Equipment management method and system based on cloud computing, equipment and storage medium

Abstract

The invention discloses an equipment management method and system based on cloud computing, equipment and a storage medium, and the method comprises the steps: recognizing and accessing each piece of equipment to a cloud computing platform, and carrying out the automatic recognition of the key information of each piece of equipment for the accessed equipment; judging the operation condition and the resource use condition of each device, and determining the device type of the device reaching the resource tension state and the devices belonging to the same type; querying a corresponding preset fault risk assessment model according to the determined equipment type, performing fault risk assessment on the equipment by using the queried fault risk assessment model, and removing the equipment with a fault risk; and querying a corresponding preset resource scheduling strategy according to the determined device type, and completing resource scheduling of the device according to the queried preset resource scheduling strategy. According to the invention, efficient, safe, flexible and adaptive equipment management can be realized.

Description

Technical Field

[0001] The present application relates to the technical field of cloud computing-based device management, and in particular to a cloud computing-based device management method, system, device and storage medium. Background Art

[0002] A large amount of information is generated during the use of equipment, and it is often necessary to process and analyze this information in order to adjust the management of the equipment. The processing of large amounts of data is time-consuming and labor-intensive, and it is also necessary to ensure the security and privacy of the data, as well as provide high availability and reliability. These requirements pose huge challenges to traditional equipment management. The management of equipment in the existing technology is still lacking in terms of efficiency, security, and flexibility; for example: when processing large amounts of data, performance bottlenecks often occur, resulting in low processing efficiency; equipment information involving user privacy and financial security is often leaked; with the development of the business, equipment functions need to be continuously expanded and upgraded, and it is impossible to manage the newly added functional modules and corresponding data in a timely manner.

[0003] As cloud computing technology continues to mature and become more popular, its elastic scaling, high availability and other features have brought revolutionary changes to all walks of life. In the field of equipment management, the powerful computing power of cloud computing technology can be used to improve data processing efficiency and meet real-time processing needs; data encryption, identity authentication, access control and other technical means can be used to ensure data security, and the use of computing resources and storage resources can be flexibly adjusted according to business needs to achieve elastic expansion of the system. However, simply applying cloud computing technology to equipment management has poor adaptability to equipment management in different types and different application scenarios. Summary of the invention

[0004] In order to achieve efficient, secure, flexible and adaptable device management, the present application provides a device management method, system, device and storage medium based on cloud computing.

[0005] In a first aspect, the present application discloses a device management method based on cloud computing, comprising: Identify and connect each device to the cloud computing platform, obtain the device information of each device, and use the deep learning algorithm in the cloud computing platform to automatically identify the key information of each device connected; the key information includes: device identification, device type, device resource usage and device operation status; Use the deep learning algorithm in the cloud computing platform to judge the operation status and resource usage of each device, and judge whether each device has reached a resource shortage state. If there is a device that has reached a resource shortage state, determine the device type of the device that has reached the resource shortage state and the devices of the same type as the device that has reached the resource shortage state; In the cloud computing platform, a preset fault risk assessment model is queried according to the determined device type, and the queried fault risk assessment model is used to perform fault risk assessment on devices of the same type as the device that has reached the resource shortage state, and devices of the same type as the device that has reached the resource shortage state that are assessed to have fault risks are eliminated; In the cloud computing platform, the corresponding preset resource scheduling strategy is queried according to the determined device type, and the resource scheduling of the device that has reached the resource-scarce state and the device of the same type as the device that has reached the resource-scarce state is completed according to the resources of the devices that have been eliminated and the preset resource scheduling strategy is queried to complete the resource scheduling of the device that has reached the resource-scarce state and the devices that are of the same type as the device that has reached the resource-scarce state.

[0006] By adopting the above solution, the intelligent algorithm in the cloud computing platform is used to identify the key information of each connected device to distinguish different types of devices. For different types of devices, adaptive intelligent algorithms are obtained to complete the identification of devices in a state of resource shortage and whether there is a risk of failure. After adaptively screening out devices that may have failures, the preset resource scheduling method is adaptively used to complete resource scheduling, thereby achieving efficient, safe, flexible and adaptive equipment management.

[0007] Preferably, it also includes: Use deep learning algorithms in the cloud computing platform to automatically identify the application scenarios of connected devices; Determine the device application scenarios corresponding to the devices that have reached the resource-constrained state, match the corresponding preset security risk level requirements based on the determined device application scenarios, and set corresponding preset security risk level requirements for different device application scenarios; In the cloud computing platform, a preset security risk level assessment model is queried according to the determined device type, and the queried security risk level assessment model is used to perform security risk level assessment on devices of the same type as the device that has reached a resource-constrained state, and devices of the same type as the device that has reached a resource-constrained state whose assessment results are lower than the preset security risk level are eliminated.

[0008] By adopting the above solution, considering that different application scenarios of various devices have different security risk requirements for devices, it is necessary to schedule devices that meet the security risk level requirements during the resource scheduling process to further ensure the security management of the devices.

[0009] Preferably, it also includes: In the cloud computing platform, the preset disaster recovery performance level requirements are matched based on the determined device application scenarios, and the corresponding disaster recovery performance level requirements are set for different device application scenarios; For devices of the same type as the device that has reached a resource-constrained state, the disaster recovery performance level of each device is determined based on the historical disaster recovery drill test results of each device, and devices of the same type as the device that has reached a resource-constrained state whose disaster recovery performance level is lower than the preset disaster recovery performance level requirement are eliminated.

[0010] By adopting the above solution, considering the different application scenarios of each device, the disaster recovery performance level requirements of the device are different. Devices with high processing rate requirements have high disaster recovery performance requirements. Therefore, during the resource scheduling process, it is necessary to schedule devices that meet the disaster recovery performance requirements to further ensure the safe management of the equipment.

[0011] Preferably, in the process of completing resource scheduling of devices that have reached a resource-constrained state and devices of the same type as those that have reached a resource-constrained state, when the resource gap between some devices that follow the resource scheduling strategy is less than the preset resource gap, devices with a high equipment safety risk assessment level or a high disaster recovery performance level are preferentially selected for resource scheduling.

[0012] By adopting the above solution, during the resource scheduling process according to the preset scheduling strategy, some devices are added or reduced based on the resource usage of each device to cope with the situation where some devices have the same available resources. In order to further ensure the security and flexibility of the equipment, devices with high security risk assessment levels or high disaster recovery performance levels are given priority for resource scheduling.

[0013] Preferably, in the process of completing resource scheduling of devices that have reached a resource-constrained state and devices of the same type as those that have reached a resource-constrained state, when the resource gap between some devices that follow the resource scheduling strategy is less than a preset resource gap, the historical scheduling times of these devices are counted, and devices with the lowest historical scheduling times are preferentially selected for resource scheduling.

[0014] By adopting the above solution, during the resource scheduling process according to the preset scheduling strategy, some devices are added or reduced based on the resource usage of each device to deal with the situation where some devices have the same available resource conditions. In order to further ensure the service life of each device and balance the number of times each device is used, the device with the lowest historical scheduling number is given priority for resource scheduling.

[0015] Preferably, it also includes: The deep learning algorithm in the cloud computing platform is used to predict the operation status and resource usage of each device, and determine whether each device will reach a resource-constrained state after the preset time. If there is a device that reaches a resource-constrained state after the preset time, the device that reaches the resource-constrained state after the preset time is supplemented to the device type that has reached the resource-constrained state.

[0016] By adopting the above solution, deep learning in the cloud computing platform can be used to predict the devices that may reach a resource-constrained state in the current adjacent period, and resource scheduling adjustments can be made to these device types in advance to further ensure efficient device management.

[0017] Preferably, it also includes: When it is determined that the ratio of devices of the same type as the devices that have reached a resource-constrained state before and after the elimination process is completed is lower than a preset ratio, some devices are reselected from the eliminated devices whose disaster recovery performance levels are lower than the preset disaster recovery performance level requirements for supplementation and restoration in descending order of the disaster recovery performance levels of each device, until the ratio of devices of the same type as the devices that have reached a resource-constrained state before and after the elimination process is completed is not lower than a preset ratio, and the supplementation and restoration operation is stopped.

[0018] By adopting the above solution, in order to ensure the number of devices that can be scheduled for resources as much as possible, the disaster recovery level requirement is correspondingly reduced, so that the devices eliminated according to the disaster recovery performance level are replenished as device objects for schedulable resources.

[0019] In a second aspect, the present application discloses a cloud computing-based device management system, comprising: The device key information identification module is used to identify and connect each device to the cloud computing platform, obtain the device information of each device, and use the deep learning algorithm in the cloud computing platform to automatically identify the key information of each device connected to the device; the key information includes: device identification, device type, device resource usage and device operation status; The device resource status judgment module is used to use the deep learning algorithm in the cloud computing platform to judge the operation status and resource usage of each device, and judge whether each device has reached a resource shortage state. If there is a device that has reached a resource shortage state, the device type of the device that has reached the resource shortage state and the device of the same type as the device that has reached the resource shortage state are determined; The device execution scheduling screening module is used to query the corresponding preset fault risk assessment model according to the determined device type in the cloud computing platform, and use the queried fault risk assessment model to perform fault risk assessment on the devices of the same type as the device that has reached the resource shortage state, and eliminate the devices of the same type as the device that has reached the resource shortage state, and the devices with the assessment results showing that there is a fault risk; The device resource scheduling execution module is used to query the corresponding preset resource scheduling strategy according to the determined device type in the cloud computing platform, and complete the resource scheduling of the device that has reached the resource-scarce state and the device of the same type as the device that has reached the resource-scarce state according to the resources of the devices that have completed the elimination processing and belong to the same type as the device that has reached the resource-scarce state and the preset resource scheduling strategy that has been queried.

[0020] By adopting the above solution, the cloud computing platform is used to perform adaptive resource scheduling processing on different types of equipment, thereby achieving efficient, secure, flexible and adaptive equipment management.

[0021] In a third aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the method as described above.

[0022] In a fourth aspect, the present application provides a computer device, comprising a memory, a processor, and a program stored and executable on the memory, wherein the program implements the steps of the above method when executed by the processor.

[0023] In summary, this application has the following beneficial effects: 1. Use multiple intelligent algorithms in the cloud computing platform to identify key information of each connected device to distinguish different types of devices. Adaptive intelligent algorithms are used to identify devices in a resource-constrained state and determine whether there is a risk of failure. After screening out devices that may have failures, the preset resource scheduling strategy is adaptively used to complete resource scheduling, achieving efficient, safe, flexible and adaptive device management; 2. Consider the security risks and disaster recovery performance requirements of the equipment in different application scenarios, and select equipment that meets the security risks and disaster recovery performance requirements to complete resource scheduling, further ensuring safe and flexible equipment management; and when some equipment resources are similar during the resource scheduling process, give priority to scheduling equipment with higher security risk levels and disaster recovery performance levels, to achieve safer and more flexible equipment management; 3. Predict the devices that may reach resource shortage in the current adjacent period, adjust the resource scheduling of these device types in advance, prevent resource shortage in advance, and further ensure efficient equipment management. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a flowchart of the device management method based on cloud computing described in a specific embodiment; Figure 2 It is a structural diagram of the cloud computing-based device management system described in a specific embodiment. DETAILED DESCRIPTION

[0025] 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.

[0026] like Figure 1 As shown, the embodiment of the present application discloses a device management method based on cloud computing. This embodiment is only illustrated by taking a ticketing device as an example. The specific steps include: S1. Identify and connect each device to the cloud computing platform.

[0027] Specifically, the cloud computing platform's automated discovery mechanism is used to identify and access various device resources, such as self-service ticket machines, window ticketing equipment, online devices (websites, apps, etc.), and special equipment (3D printing ticket machines). Through standardized interface protocols, interconnection between different devices is achieved to ensure smooth access to resources.

[0028] In addition, in order to facilitate subsequent resource calls and device data management, the connected device resources are abstracted and converted into unified resource units, and resource standardization specifications are formulated. A dynamic device resource pool is built using the cloud computing platform, and the abstracted device resources are included. Functions such as dynamic allocation, release, monitoring and tuning of resources are realized, and security policies and access control mechanisms are set for each connected device to ensure the security and isolation of resources.

[0029] S2. Obtain the device information of each device, and use the deep learning algorithm in the cloud computing platform to automatically identify the key information of each connected device.

[0030] Specifically, the key information includes: device identification, device type, device resource usage, device operation status, device application scenario and other device information.

[0031] Among them, the device identifier is a unique device ID for distinguishing the device; the device type can be divided according to the device function, such as: divided into ticket checking equipment, ticket selling equipment, and ticket printing equipment according to the ticket transaction function; it can also be divided according to the device usage method, such as: the ticket device transaction method is divided into online transaction equipment and offline transaction equipment; or the device type is divided according to other classification methods (such as comprehensive device function and usage method); the device resource usage includes: CPU, memory, storage, network and other resources; the device operation status includes: device usage (such as: transaction content, transaction object, transaction duration, etc. in ticket device transaction status) and device usage requirements (ticket transaction requirements), the device's own operation data (such as: power, service life, operation status of device submodules or components), etc.; the device application scenarios include: application scenarios of different usage scales or different usage periods; such as: concerts of different scales, sports events and smart travel, or scenic spot tourism at different periods; other device information includes: IP address or physical address of the device, etc.

[0032] Among them, a neural network can be selected using the deep learning algorithm in the cloud computing platform, which is denoted as the first neural network; the input of the first neural network is the data information of the device, and the output is the key information of the device, which is trained and generated through the data information of historical devices historically annotated with the key information of the device.

[0033] S3. Use the deep learning algorithm in the cloud computing platform to analyze the operation status and resource usage of each device to determine whether each device has reached a resource-constrained state.

[0034] Specifically, a neural network can be selected using a deep learning algorithm in a cloud computing platform, which is denoted as a second neural network. The input of the second neural network is the operating status and resource usage of each device, and the output is whether each device has reached a resource-constrained state. The second neural network is trained and generated through the operating status and resource usage of historical devices that are historically marked with whether the devices have reached a resource-constrained state.

[0035] Inputting the operation status and resource usage status of each device obtained in real time or at a scheduled time into the second neural network, determining whether there is a device that has reached a resource shortage state, determining the device type of the device that has reached a resource shortage state (referred to as the first device) and the device that is of the same type as the device that has reached a resource shortage state (referred to as the second device); S4. In the cloud computing platform, query the corresponding preset fault risk assessment model according to the determined device type, use the fault risk assessment model to determine whether the device has a fault risk, and eliminate the device with a fault risk.

[0036] Considering the resource shortage of each device, it is necessary to carry out corresponding resource scheduling and adjust the resources of each device, such as increasing the computing resources of online devices or increasing the number of offline devices put into use. However, during the resource scheduling process, attention should be paid to whether the adjusted devices have the risk of failure. Once the added offline devices or online devices have failures, the required resources cannot be scheduled, so the devices with failure risks are eliminated in advance. At the same time, considering that the resource scheduling methods and failure modes of different types of equipment are different, the failure risk judgment of the same type of equipment is completed adaptively for a certain type of equipment.

[0037] Specifically, the corresponding preset fault risk assessment model is queried in the cloud computing platform according to the determined equipment type; wherein the preset fault risk models corresponding to different types of equipment are stored in the cloud computing platform; each preset fault risk model adopts a neural network model to determine whether a specific type of equipment has a fault risk or not, the input of the model is the operating status information of the specific type of equipment, and the output of the model is the inference result of whether the equipment has a fault risk or not, which is generated through training on the operating status of the specific type of equipment that has been historically marked as having a fault risk or not.

[0038] In the cloud computing platform, the corresponding preset fault risk assessment model is queried according to the determined device type, such as: the fault risk assessment model of online equipment, the fault risk assessment model of offline equipment; the fault risk assessment of the second device is performed using the queried fault risk assessment model, and the devices in the second device that are evaluated as having a fault risk are eliminated.

[0039] S5. In the cloud computing platform, query the corresponding preset resource scheduling strategy according to the determined device type, and then complete the resource scheduling of the device.

[0040] Specifically, considering that different types of equipment have different focuses in resource scheduling, for example, the resource scheduling strategy for online equipment focuses more on responding to sudden traffic peaks and network security threats. The main strategy is to use the elastic expansion capability of cloud computing to automatically increase the server resources of equipment that is resource-constrained during traffic peaks. When it is difficult to increase the server resources of equipment that is resource-constrained, consider adding online equipment, such as opening a new APP as a new ticket transaction channel to alleviate high transaction demand. The intelligent scheduling strategy for offline equipment pays more attention to the physical location and frequency of use of the equipment. If the resources of the current equipment are tight, choose to open a device that is less than the preset distance from the physical location of the current device and has not reached a resource-constrained state to alleviate high transaction demand.

[0041] In the cloud computing platform, query the corresponding preset resource scheduling strategy according to the determined device type, such as: the preset resource scheduling strategy of online devices and the resource scheduling strategy of offline devices; According to the resources of the second device that has completed the elimination process (such as device A with a CPU usage rate of 45% and device B with a CPU usage rate of 50%) and the preset resource scheduling policy (such as the resource scheduling policy for offline devices), the resource scheduling of the first device and the second device is completed (for example, according to the location information of the second device, it is determined that device A is closest to device C in physical distance, and the resources of device A are in a tight state. Therefore, choose to open device A and do not operate device B to complete the resource scheduling).

[0042] By adopting the method described in the above embodiment, efficient, safe, flexible and adaptable equipment management can be achieved.

[0043] In a specific embodiment, considering that the information required to be obtained by the device in different application scenarios is different, each device is required to ensure that the information is at a specific security risk level in order to protect the security of the collected information. Therefore, in the process of resource scheduling, the device that meets the security risk level should be called as much as possible. The method also includes: Determine the device application scenario corresponding to the device that has reached a resource-constrained state; in this embodiment, the device application scenario is an application scenario of different usage scales; such as: (concert) application scenarios of different usage scales, including: a small-scale (concert) application scenario, a medium-scale (concert) application scenario, and a large-scale (concert) application scenario.

[0044] Based on the determined device application scenario matching and corresponding preset security risk level requirements, different device application scenarios are set with corresponding preset security risk level requirements, such as: the preset general security risk level requirements corresponding to the small-scale (concert) application scenario settings, the preset lower security risk level requirements corresponding to the medium-scale (concert) application scenario settings, and the low risk level requirements corresponding to the large-scale (concert) application scenario settings.

[0045] In the cloud computing platform, the corresponding preset security risk level assessment model is queried according to the determined device type, such as: the security risk level assessment model for online devices and the security risk level assessment model for offline devices; wherein, the cloud computing platform stores preset security risk level assessment models corresponding to different types of devices; each security risk level assessment model adopts a neural network model to determine the security risk level of a specific type of device, the input of the model is the operating status information of the specific type of device, and the output of the model is the inference result of the security risk level of the device, which is generated through training on the operating status of a specific type of device that has been historically annotated with a specific security risk level.

[0046] Use the queried security risk level assessment model to perform security risk level assessment on the second device, for example: use the security risk level assessment model of the currently queried offline device to perform security risk level assessment on the second device, and obtain assessment results (for example, the security risk level of device A with a CPU usage rate of 45% that is not put into use is a relatively low risk level, and the security risk level of device B with a CPU usage rate of 50% is a low risk level); Eliminate devices of the same type as the device that has reached resource shortage status and whose evaluation results are lower than the preset security risk level; for example: the application scenario of device C is a medium-sized concert application scenario with a corresponding preset lower security risk level requirement. The security risk level of device A meets the requirement and can be called without being eliminated.

[0047] In a specific embodiment, considering that different application scenarios of various devices have different requirements for the disaster recovery performance level of the devices, the larger the application scenario, the higher the requirements for the disaster recovery performance level. In the resource scheduling process, it is necessary to schedule devices that meet the requirements for disaster recovery performance to further ensure the safety management of the devices. The method also includes: In the cloud computing platform, the corresponding preset disaster recovery performance level requirements are matched based on the determined device application scenarios, and the corresponding disaster recovery performance level requirements are set for different device application scenarios; for example: the preset general disaster recovery performance level corresponding to the small-scale (concert) application scenario setting, the preset higher disaster recovery performance level corresponding to the medium-scale (concert) application scenario setting, and the preset high disaster recovery performance level corresponding to the large-scale (concert) application scenario setting.

[0048] For devices of the same type as the device that has reached a resource-constrained state, the disaster recovery performance level of each device is determined based on the historical disaster recovery drill test results of each second device; specifically, the scores of the disaster recovery drill test results of each device closest to the current moment stored in the cloud computing platform are used to determine the different score threshold intervals, and then determine the disaster recovery performance level that matches the different score threshold intervals. For example: X1 is in [60-75), the matching disaster recovery performance level is the preset general disaster recovery performance level; X1 is in [75-85), the matching disaster recovery performance level is the preset higher disaster recovery performance level; X1 is in [80-100], the matching disaster recovery performance level is the preset high disaster recovery performance level; Eliminate devices of the same type as the device that has reached the resource shortage state, and whose disaster recovery performance level is lower than the preset disaster recovery performance level requirements. For example, the application scenario of device C is a medium-sized concert application scenario with a corresponding preset higher disaster recovery performance level. The disaster recovery performance level of device A is determined to be a high-performance level that meets the requirements and can be called without being eliminated.

[0049] In addition, in order to ensure the number of devices that can perform resource scheduling as much as possible, the method further includes: When it is determined that the ratio of devices of the same type as the devices that have reached a resource-constrained state before and after the elimination process is completed is lower than a preset ratio, which can be set manually; indicating that there are fewer devices currently retained and available for resource invocation, then among the devices whose disaster recovery performance levels have been eliminated and are lower than the preset disaster recovery performance level requirements, some devices are reselected for supplementary restoration in descending order of the disaster recovery performance levels of each device, until the ratio of devices of the same type as the devices that have reached a resource-constrained state before and after the elimination process is completed is not lower than the preset ratio, and the supplementary restoration operation is stopped.

[0050] In a specific embodiment, considering that in the process of resource scheduling according to the preset scheduling strategy, some devices are added or reduced based on the resource usage of each device to cope with the situation that some devices have the same resource availability, in order to further ensure the security and flexibility of the device, the method also includes: In the process of completing resource scheduling of the equipment that has reached the resource-constrained state and the equipment of the same type as the equipment that has reached the resource-constrained state, when the resource gap between some equipment that follows the resource scheduling strategy is less than the preset resource gap, that is, according to the resource scheduling strategy, some equipment that meets the resource scheduling strategy requirements is selected from the second equipment, and if the resource gap between any two equipment in the part of the equipment (which can be the resource score numerical gap obtained by weighted calculation of various resource scores) is less than the preset resource gap (which can be the preset resource gap score), equipment with a high equipment security risk assessment level or a high disaster recovery performance level is preferentially selected for resource scheduling. In addition, the equipment can be selected for resource scheduling by comprehensively considering the security risk assessment level and the disaster recovery performance level according to the preset weight ratio.

[0051] In a specific embodiment, during resource scheduling according to a preset scheduling strategy, some devices are added or reduced based on the resource usage of each device to cope with the situation where some devices have the same resource availability. In order to further ensure the service life of each device, the method further includes: During the resource scheduling process of the devices that have reached a resource-constrained state and the devices of the same type as the devices that have reached a resource-constrained state, when the resource gap between some devices that follow the resource scheduling strategy is less than the preset resource gap, the historical scheduling times of these devices are counted. In order to further balance the scheduling times of each device, the device with the lowest historical scheduling times is preferentially selected for resource scheduling.

[0052] Alternatively, the historical service life and scheduling times of the equipment are counted, and the equipment with the shortest historical service life is given priority. When the historical service life is the same, the equipment with the lowest scheduling times is given priority for resource scheduling.

[0053] In a specific embodiment, to further ensure efficient device management, the method further includes: The deep learning algorithm in the cloud computing platform is used to predict the operation and resource usage of each device, and determine whether each device will reach a resource shortage state after a preset time; the preset time is manually set. If there is a device that reaches a resource shortage state after the preset time, the device that reaches a resource shortage state after the preset time is added to the device type that has reached a resource shortage state.

[0054] like Figure 2 As shown, the embodiment of the present application also discloses a device management system based on cloud computing, and the system is applied to a cloud computing platform, including: The device key information identification module 101 is used to identify and connect each device to the cloud computing platform, obtain the device information of each device, and use the deep learning algorithm in the cloud computing platform to automatically identify the key information of each device connected to the device; the key information includes: device identification, device type, device resource usage and device operation status; The device resource status judgment module 102 is used to judge the operation status and resource usage status of each device by using the deep learning algorithm in the cloud computing platform, and judge whether each device has reached a resource shortage state. If a device has reached a resource shortage state, the device type of the device reaching the resource shortage state and the device of the same type as the device reaching the resource shortage state are determined; The device execution scheduling screening module 103 is used to query the corresponding preset fault risk assessment model according to the determined device type in the cloud computing platform, and use the queried fault risk assessment model to perform fault risk assessment on the devices of the same type as the device that has reached the resource shortage state, and eliminate the devices of the same type as the device that has reached the resource shortage state, and the devices with the assessment results showing that there is a fault risk; The device resource scheduling execution module 104 is used to query the corresponding preset resource scheduling strategy according to the determined device type in the cloud computing platform, and complete the resource scheduling of the device that has reached the resource-scarce state and the device of the same type as the device that has reached the resource-scarce state according to the resources of the devices that have completed the elimination processing and are of the same type as the device that has reached the resource-scarce state and the preset resource scheduling strategy that has been queried.

[0055] In a specific embodiment, the device resource status judgment module 102 in the system is also used to use the deep learning algorithm in the cloud computing platform to predict the operation status and resource usage of each device, and judge whether each device reaches a resource-constrained state after a preset time. If there is a device that reaches a resource-constrained state after a preset time, the device that reaches a resource-constrained state after the preset time is supplemented to the device type determined to reach a resource-constrained state.

[0056] In a specific embodiment, the device execution scheduling screening module 103 in the system is also used to use the deep learning algorithm in the cloud computing platform to automatically identify the application scenarios of each device connected to the device; determine the device application scenario corresponding to the device that has reached the resource-constrained state, match the corresponding preset security risk level requirements based on the determined device application scenario, and set the corresponding preset security risk level requirements for different device application scenarios; query the corresponding preset security risk level assessment model in the cloud computing platform according to the determined device type, and use the queried security risk level assessment model to assess the security risk level of devices of the same type as the device that has reached the resource-constrained state, and eliminate devices of the same type as the device that has reached the resource-constrained state whose assessment results are lower than the preset security risk level.

[0057] In a specific embodiment, the device execution scheduling screening module 103 in the system is also used to match the corresponding preset disaster recovery performance level requirements based on the determined device application scenarios in the cloud computing platform, and set corresponding disaster recovery performance level requirements for different device application scenarios; for devices of the same type as the device that has reached the resource shortage state, the disaster recovery performance level of each device is determined according to the historical disaster recovery drill test results of each device, and the devices of the same type as the device that has reached the resource shortage state whose disaster recovery performance level is lower than the preset disaster recovery performance level requirements are eliminated; It is also used to determine that when the ratio of devices of the same type as the devices that have reached a resource-constrained state before and after the elimination process is completed is lower than a preset ratio, some devices are reselected from the eliminated devices whose disaster recovery performance levels are lower than the preset disaster recovery performance level requirements for supplementation and restoration in descending order of the disaster recovery performance levels of each device, and the supplementation and restoration operation is stopped when the ratio of devices of the same type as the devices that have reached a resource-constrained state before and after the elimination process is completed is not lower than the preset ratio.

[0058] The embodiment of the present application also discloses a computer-readable storage medium.

[0059] Specifically, the computer-readable storage medium stores a computer program that can be loaded by a processor and execute the above-mentioned cloud computing-based device management method. The computer-readable storage medium includes, for example: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0060] The embodiment of the present application also discloses a computer device.

[0061] Specifically, the computer device includes a memory and a processor, and the memory stores a computer program that can be loaded by the processor and execute the above-mentioned cloud computing-based device management method.

[0062] The above are all preferred embodiments of the present application, and are not intended to limit the protection scope of the present application. Any feature disclosed in this specification (including the abstract and drawings), unless otherwise stated, can be replaced by other equivalent or alternative features with similar purposes. That is, unless otherwise stated, each feature is only an example of a series of equivalent or similar features.

Claims

1. A device management method based on cloud computing, characterized in that: include: Identify and connect each device to the cloud computing platform, obtain the device information of each device, and use the deep learning algorithm in the cloud computing platform to automatically identify the key information of each device connected; The key information includes: device identification, device type, device resource usage, and device operation status; Use the deep learning algorithm in the cloud computing platform to judge the operation status and resource usage of each device, and judge whether each device has reached a resource shortage state. If there is a device that has reached a resource shortage state, determine the device type of the device that has reached the resource shortage state and the devices of the same type as the device that has reached the resource shortage state; In the cloud computing platform, a preset fault risk assessment model is queried according to the determined device type, and the queried fault risk assessment model is used to perform fault risk assessment on devices of the same type as the device that has reached the resource shortage state, and devices of the same type as the device that has reached the resource shortage state that are assessed to have fault risks are eliminated; In the cloud computing platform, the corresponding preset resource scheduling strategy is queried according to the determined device type, and the resource scheduling of the device that has reached the resource-scarce state and the device of the same type as the device that has reached the resource-scarce state is completed according to the resources of the devices that have been eliminated and the preset resource scheduling strategy is queried to complete the resource scheduling of the device that has reached the resource-scarce state and the devices that are of the same type as the device that has reached the resource-scarce state.

2. The device management method based on cloud computing according to claim 1, characterized in that: Also includes: Use deep learning algorithms in the cloud computing platform to automatically identify the application scenarios of connected devices; Determine the device application scenarios corresponding to the devices that have reached the resource-constrained state, match the corresponding preset security risk level requirements based on the determined device application scenarios, and set corresponding preset security risk level requirements for different device application scenarios; In the cloud computing platform, a preset security risk level assessment model is queried according to the determined device type, and the queried security risk level assessment model is used to perform security risk level assessment on devices of the same type as the device that has reached a resource-constrained state, and devices of the same type as the device that has reached a resource-constrained state whose assessment results are lower than the preset security risk level are eliminated.

3. The device management method based on cloud computing according to claim 2, characterized in that: Also includes: In the cloud computing platform, the preset disaster recovery performance level requirements are matched based on the determined device application scenarios, and the corresponding disaster recovery performance level requirements are set for different device application scenarios; For devices of the same type as the device that has reached a resource-constrained state, the disaster recovery performance level of each device is determined based on the historical disaster recovery drill test results of each device, and devices of the same type as the device that has reached a resource-constrained state whose disaster recovery performance level is lower than the preset disaster recovery performance level requirement are eliminated.

4. The device management method based on cloud computing according to claim 3, characterized in that: In the process of completing resource scheduling of devices that have reached a resource-constrained state and devices of the same type as the devices that have reached a resource-constrained state, when the resource gap between some devices that follow the resource scheduling strategy is less than the preset resource gap, devices with a high device security risk assessment level or a high disaster recovery performance level are preferentially selected for resource scheduling.

5. The device management method based on cloud computing according to claim 3, characterized in that: During the resource scheduling process of the devices that have reached a resource-constrained state and the devices of the same type as the devices that have reached a resource-constrained state, when the resource gap between some devices that follow the resource scheduling strategy is less than the preset resource gap, the historical scheduling times of these devices are counted, and the devices with the lowest historical scheduling times are preferentially selected for resource scheduling.

6. The device management method based on cloud computing according to claim 1, characterized in that: Also includes: The deep learning algorithm in the cloud computing platform is used to predict the operation status and resource usage of each device, and determine whether each device will reach a resource-constrained state after the preset time. If there is a device that reaches a resource-constrained state after the preset time, the device that reaches the resource-constrained state after the preset time is supplemented to the device type that has reached the resource-constrained state.

7. The device management method based on cloud computing according to claim 1, characterized in that: Also includes: When it is determined that the ratio of devices of the same type as the devices that have reached a resource-constrained state before and after the elimination process is completed is lower than a preset ratio, some devices are reselected from the eliminated devices whose disaster recovery performance levels are lower than the preset disaster recovery performance level requirements for supplementation and restoration in descending order of the disaster recovery performance levels of each device, until the ratio of devices of the same type as the devices that have reached a resource-constrained state before and after the elimination process is completed is not lower than a preset ratio, and the supplementation and restoration operation is stopped.

8. A cloud computing-based equipment management system, characterized in that: include: The device key information identification module is used to identify and connect each device to the cloud computing platform, obtain the device information of each device, and use the deep learning algorithm in the cloud computing platform to automatically identify the key information of each device connected to the device; The key information includes: device identification, device type, device resource usage, and device operation status; The device resource status judgment module is used to use the deep learning algorithm in the cloud computing platform to judge the operation status and resource usage of each device, and judge whether each device has reached a resource shortage state. If there is a device that has reached a resource shortage state, the device type of the device that has reached the resource shortage state and the device of the same type as the device that has reached the resource shortage state are determined; The device execution scheduling screening module is used to query the corresponding preset fault risk assessment model according to the determined device type in the cloud computing platform, and use the queried fault risk assessment model to perform fault risk assessment on the devices of the same type as the device that has reached the resource shortage state, and eliminate the devices of the same type as the device that has reached the resource shortage state, and the devices with the assessment results showing that there is a fault risk; The device resource scheduling execution module is used to query the corresponding preset resource scheduling strategy according to the determined device type in the cloud computing platform, and complete the resource scheduling of the device that has reached the resource-scarce state and the device of the same type as the device that has reached the resource-scarce state according to the resources of the devices that have completed the elimination processing and belong to the same type as the device that has reached the resource-scarce state and the preset resource scheduling strategy that has been queried.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the method according to any one of claims 1 to 7.

10. A computer device, characterized in that: The computer device comprises a memory, a processor and a program stored and executable on the memory, and the program implements the steps of the method according to any one of claims 1 to 7 when executed by the processor.

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