, and relates to the technical field of computers, wherein the method comprises the following steps: firstly, acquiring computing power grid connection process data corresponding to a node device and selecting to-be-analyzed node data in a preset time period from the computing power grid connection process data; processing the to-be-analyzed node data through a preset risk prediction model to generate an initial risk value corresponding to the node device; adjusting the initial risk value according to the computing power grid connection process data to obtain a target risk value meeting a preset risk condition; and performing risk monitoring on the node device based on the target risk value to determine a risk level corresponding to the node device in the computing power grid connection process. Compared with the prior art, the application effectively reduces risk loss, reduces manual participation, reduces the labor cost of risk management, improves the efficiency and quality of risk management, and provides a powerful guarantee for the safety and stability of computing power grid connection.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer, in particular to a risk monitoring method and device. BACKGROUND

[0002] Computing power grid connection is an innovative technology system and service mode based on computing power measurement, general computing scheduling and trusted transaction, aiming to give full play to the advantages of computing power grid, widely gather multi-party computing power, and promote the innovation of computing power popularization and efficient service. It uses decentralized technologies such as blockchains to uniformly register and manage idle computing power resources, multi-party computing power resources and computing power services, and realizes unified operation of distributed computing power.

[0003] At present, the risk monitoring of the computing power grid connection process is mainly through manual experience to make risk judgment at the risk high-occurrence place or risk high-occurrence time, so as to achieve the purpose of risk monitoring of the computing power grid connection process.

[0004] However, in the case of complex network environment and large number of node devices, using this risk monitoring method will lead to the inability to timely determine the risk situation of all node devices, and thus the risk monitoring of each node device cannot be achieved, affecting the efficiency and quality of computing power grid connection. SUMMARY

[0005] Therefore, the present application provides a risk monitoring method and device, which aims to solve the technical problem that the existing technology cannot timely determine the risk situation of all node devices in the case of complex network environment and large number of node devices, and thus cannot achieve the risk monitoring of each node device, affecting the efficiency and quality of computing power grid connection.

[0006] In a first aspect, the present application provides a risk monitoring method, comprising:

[0007] obtaining computing power grid connection process data corresponding to a node device and selecting node data to be analyzed in a preset time period from the computing power grid connection process data;

[0008] processing the node data to be analyzed by a preset risk prediction model to generate an initial risk value corresponding to the node device;

[0009] adjusting the initial risk value according to the computing power grid connection process data to obtain a target risk value meeting a preset risk condition;

[0010] based on the target risk value, monitoring the risk of the node device to determine the risk level of the node device in the computing power grid connection process.

[0011] In a second aspect, the present application provides a risk monitoring device, comprising:

[0012] The acquisition module is configured to acquire computing power grid connection process data corresponding to the node device and select node data to be analyzed within a preset time period from the computing power grid connection process data;

[0013] The generation module is configured to process the data of the node to be analyzed through a preset risk prediction model to generate the initial risk value corresponding to the node device;

[0014] The adjustment module is configured to adjust the initial risk value based on the computing power grid connection process data to obtain a target risk value that meets preset risk conditions;

[0015] The monitoring module is configured to perform risk monitoring on the node device based on the target risk value, and determine the risk level of the node device during the computing power grid connection process.

[0016] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the risk monitoring method of the first aspect.

[0017] Fourthly, this application provides an electronic device, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein the processor executes the computer program to implement the risk monitoring method of the first aspect.

[0018] Fifthly, this application provides a computer program product, which includes a computer program that, when executed by a processor, implements the risk monitoring method of the first aspect.

[0019] Using the above technical solution, this application provides a risk monitoring method and apparatus, which first acquires computing power grid connection process data corresponding to node devices and selects node data to be analyzed within a preset time period from the computing power grid connection process data; processes the node data to be analyzed through a preset risk prediction model to generate an initial risk value corresponding to the node device; adjusts the initial risk value according to the computing power grid connection process data to obtain a target risk value that meets preset risk conditions; and performs risk monitoring on the node device based on the target risk value to determine the risk level corresponding to the node device in the computing power grid connection process. Compared with existing technologies, this application obtains the initial risk value of the node device by using a preset risk prediction model to analyze the node data within a preset time period. Then, it adjusts the initial risk value based on the computing power grid connection process data of the node device to obtain the target risk value that meets the preset risk conditions. Based on the target risk value, risk monitoring of the node device can more accurately identify and assess various risks in the computing power grid connection process. It can also accurately monitor the risks of each node device participating in the computing power grid connection process, effectively reducing risk losses in the computing power grid connection process. It can also reduce manual intervention, lower the manpower cost of risk management, and improve the efficiency and quality of risk management, providing a strong guarantee for the security and stability of computing power grid connection.

[0020] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0021] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0022] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 A flowchart illustrating a risk monitoring method provided in an embodiment of this application is shown;

[0024] Figure 2 A flowchart illustrating a risk monitoring method provided in an embodiment of this application is shown;

[0025] Figure 3A schematic diagram of the structure of a risk monitoring device provided in an embodiment of this application is shown. Detailed Implementation

[0026] The embodiments of this application will now be described in more detail with reference to the accompanying drawings. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0027] To address the technical problem that current technologies cannot promptly assess the risk status of all node devices in complex network environments with numerous nodes, thus hindering risk monitoring of each node and impacting the efficiency and quality of computing power network integration, this embodiment provides a risk monitoring method, such as... Figure 1 As shown, the method includes:

[0028] Step 101: Obtain the computing power grid connection process data corresponding to the node device and select the node data to be analyzed within a preset time period from the computing power grid connection process data.

[0029] In this application embodiment, node devices are fundamental components of network communication and distributed computing systems. They can be any form of physical or virtual device, possessing certain computing, storage, and network communication capabilities to perform specific tasks or services. Node devices play different roles in different application scenarios. Specifically, node devices can include server nodes, client nodes, edge nodes, storage nodes, computing nodes, blockchain nodes, and sensor nodes, etc. Correspondingly, server nodes are high-performance computing devices within data centers or enterprises, responsible for processing large amounts of data, running complex applications, or providing services, such as database servers, web servers, and mail servers. Client nodes are devices used by end users, such as personal computers, smartphones, and tablets, which are typically used to access network resources, run applications, or interact with servers. Edge nodes are devices located at the network edge, such as routers, switches, and Internet of Things (IoT) devices. They are responsible for processing local data, performing preliminary data processing and filtering, reducing the pressure on central servers, and improving response speed. Storage nodes are devices specifically used for storing data, such as network-attached storage (NAS), storage servers in storage area networks (SANs), or storage nodes in distributed file systems. Computing nodes are devices focused on computational tasks, commonly found in high-performance computing (HPC) clusters and virtual machines in cloud computing environments. They are used to perform parallel computing tasks, scientific computing, big data analysis, and more. Blockchain nodes, in a blockchain network, are node devices responsible for verifying transactions, maintaining the consistency of the blockchain ledger, and participating in consensus mechanisms to ensure network security and stability. Sensor nodes, in Internet of Things (IoT) systems, are small sensor devices that act as nodes, responsible for collecting environmental data, monitoring the state of the physical world, and sending the data to a central node or cloud platform.

[0030] In some examples, computing power grid (CPG) is a concept that connects globally distributed computing resources through a network to form a unified, schedulable pool of computing resources. This is similar to power grid interconnection, where multiple power producers' grids are connected to form a larger power network to improve resource utilization efficiency and system stability. In CPG, computing resources such as personal computers, data centers, and edge computing nodes can all become "power plants," contributing their computing power via the internet to collectively complete large-scale computing tasks.

[0031] In this embodiment, the data from the computing power grid connection process can include: device status data of node devices, network status data, data status data, and user behavior data. Specifically, device status data can include the operating status, performance parameters, and load of hardware facilities such as servers, network devices, and storage devices. Based on this, it can be determined whether the devices are operating normally and whether there are risks such as overload or failure. Network status data mainly includes key indicators such as network traffic, bandwidth utilization, network latency, and packet loss rate. The network is the backbone of the computing power grid connection; any network anomaly can affect the stability and performance of the entire system. Based on this, the stability and performance of the devices can be determined. Data status data includes the integrity, confidentiality, and availability of data. The system monitors the data transmission, storage, and access processes; based on this, it can detect the risk of data leakage, tampering, or unauthorized access. User behavior data includes user operational behaviors; abnormal behavior patterns include frequent login failures and unauthorized access attempts, which may be potential security threats; based on this, potential risks can be assessed.

[0032] It should be noted that the computing power grid connection process can be divided into multiple time periods. Correspondingly, the preset time period can be the time period that needs to be monitored during the current computing power grid connection process.

[0033] In some examples, the node data to be analyzed is node process data selected from the computing power grid connection process data that can be used for subsequent analysis.

[0034] Step 102: Process the data of the node to be analyzed using a preset risk prediction model to generate the initial risk value corresponding to the node device.

[0035] In this embodiment, the preset risk prediction model can be a Convolutional Neural Network (CNN), which is a type of feedforward neural network that includes convolutional computation and has a deep structure. It is one of the representative algorithms of deep learning. Specifically, CNNs have representation learning capabilities and can classify input information in a translation-invariant manner according to a hierarchical structure, hence the name "translation-invariant artificial neural network". CNNs are constructed by mimicking biological visual mechanisms, performing supervised and unsupervised learning. They were first applied to computer vision and are now widely used in fields such as natural language processing. A CNN mainly consists of a three-layer architecture: an input layer, hidden layers, and an output layer. By adjusting the network structure, increasing the amount of training data, and using more effective optimization algorithms, the output accuracy of CNNs can be significantly improved. Furthermore, the preprocessing of input data and the model's generalization ability also affect output accuracy.

[0036] In some examples, the initial risk value can be the risk value obtained after the data to be analyzed is processed by a preset risk prediction model. It should be noted that the risk values ​​in the embodiments of this application are all percentage values, that is, the risk values ​​are all less than 1.

[0037] Step 103: Adjust the initial risk value based on the computing power grid connection process data to obtain the target risk value that meets the preset risk conditions.

[0038] In this embodiment, the initial risk value obtained through the preset risk prediction model needs to be further adjusted to obtain a more accurate target risk value. The target risk value is used to determine the current computing power grid connection risk of the node device.

[0039] Step 104: Based on the target risk value, perform risk monitoring on the node devices to determine the risk level of the node devices during the computing power grid connection process.

[0040] Compared with existing technologies, this embodiment uses a preset risk prediction model to obtain the initial risk value of the node device based on the node data to be analyzed within a preset time period. Then, it adjusts the initial risk value based on the computing power grid connection process data of the node device to obtain the target risk value that meets the preset risk conditions. Based on the target risk value, risk monitoring of the node device can more accurately identify and assess various risks in the computing power grid connection process. It can also accurately monitor the risks of each node device participating in the computing power grid connection process, which can effectively reduce risk losses in the computing power grid connection process. It can also reduce manual intervention, reduce the manpower cost of risk management, and improve the efficiency and quality of risk management, thus providing a strong guarantee for the security and stability of computing power grid connection.

[0041] To further illustrate the specific implementation process of the method in this embodiment, this embodiment provides the following: Figure 2 The specific method shown includes:

[0042] Step 201: Obtain the computing power grid connection process data corresponding to the node device and classify the computing power grid connection process data according to the preset categories to obtain multiple computing power grid connection process sub-data.

[0043] In this embodiment of the application, the multiple sub-data of the computing power grid connection process obtained according to the preset categories may include: device status data, network status data, data status data, and user behavior data.

[0044] For example, for risk monitoring of node device A during the computing power grid connection process, it is first necessary to obtain computing power grid connection process data A corresponding to node device A, and then classify the computing power grid connection process data A according to preset categories to obtain device status data A, network status data A, data status data A and user behavior data A.

[0045] Step 202: Generate fitting curves corresponding to the sub-data of multiple computing power grid connection processes, and determine the node data to be analyzed based on the fitting curves.

[0046] The fitted curve reflects the time-varying changes that occur during the grid connection process of computing power.

[0047] Optionally, step 202 may specifically include: selecting a target fitting curve within a preset time period from the fitting curves, merging the target fitting curves to obtain the target image data corresponding to the node device; marking the target image data with the device sequence identifier corresponding to the node device, and determining the marked target image data as the node data to be analyzed.

[0048] In this application embodiment, the association sequence identifier can be a unique identifier used to identify and track a series of related events, actions or data in data mining, sequence pattern recognition or specific business processes.

[0049] For example, based on step 201, a fitting curve 1 corresponding to device status data A, a fitting curve 2 corresponding to network status data A, a fitting curve 3 corresponding to data status data A, and a fitting curve 4 corresponding to user behavior data A are generated. Fitting curves 1, 2, 3, and 4 are truncated and merged into the same blank image according to a preset time length, and an associated sequence identification identifier A is configured in the blank image to obtain the data A to be analyzed corresponding to node device A.

[0050] Step 203: Process the data of the node to be analyzed using a preset risk prediction model to generate the initial risk value corresponding to the node device.

[0051] For example, based on step 202, the initial risk value A corresponding to node device A is obtained by using a preset risk prediction model to analyze the data A to be analyzed.

[0052] Step 204: Adjust the initial risk value based on the computing power grid connection process data to obtain the target risk value that meets the preset risk conditions.

[0053] Optionally, the multiple computing power grid connection process sub-data include: device status data, network status data, data status data, and user behavior data; correspondingly, step 204 may specifically include: adjusting the initial risk value based on the device status data, network status data, data status data, and user behavior data to obtain a target risk value that meets the preset risk conditions.

[0054] Furthermore, step 204 specifically includes: generating device status risk values, network status risk values, data security risk values, and user behavior risk values ​​corresponding to the node devices based on device status data, network status data, data status data, and user behavior data; adjusting the initial risk values ​​according to the device status risk values, network status risk values, data security risk values, and user behavior risk values ​​to obtain target risk values ​​that meet preset risk conditions.

[0055] In this embodiment of the application, the device status risk value corresponding to the node device can be determined by Formula 1, which is shown below:

[0056]

[0057] In Formula 1, Ld j t represents the real-time power load of the j-th node device, and Lc j t is the rated power load of the j-th node device, Lc j Let Lct be the real-time cooling load of the j-th node device, Lct be the rated cooling load of the i-th node device, and pT be the number of failures.

[0058] It should be noted that using the number of failures as an exponential amplification factor can significantly increase f immediately when the failure frequency rises. M (S j *W j The value of ) thus significantly increases the final risk value F. j The calculated value of (T) helps to quickly respond to fault maintenance and eliminate risks rapidly.

[0059] Accordingly, the network status risk value corresponding to the node device can be determined using Formula 2, as shown below:

[0060]

[0061] In Formula 2, Kt is the real-time bandwidth utilization rate within a preset time length T, ws is the real-time network traffic, Uk is the network latency fluctuation dispersion, fv is the average packet loss rate, and K is the minimum threshold for bandwidth utilization.

[0062] It should be noted that when real-time bandwidth utilization is high, but network traffic is very low, the power-saving equipment is more likely to have problems.

[0063] Furthermore, the data security risk value and user behavior risk value corresponding to the node device can be determined using Formulas 3 and 4, which are shown below:

[0064]

[0065]

[0066] In Formulas 3 and 4, s is a preset coefficient (which can be set based on experience), Dw is the data integrity rate, Dm is the data encryption level, Cl is the number of failed login attempts, Cn is the number of illegal attempts, cp is the frequency of illegal attempts, and ct is the average interval between illegal attempts.

[0067] It should be noted that, in the embodiments of this application, Cn cp It is an exponential amplification term, enabling a rapid response to sudden waves of attacks; correspondingly, encryption level 1 is the highest; if the data integrity rate decreases, the probability of anomalies is considered higher, therefore, the higher the encryption level, the greater the probability of anomalies. j The greater the fluctuation in risk calculation, the stronger the reaction, which improves the flexibility and agility of risk calculation, and can reduce the number of risk alarms when the encryption level is low, thereby reducing unnecessary risk warning actions and reducing costs.

[0068] Furthermore, the initial risk value can be adjusted based on the device status risk value, network status risk value, data security risk value, and user behavior risk value using Formula 5, which is shown below:

[0069] F j (T)=Z j (T)*ln(e+f M (S j *W j )+D j *C j (Formula 5)

[0070] In Formula 5, F j (T) represents the final risk value of the j-th node device within a preset time period T, which is also the target risk value. j (T) represents the risk probability value of the j-th node device within a preset time length T, f M (x) is the target logic function.

[0071] It should be noted that F j (T) The coefficient is obtained by multiplying the risk probability value by the coefficient, and the coefficient is obtained by weighting the risks of equipment status, network status, data security, and user behavior.

[0072] In this embodiment of the application, step 204 further includes: determining the target logic function corresponding to the node device based on the device status risk value and the network status risk value; adjusting the initial risk value based on the device status risk value, network status risk value, data security risk value, user behavior risk value and the target logic function to obtain a target risk value that meets the preset risk conditions.

[0073] Accordingly, step 204 further includes: if the product of the device status risk value and the network status risk value is greater than a preset risk threshold, the first preset logic function is determined as the target logic function; if the product of the device status risk value and the network status risk value is less than or equal to the preset risk threshold, the second preset logic function is determined as the target logic function.

[0074] The first preset logic function is different from the second preset logic function.

[0075] In this embodiment of the application, when S j *W j When the value is greater than or equal to the preset risk threshold, it indicates that the device status risk and network status risk are relatively high. In this case, the first preset logic function is determined as the target logic function. Specifically, the first preset logic function is shown in Formula Six below:

[0076]

[0077] It should be noted that when the product of the device status risk value and the network status risk value exceeds the rated value, it indicates a higher risk. To ensure F... j The magnitude of the increase in (T) will affect f. M (x) was magnified. times)

[0078] Correspondingly, when S j *W j When the value is less than the preset risk threshold, it indicates that both the device status risk and the network status risk are within a controllable range. The second preset logic function is then determined as the target logic function. The preset risk threshold is obtained through experience.

[0079] Specifically, the second preset logic function is shown in Formula 7 below:

[0080] fM(x)=0 (Formula 7)

[0081] Step 205: Based on the target risk value, perform risk monitoring on the node devices to determine the risk level of the node devices during the computing power grid connection process.

[0082] Optionally, after step 205, the method of this embodiment further includes: if the risk level is determined to be greater than a preset risk level threshold, determining the impact range information corresponding to the risk level and generating alarm information corresponding to the node device.

[0083] The alarm information includes risk level and scope of impact.

[0084] In this embodiment, with the rapid development of technologies such as cloud computing and big data, computing power grid connection has become an important means to improve the utilization rate of computing resources and reduce operating costs. Computing power grid connection is an innovative technology system and service model based on key technologies such as computing power, generalized computing scheduling, and trusted transactions. It aims to fully leverage the advantages of computing networks, widely aggregate computing power from multiple parties, and promote inclusive and efficient computing power services. It utilizes decentralized technologies such as blockchain to uniformly register and manage idle computing power resources, multi-party computing power resources, and computing power services, achieving unified operation of distributed computing power.

[0085] Correspondingly, the goal of computing power grid connection is to promote the integrated supply of social computing power, build a new computing network service capability system, support integrated services, and gradually promote computing power to become a social-level service like hydropower, which can be "accessed at a single point and used immediately." Currently, when managing risks in computing power grid connection, risks are reduced from the perspectives of encryption technology, secure communication, and operation. In specific implementation, advanced encryption technology can be used to ensure the security of data during transmission, and secure communication protocols, such as TLS / SSL, can be introduced to improve the security of network transmission. At the same time, identity authentication and access control need to be adopted, and a strict identity authentication mechanism needs to be implemented to ensure that only authorized users can access and use computing power resources. Different levels of access permissions should be set, and access control should be implemented according to user needs and security levels.

[0086] Optionally, in operation, intelligent resource scheduling algorithms are introduced to ensure efficient utilization and load balancing of computing resources. Dynamic adjustments are made based on task requirements and resource status to avoid resource waste and overload. A comprehensive log management mechanism is established to record all critical operations and security events, providing a basis for risk tracing. Detailed emergency response plans are developed to ensure rapid response and handling of potential security incidents. Backup and recovery mechanisms are also established to ensure rapid restoration of the computing network's normal operation in the event of a failure or attack. However, various risks and problems in the process of computing power networking are gradually becoming apparent, such as equipment failure, network congestion, and data leakage. Traditional risk management methods often rely on manual experience and rule-based judgment, which are difficult to cope with complex and ever-changing network environments.

[0087] Compared with existing technologies, this embodiment uses a preset risk prediction model to obtain the initial risk value of the node device based on the node data to be analyzed within a preset time period. Then, it adjusts the initial risk value based on the computing power grid connection process data of the node device to obtain the target risk value that meets the preset risk conditions. Based on the target risk value, risk monitoring of the node device can more accurately identify and assess various risks in the computing power grid connection process. It can also accurately monitor the risks of each node device participating in the computing power grid connection process, which can effectively reduce risk losses in the computing power grid connection process. It can also reduce manual intervention, reduce the manpower cost of risk management, and improve the efficiency and quality of risk management, thus providing a strong guarantee for the security and stability of computing power grid connection.

[0088] Furthermore, as Figure 1 and Figure 2 The specific implementation of the method shown in this embodiment provides a risk monitoring device, such as... Figure 3 As shown, the device includes: an acquisition module 31, a generation module 32, an adjustment module 33, and a monitoring module 34.

[0089] The acquisition module 31 is configured to acquire computing power grid connection process data corresponding to the node device and select node data to be analyzed within a preset time period from the computing power grid connection process data;

[0090] The generation module 32 is configured to process the data of the node to be analyzed through a preset risk prediction model to generate the initial risk value corresponding to the node device;

[0091] The adjustment module 33 is configured to adjust the initial risk value based on the computing power grid connection process data to obtain a target risk value that meets the preset risk conditions;

[0092] The monitoring module 34 is configured to perform risk monitoring on the node device based on the target risk value, and determine the risk level of the node device during the computing power grid connection process.

[0093] In some examples of this embodiment, the acquisition module 31 is specifically configured to acquire computing power grid connection process data corresponding to the node device and classify the computing power grid connection process data according to a preset category to obtain multiple computing power grid connection process sub-data; generate fitting curves corresponding to the multiple computing power grid connection process sub-data respectively, and determine the node data to be analyzed based on the fitting curves, wherein the fitting curves follow the time changes of the computing power grid connection process.

[0094] In some examples of this embodiment, the acquisition module 31 is further configured to select a target fitting curve within the preset time period from the fitting curves, merge the target fitting curves to obtain the target image data corresponding to the node device, mark the target image data with the device sequence identifier corresponding to the node device, and determine the marked target image data as the node data to be analyzed.

[0095] In some examples of this embodiment, the multiple computing power grid connection process sub-data includes: device status data, network status data, data status data, and user behavior data; correspondingly, the adjustment module 33 is specifically configured to adjust the initial risk value based on the device status data, the network status data, the data status data, and the user behavior data to obtain a target risk value that meets preset risk conditions.

[0096] In some examples of this embodiment, the adjustment module 33 is further configured to generate a device status risk value, a network status risk value, a data security risk value, and a user behavior risk value corresponding to the node device based on the device status data, the network status data, the data status data, and the user behavior data; and to adjust the initial risk value according to the device status risk value, the network status risk value, the data security risk value, and the user behavior risk value to obtain a target risk value that meets preset risk conditions.

[0097] In some examples of this embodiment, the adjustment module 33 is further configured to determine the target logic function corresponding to the node device based on the device state risk value and the network state risk value; and to adjust the initial risk value based on the device state risk value, the network state risk value, the data security risk value, the user behavior risk value, and the target logic function to obtain a target risk value that meets preset risk conditions.

[0098] In some examples of this embodiment, the adjustment module 33 is further configured to determine the first preset logic function as the target logic function when the product of the device state risk value and the network state risk value is greater than a preset risk threshold; and to determine the second preset logic function as the target logic function when the product of the device state risk value and the network state risk value is less than or equal to the preset risk threshold, wherein the first preset logic function is different from the second preset logic function.

[0099] In some examples of this embodiment, the monitoring module 34 is further configured to determine the impact range information corresponding to the risk level and generate alarm information corresponding to the node device when it is determined that the risk level is greater than a preset risk level threshold. The alarm information includes the risk level and the impact range information.

[0100] It should be noted that other corresponding descriptions of the functional units involved in the risk monitoring device provided in this embodiment can be found in [reference needed]. Figure 1 and Figure 2 The corresponding descriptions in [the document] will not be repeated here.

[0101] Based on the above, Figures 1 to 2 Accordingly, this embodiment also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method. Figures 1 to 2 The method shown.

[0102] Based on this understanding, the technical solution of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as CD-ROM, USB flash drive, mobile hard drive, etc.) and includes several instructions to cause a computer device (such as personal computer, server, or network device, etc.) to execute the methods of various implementation scenarios of this application.

[0103] Based on the above, Figures 1 to 2 The method shown, and Figure 3 To achieve the above objectives, this application also provides an electronic device, such as a personal computer, server, laptop, smartphone, intelligent robot, or other intelligent terminal, as illustrated in the virtual device embodiment. This device includes a storage medium and a processor; the storage medium stores a computer program; and the processor executes the computer program to implement the above-described virtual device. Figures 1 to 2 The method shown.

[0104] Optionally, the aforementioned physical devices may also include a user interface, a network interface, a camera, radio frequency (RF) circuitry, sensors, audio circuitry, a Wi-Fi module, etc. The user interface may include a display screen, input units such as a keyboard, etc., and optional user interfaces may also include USB interfaces, card reader interfaces, etc. The network interface may optionally include standard wired interfaces, wireless interfaces (such as Wi-Fi interfaces), etc.

[0105] Those skilled in the art will understand that the physical device structure provided in this embodiment does not constitute a limitation on the physical device, and may include more or fewer components, or combine certain components, or have different component arrangements.

[0106] The storage medium may also include an operating system and a network communication module. The operating system is a program that manages the hardware and software resources of the aforementioned physical device, supporting the operation of information processing programs and other software and / or programs. The network communication module is used to enable communication between the various components within the storage medium, as well as communication with other hardware and software in the information processing physical device.

[0107] Based on the above, Figures 1 to 2 The method shown in this application embodiment also provides a computer program product, which includes a computer program that, when executed by a processor, implements the above-described method. Figures 1 to 2 The methods shown, and the methods implemented when the computer program is executed by the processor, can be referred to in the various embodiments of this application, and will not be repeated here.

[0108] Through the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented using software plus necessary general-purpose hardware platforms, or it can be implemented through hardware. Compared with the existing technology, this embodiment obtains the initial risk value corresponding to the node device by using a preset risk prediction model to analyze the node data within a preset time period of the node device. Then, the initial risk value is adjusted based on the computing power grid connection process data corresponding to the node device to obtain the target risk value that meets the preset risk conditions. Based on the target risk value, risk monitoring of the node device can more accurately identify and assess various risks in the computing power grid connection process. It can also accurately monitor the risks of each node device participating in the computing power grid connection process, so that the risk loss in the computing power grid connection process can be effectively reduced. It can also reduce manual intervention, reduce the manpower cost of risk management, and improve the efficiency and quality of risk management, providing a strong guarantee for the security and stability of computing power grid connection.

[0109] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0110] The above description is merely a specific embodiment of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments described herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A risk monitoring method, characterized in that, include: Acquire the computing power grid connection process data corresponding to the node devices and select the node data to be analyzed within a preset time period from the computing power grid connection process data; The data of the node to be analyzed is processed by a preset risk prediction model to generate the initial risk value corresponding to the node device; The initial risk value is adjusted based on the computing power grid connection process data to obtain a target risk value that meets the preset risk conditions; Based on the target risk value, risk monitoring is performed on the node device to determine the risk level of the node device during the computing power grid connection process.

2. The method according to claim 1, characterized in that, The process of acquiring computing power grid connection process data corresponding to node devices and selecting node data to be analyzed within a preset time period from the computing power grid connection process data includes: The computing power grid connection process data corresponding to the node device is obtained and classified according to a preset category to obtain multiple computing power grid connection process sub-data. The fitting curves corresponding to the sub-data of the multiple computing power grid connection process are generated respectively, and the data of the node to be analyzed is determined based on the fitting curves. The fitting curves follow the time changes of the computing power grid connection process.

3. The method according to claim 2, characterized in that, Determining the node data to be analyzed based on the fitted curve includes: Select the target fitting curve within the preset time period from the fitting curves, merge the target fitting curves, and obtain the target image data corresponding to the node device; The target image data is labeled with the device sequence identifier corresponding to the node device, and the labeled target image data is determined as the node data to be analyzed.

4. The method according to claim 2, characterized in that, The multiple computing power grid connection process sub-data includes: device status data, network status data, data status data, and user behavior data; The step of adjusting the initial risk value based on the computing power grid connection process data to obtain a target risk value that meets preset risk conditions includes: The initial risk value is adjusted based on the device status data, the network status data, the data status data, and the user behavior data to obtain a target risk value that meets the preset risk conditions.

5. The method according to claim 4, characterized in that, The step of adjusting the initial risk value based on the device status data, network status data, data status data, and user behavior data to obtain a target risk value that meets preset risk conditions includes: Based on the device status data, the network status data, the data status data, and the user behavior data, generate device status risk value, network status risk value, data security risk value, and user behavior risk value corresponding to the node device; The initial risk value is adjusted based on the device status risk value, the network status risk value, the data security risk value, and the user behavior risk value to obtain a target risk value that meets the preset risk conditions.

6. The method according to claim 5, characterized in that, The step of adjusting the initial risk value based on the device status risk value, the network status risk value, the data security risk value, and the user behavior risk value to obtain a target risk value that meets preset risk conditions includes: Based on the device status risk value and the network status risk value, determine the target logical function corresponding to the node device; The initial risk value is adjusted based on the device status risk value, the network status risk value, the data security risk value, the user behavior risk value, and the target logic function to obtain a target risk value that meets the preset risk conditions.

7. The method according to claim 6, characterized in that, The step of determining the target logical function corresponding to the node device based on the device state risk value and the network state risk value includes: If the product of the device status risk value and the network status risk value is greater than a preset risk threshold, the first preset logic function is determined as the target logic function. If the product of the device status risk value and the network status risk value is less than or equal to a preset risk threshold, the second preset logic function is determined as the target logic function, wherein the first preset logic function is different from the second preset logic function.

8. The method according to any one of claims 1 to 7, characterized in that, After performing risk monitoring on the node device based on the target risk value and determining the risk level of the node device during the computing power grid connection process, the method further includes: If the risk level is determined to be greater than a preset risk level threshold, the impact range information corresponding to the risk level is determined, and alarm information corresponding to the node device is generated, wherein the alarm information includes the risk level and the impact range information.

9. A risk monitoring device, characterized in that, include: The acquisition module is configured to acquire computing power grid connection process data corresponding to the node device and select node data to be analyzed within a preset time period from the computing power grid connection process data; The generation module is configured to process the data of the node to be analyzed through a preset risk prediction model to generate the initial risk value corresponding to the node device; The adjustment module is configured to adjust the initial risk value based on the computing power grid connection process data to obtain a target risk value that meets preset risk conditions; The monitoring module is configured to perform risk monitoring on the node device based on the target risk value, and determine the risk level of the node device during the computing power grid connection process.

10. A computer program product, the computer program product comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 8.

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