Terminal equipment management method, system and equipment of power operating system and storage medium

Abstract

The invention discloses a terminal equipment management method, system and equipment of a power operation system and a storage medium. The method comprises the following steps: collecting equipment operation information of at least one terminal equipment connected to a power operation system and first load information of the power operation system; inputting the equipment operation information and the first load information into a pre-trained system load prediction model, and determining second load information corresponding to the power operation system based on a model output result, the load demand prediction model is obtained by training a neural network model based on historical equipment operation information and historical load information corresponding to the historical equipment operation information; and determining a resource scheduling strategy corresponding to the terminal equipment based on the second load information and the equipment operation information, and performing resource scheduling on at least one terminal equipment connected to the power operation system based on the resource scheduling strategy. The resource scheduling of the power operation system is optimized, and the intelligent level of the power operation system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system equipment management, and in particular to a terminal equipment management method, system, equipment and storage medium of a power operation system. Background Art

[0002] As the core support of the power operating system, the Dianhong operating system needs to have strong data analysis capabilities to cope with frequent version iterations, strict reliability requirements and diverse hardware platform challenges.

[0003] Power system equipment management is crucial to ensure the safe, stable and efficient operation of the power system. Traditional power operating systems often adopt a monolithic architecture. When the system scale expands or the demand changes, the development and maintenance costs are extremely high. When faced with a large number of terminal devices, it is impossible to quickly and accurately determine the resource allocation strategy corresponding to each terminal device, which easily leads to the risk of failure of terminal devices due to insufficient resources or overload. Summary of the invention

[0004] The present invention provides a terminal device management method, system, device and storage medium of an electric power operation system to solve the problem of failure risk caused by insufficient resources or overload of terminal devices.

[0005] According to one aspect of the present invention, a terminal device management method of a power operation system is provided, the method comprising:

[0006] Collecting equipment operation information of at least one terminal device connected to the power operation system and first load information of the power operation system;

[0007] Inputting the equipment operation information and the first load information into a pre-trained system load prediction model, and determining second load information corresponding to the power operation system based on a model output result, wherein the load demand prediction model is obtained by training a neural network model based on historical equipment operation information and historical load information corresponding to the historical equipment operation information;

[0008] A resource scheduling strategy corresponding to the terminal device is determined based on the second load information and the device operation information, and resources are scheduled for at least one of the terminal devices connected to the power operation system based on the resource scheduling strategy.

[0009] According to another aspect of the present invention, a terminal equipment management system of a power operation system is provided, the system comprising:

[0010] An information acquisition module, used to collect device operation information of at least one terminal device connected to the power operation system and first load information of the power operation system;

[0011] A load prediction module, used for inputting the equipment operation information and the first load information into a pre-trained system load prediction model, and determining second load information corresponding to the power operation system based on a model output result, wherein the load demand prediction model is obtained by training a neural network model based on historical equipment operation information and historical load information corresponding to the historical equipment operation information;

[0012] A resource scheduling module is used to determine a resource scheduling strategy corresponding to the terminal device based on the second load information and the device operation information, and to perform resource scheduling on at least one of the terminal devices connected to the power operation system based on the resource scheduling strategy.

[0013] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0014] at least one processor; and

[0015] a memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the terminal device management method of the power operation system described in any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the terminal device management method of the power operating system described in any embodiment of the present invention when executed.

[0018] The technical solution of the embodiment of the present invention is to collect the device operation information of at least one terminal device connected to the power operation system and the first load information of the power operation system; provide accurate information for the system load prediction model to make accurate predictions later; then, input the device operation information and the first load information into the pre-trained system load prediction model, and determine the second load information corresponding to the power operation system based on the model output result, wherein the load demand prediction model is obtained by training the neural network model based on the historical device operation information and the historical load information corresponding to the historical device operation information; by predicting future load information, the power operation system can plan and schedule in advance to avoid resource shortage or overload; finally, determine the resource scheduling strategy corresponding to the terminal device based on the second load information and the device operation information, and perform resource scheduling on at least one terminal device connected to the power operation system based on the resource scheduling strategy. According to the accurate prediction results and the operation status of the equipment, a more accurate resource scheduling strategy can be formulated to ensure that each device obtains sufficient resources when needed, improve resource utilization efficiency, solve the problem of failure risk caused by insufficient resources or overload of terminal equipment, and achieve the beneficial effects of reducing operation and maintenance costs, improving resource utilization efficiency, and improving the stability and reliability of the power grid.

[0019] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 is a flowchart of a terminal device management method of a power operation system provided according to Embodiment 1 of the present invention;

[0022] Figure 2 is a flow chart of a terminal device management method of a power operation system provided according to a second embodiment of the present invention;

[0023] Figure 3 This is a schematic diagram of the structure of a terminal device management system of a power operation system provided according to Embodiment 3 of the present invention;

[0024] Figure 4It is a structural schematic diagram of an electronic device for implementing the terminal device management method of the power operation system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0026] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0027] Embodiment 1

[0028] Figure 1 A flowchart of a terminal device management method of an electric power operation system is provided for the first embodiment of the present invention. This embodiment is applicable to the case of terminal device management. The method can be executed by a terminal device management system of an electric power operation system. The terminal device management system of the electric power operation system can be implemented in the form of hardware and / or software. The terminal device management system of the electric power operation system can be configured in an electronic device. Figure 1 As shown, the method includes:

[0029] S110: Collect device operation information of at least one terminal device connected to a power operation system and first load information of the power operation system.

[0030] Among them, the power operating system can be understood as a power Internet of Things operating system, such as: Dianhong operating system. The device operation information can be understood as information related to the device operation status, performance and operation. The first load information can be understood as the current load information of the system. The first load information can include parameters such as power consumption, current and voltage.

[0031] Specifically, a distributed link tracking system is used to track the call chain across services and components of the operation process of at least one terminal device in the Dianhong operating system to obtain the device operation information of at least one terminal device.

[0032] Optionally, the device operation information includes data transmission rate, response time and resource utilization; after collecting the device operation information of at least one terminal device connected to the power operation system, it also includes: performing abnormality detection on the device operation information, and generating abnormal prompt information when the device operation information meets a preset abnormal condition;

[0033] The preset abnormal condition includes at least one of the following:

[0034] The data transmission rate is lower than a preset rate threshold;

[0035] The response time exceeds a preset response threshold;

[0036] The resource utilization is higher than a preset utilization threshold.

[0037] Among them, the abnormal prompt information can be understood as the notification information automatically generated by the system and sent to the management personnel when it is detected that the equipment operation information meets the preset abnormal conditions during the operation of the system or equipment.

[0038] Specifically, real-time abnormality detection is performed on the device operation information to determine whether the device operation information is within a normal range. If the collected data transmission rate is lower than the preset rate threshold, it indicates that there is a problem with the device communication or data loss. If the response time of the device exceeds the preset response threshold, it indicates that the device performance has declined or a fault has occurred, and it cannot respond to system instructions in time. If the resource utilization of the device is higher than the preset utilization threshold, it indicates that the device is overloaded and there is a resource bottleneck or a performance bottleneck. When the device operation information meets any of the above preset abnormal conditions, the system will automatically generate abnormal prompt information. The abnormal prompt information may include key information such as device name, abnormal type, abnormal time, abnormal value, and recommended processing measures. Among them, the preset rate threshold, preset response threshold, and preset utilization threshold are reasonably set according to the actual situation and operation requirements of the device, and this embodiment does not limit them.

[0039] Exemplarily, the equipment operation information is stored in a time series database, and the stored performance data is analyzed in real time through real-time data stream processing technology. When the equipment operation information meets preset abnormal conditions, an early warning mechanism is triggered.

[0040] S120: Input the equipment operation information and the first load information into a pre-trained system load prediction model, and determine second load information corresponding to the power operation system based on the model output result.

[0041] The load demand prediction model is obtained by training a neural network model based on historical equipment operation information and historical load information corresponding to the historical equipment operation information. The second load information can be understood as system load information predicted in a future time period. The second load information includes load values, load change trends, and load peaks in a future preset time period.

[0042] Specifically, the equipment operation information and the first load information are used as input data and sent to a pre-trained system load prediction model. The system load prediction model uses machine learning or deep learning algorithms to process and analyze the input data. The model predicts the load of the power operation system in the future based on historical data and current input data. The second load information corresponding to the power operation system is determined based on the prediction result output by the model.

[0043] Optionally, before inputting the equipment operation information and the first load information into a pre-trained system load prediction model and determining the second load information corresponding to the power operation system based on the model output result, it also includes: constructing a sample data set, and dividing the sample data set into a training set and a validation set; wherein the data set includes historical equipment operation information within a preset historical time period, a timestamp corresponding to the historical equipment operation information, and historical load information corresponding to the historical equipment operation information; iteratively training a pre-constructed initial neural network model based on the training set and a preset training cycle to obtain the predicted load information output by the model, determining a model error based on the predicted load information and the historical load information, and adjusting model parameters based on the model error; after each training cycle, evaluating the model performance indicators of the model through the validation set, and determining the system load prediction model based on multiple model performance indicators.

[0044] The historical load information includes historical total load, historical branch load and historical load change trend information.

[0045] Specifically, historical equipment operation information within a preset historical time period is collected, and the historical equipment operation information includes data transmission rate, response time, resource utilization, etc. The timestamp corresponding to the historical equipment operation information is determined for subsequent time series analysis. Historical load information corresponding to the historical equipment operation information, such as power consumption, current, and voltage, etc., is collected. The collected data is cleaned to remove outliers and missing values. The data is normalized or standardized to improve the training efficiency and prediction performance of the model. The processed data set is divided into a training set and a validation set. The training set is used for model training, and the validation set is used to evaluate the performance of the model. Based on the training set and the preset training cycle, the initial neural network model is iteratively trained. In each training cycle, the data of the training set is input into the model to obtain the predicted load information. The model error between the predicted load information and the historical load information is calculated, such as the mean square error, the mean absolute error, etc. According to the model error, the parameters of the model are adjusted using the back propagation algorithm to reduce the error and improve the prediction accuracy. After each training cycle, the performance of the model is evaluated using the validation set, and multiple model performance indicators are calculated. According to the model performance indicators on the validation set, the model with the best performance is selected as the final system load prediction model.

[0046] S130. Determine a resource scheduling strategy corresponding to the terminal device based on the second load information and the device operation information, and perform resource scheduling on at least one of the terminal devices connected to the power operation system based on the resource scheduling strategy.

[0047] Among them, the resource scheduling strategy may include a priority scheduling strategy and a dynamic resource adjustment strategy.

[0048] Specifically, according to the predicted second load information, dynamically adjust the resource allocation of the terminal devices connected to the power operating system. During peak load periods, increase resource support for key devices to ensure stable operation of the system. During low load periods, release some resources for use by other devices to improve resource utilization. Optionally, set different priorities for different types of terminal devices or tasks. When resources are tight, give priority to ensuring the resource requirements of high-priority devices. Through priority scheduling, ensure that key businesses can obtain more resource support when resources are tight. For example, when the system load is high, terminal devices with good performance and low resource utilization are preferentially scheduled to handle system tasks.

[0049] Optionally, the method further includes:

[0050] For each of the terminal devices, a security check is performed on the terminal device to obtain a security check result; wherein the security check includes security vulnerability scanning and / or intrusion detection, and the security check result includes at least one of a vulnerability detection result, a vulnerability type and a vulnerability risk level; when the vulnerability detection result indicates that a security vulnerability exists, a repair strategy corresponding to the terminal device is determined based on the vulnerability type and the vulnerability risk level.

[0051] Among them, the repair strategy can be understood as a vulnerability repair strategy. The repair strategy may include closing dangerous ports and updating encryption algorithms.

[0052] Specifically, security checks are performed on the network connections and data storage of terminal devices based on the preset security check cycle. If security loopholes are found, such as unauthorized opening of network ports and incomplete data encryption, remediation measures are taken, such as closing dangerous ports and updating encryption algorithms. At the same time, a branch device security event log is established to record all security-related operations and events for easy traceability and analysis, thereby ensuring the safe operation of terminal devices in the Dianhong operating system.

[0053] For example, the security test suite combines the containerized security upgrade environment to conduct comprehensive security vulnerability scanning, intrusion detection, and firmware version security upgrades on terminal devices. Timely discover and repair network security vulnerabilities and encryption defects in branch devices, ensure data security and stable operation of terminal devices in the Dianhong operating system, reduce the risk of system failures caused by security issues, and improve the overall reliability and security of the system.

[0054] Optionally, the method also includes: tracking a service call chain associated with at least one of the terminal devices to obtain device association information corresponding to the terminal device; registering the device association information of at least one of the terminal devices in the power operating system, and establishing a terminal device information library based on the registered device association information.

[0055] The device-related information includes at least one of device model, hardware configuration, firmware version and network connection parameters.

[0056] Specifically, the collection component deployed by containerization technology is used to collect information from terminal devices connected to the power operating system, and the device association information of at least one terminal device is registered in the power operating system. After successful registration, the device association information will be stored in the database of the power operating system. By establishing a terminal device information library, this information can be easily managed and queried.

[0057] In the embodiment of the present invention, by establishing a terminal device information database, the power operation system can achieve comprehensive monitoring and management of the terminal devices, thereby improving the reliability and security of the power grid.

[0058] Optionally, the method also includes: when the solid-state version of the terminal device meets preset update conditions, obtaining a target update version file from a version library, and updating the solid-state version of the terminal device based on the target update version file; wherein the preset update conditions include that the firmware version of the terminal device is inconsistent with a pre-stored standard firmware version, and / or, receiving a version update instruction for the terminal device.

[0059] Specifically, the current firmware version of the terminal device is regularly checked to see if it is consistent with the pre-stored standard firmware version. If a difference is found between the current firmware version and the standard firmware version, the system will deem that the terminal device needs to update its firmware version. And / or, the system receives a version update instruction issued by an administrator and deems that the terminal device needs to update its firmware version. The system queries a version library storing various firmware versions to find a target update version file that matches the terminal device. After determining the target update version file, the system downloads the target update version file from the version library, uploads the target update version file to the terminal device, and updates the solid-state version of the terminal device based on the target update version file.

[0060] The technical solution of the embodiment of the present invention is to collect the device operation information of at least one terminal device connected to the power operation system and the first load information of the power operation system; provide accurate information for the system load prediction model to make accurate predictions later; then, input the device operation information and the first load information into the pre-trained system load prediction model, and determine the second load information corresponding to the power operation system based on the model output result, wherein the load demand prediction model is obtained by training the neural network model based on the historical device operation information and the historical load information corresponding to the historical device operation information; by predicting future load information, the power operation system can plan and schedule in advance to avoid resource shortage or overload; finally, determine the resource scheduling strategy corresponding to the terminal device based on the second load information and the device operation information, and perform resource scheduling on at least one terminal device connected to the power operation system based on the resource scheduling strategy. According to the accurate prediction results and the operation status of the equipment, a more accurate resource scheduling strategy can be formulated to ensure that each device obtains sufficient resources when needed, improve resource utilization efficiency, solve the problem of failure risk caused by insufficient resources or overload of terminal equipment, and achieve the beneficial effects of reducing operation and maintenance costs, improving resource utilization efficiency, and improving the stability and reliability of the power grid.

[0061] Embodiment 2

[0062] Figure 2A flowchart of a terminal device management method for an electric power operating system provided in Embodiment 2 of the present invention, this embodiment is a further refinement of how the relationship in the above embodiment determines the resource scheduling strategy corresponding to the terminal device based on the second load information and the device operation information. Optionally, the resource scheduling strategy corresponding to the terminal device based on the second load information and the device operation information includes: determining the load fluctuation trend information of the electric power operating system within the predicted time based on the second load information; determining the resource scheduling strategy corresponding to the terminal device based on the load fluctuation trend information and the device operation information.

[0063] like Figure 2 As shown, the method includes:

[0064] S210: Collect device operation information of at least one terminal device connected to a power operation system and first load information of the power operation system.

[0065] S220: Input the equipment operation information and the first load information into a pre-trained system load prediction model, and determine second load information corresponding to the power operation system based on the model output result.

[0066] S230. Determine load fluctuation trend information of the power operating system within a predicted time based on the second load information.

[0067] The load fluctuation trend information can be understood as the load fluctuation trend information of the power operation system within the forecast time. The load fluctuation trend information can include an upward trend, a downward trend, a stable trend, and a mutation point.

[0068] Specifically, the second load information is analyzed to obtain information such as the size, change trend and periodicity of the load within the predicted time corresponding to the power operating system, and based on the analysis results, the load fluctuation trend information of the power operating system within the predicted time is determined.

[0069] S240. Determine a resource scheduling strategy corresponding to the terminal device based on the load fluctuation trend information and the device operation information, and perform resource scheduling on at least one of the terminal devices connected to the power operation system based on the resource scheduling strategy.

[0070] Specifically, determine the resources allocated to each terminal device and the resource utilization rate. Evaluate the allocated resources and resource utilization rate of each terminal device. Adjust the resource allocation of the terminal device according to the resource scheduling strategy. Monitor the system performance after resource scheduling in real time, including response time, throughput, resource utilization, etc., to ensure that the scheduling effect meets expectations. Make necessary adjustments and optimizations to the resource scheduling strategy based on the monitoring results. If it is found that some strategies fail to achieve the expected results, or the system environment changes (such as changes in load fluctuation trends, new equipment access, etc.), update the strategy in a timely manner.

[0071] For example, the forecast results show that in the coming week, the power demand in area A will peak on weekends and be relatively low on weekdays. At the same time, the maintenance record of a generator equipment shows that it is about to reach the scheduled maintenance cycle. Formulate the following resource scheduling strategy: reduce the output of the generator on weekdays to reduce its load pressure. At the same time, use other generators or renewable energy to meet the power demand. During the peak power demand period on weekends, increase the output of the generator and work with other equipment to ensure the stability of power supply.

[0072] Optionally, the method further includes: regularly checking the firmware status of the terminal device, and if a firmware vulnerability is detected and a new firmware with performance optimization is released, updating the terminal device firmware using a containerized upgrade environment.

[0073] The technical solution of the embodiment of the present invention determines the load fluctuation trend information of the power operating system within the predicted time based on the second load information; determines the resource scheduling strategy corresponding to the terminal device based on the load fluctuation trend information and the device operation information. According to the load fluctuation trend information, the power operating system can predict future resource requirements and dynamically adjust resource allocation accordingly, thereby improving resource utilization efficiency and enhancing system stability and reliability.

[0074] Embodiment 3

[0075] Figure 3 This is a schematic diagram of the structure of a terminal device management system of a power operation system provided by the third embodiment of the present invention. Figure 3 As shown, the device includes: an information acquisition module 310, a load prediction module 320 and a resource scheduling module 330.

[0076] Among them, the information acquisition module 310 is used to collect the equipment operation information of at least one terminal device connected to the power operating system and the first load information of the power operating system; the load prediction module 320 is used to input the equipment operation information and the first load information into a pre-trained system load prediction model, and determine the second load information corresponding to the power operating system based on the model output result, wherein the load demand prediction model is obtained by training a neural network model based on historical equipment operation information and historical load information corresponding to the historical equipment operation information; the resource scheduling module 330 is used to determine the resource scheduling strategy corresponding to the terminal device based on the second load information and the equipment operation information, and perform resource scheduling for at least one of the terminal devices connected to the power operating system based on the resource scheduling strategy.

[0077] The technical solution of the embodiment of the present invention is to collect the device operation information of at least one terminal device connected to the power operation system and the first load information of the power operation system; provide accurate information for the system load prediction model to make accurate predictions later; then, input the device operation information and the first load information into the pre-trained system load prediction model, and determine the second load information corresponding to the power operation system based on the model output result, wherein the load demand prediction model is obtained by training the neural network model based on the historical device operation information and the historical load information corresponding to the historical device operation information; by predicting future load information, the power operation system can plan and schedule in advance to avoid resource shortage or overload; finally, determine the resource scheduling strategy corresponding to the terminal device based on the second load information and the device operation information, and perform resource scheduling on at least one terminal device connected to the power operation system based on the resource scheduling strategy. According to the accurate prediction results and the operation status of the equipment, a more accurate resource scheduling strategy can be formulated to ensure that each device obtains sufficient resources when needed, improve resource utilization efficiency, solve the problem of failure risk caused by insufficient resources or overload of terminal equipment, and achieve the beneficial effects of reducing operation and maintenance costs, improving resource utilization efficiency, and improving the stability and reliability of the power grid.

[0078] Optionally, the resource scheduling module includes:

[0079] a fluctuation trend determining unit, configured to determine load fluctuation trend information of the power operation system within a predicted time based on the second load information;

[0080] A resource scheduling strategy determining unit is used to determine the resource scheduling strategy corresponding to the terminal device based on the load fluctuation trend information and the device operation information.

[0081] Optionally, the device operation information includes data transmission rate, response time and resource utilization; accordingly, the system further includes:

[0082] The abnormality prompt module is used to perform abnormality detection on the device operation information after collecting the device operation information of at least one terminal device connected to the power operation system, and generate abnormality prompt information when the device operation information meets the preset abnormal conditions.

[0083] The preset abnormal condition includes at least one of the following:

[0084] The data transmission rate is lower than a preset rate threshold;

[0085] The response time exceeds a preset response threshold;

[0086] The resource utilization is higher than a preset utilization threshold.

[0087] Optionally, the system further includes:

[0088] A data set construction module, used for constructing a sample data set before inputting the device operation information and the first load information into a pre-trained system load prediction model and determining the second load information corresponding to the power operation system based on the model output result, and dividing the sample data set into a training set and a validation set; wherein the data set includes historical device operation information within a preset historical time period, a timestamp corresponding to the historical device operation information, and historical load information corresponding to the historical device operation information;

[0089] A model training module, configured to iteratively train a pre-built initial neural network model based on the training set and a preset training cycle to obtain predicted load information output by the model, determine a model error based on the predicted load information and the historical load information, and adjust model parameters based on the model error;

[0090] The model determination module is used to evaluate the model performance index of the model through the verification set after each training cycle, and determine the load demand prediction model based on multiple model performance indicators.

[0091] Optionally, the system further includes:

[0092] A security detection module, used for performing security detection on each terminal device to obtain a security detection result; wherein the security detection includes security vulnerability scanning and / or intrusion detection, and the security detection result includes at least one of a vulnerability detection result, a vulnerability type, and a vulnerability risk level;

[0093] A repair strategy determination module is used to determine a repair strategy corresponding to the terminal device based on the vulnerability type and the vulnerability risk level when the vulnerability detection result is that a security vulnerability exists.

[0094] Optionally, the system further includes:

[0095] an associated information acquisition module, used to track a service call chain associated with at least one of the terminal devices to obtain device associated information corresponding to the terminal device, wherein the device associated information includes at least one of a device model, hardware configuration, firmware version, and network connection parameters;

[0096] The information base establishment module is used to register the device association information of at least one of the terminal devices in the power operation system, and establish a terminal device information base based on the registered device association information.

[0097] Optionally, the system further includes:

[0098] A version update module, used to obtain a target update version file from a version library when the solid-state version of the terminal device meets a preset update condition, and update the solid-state version of the terminal device based on the target update version file;

[0099] The preset update condition includes that the firmware version of the terminal device is inconsistent with the pre-stored standard firmware version, and / or that a version update instruction of the terminal device is received.

[0100] The terminal equipment management system of the power operating system provided in the embodiment of the present invention can execute the terminal equipment management method of the power operating system provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0101] Embodiment 4

[0102] Figure 4 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0103] like Figure 4As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0104] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0105] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The processor 11 executes the various methods and processes described above, such as the terminal device management of the method power operating system.

[0106] In some embodiments, the terminal device management of the method power operating system can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the terminal device management of the method power operating system described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to execute the terminal device management of the method power operating system in any other appropriate manner (for example, by means of firmware).

[0107] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0108] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0109] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0110] To provide interaction with a service acquirer, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the service acquirer; and a keyboard and a pointing device (e.g., a mouse or a trackball), through which the service acquirer can provide input to the electronic device. Other types of devices may also be used to provide interaction with a service acquirer; for example, the feedback provided to the service acquirer may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the service acquirer may be received in any form (including acoustic input, voice input, or tactile input).

[0111] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a service acquirer computer having a graphical service acquirer interface or a web browser through which the service acquirer can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0112] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0113] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0114] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A terminal device management method for a power operation system, characterized in that: include: Collecting equipment operation information of at least one terminal device connected to the power operation system and first load information of the power operation system; Inputting the equipment operation information and the first load information into a pre-trained system load prediction model, and determining second load information corresponding to the power operation system based on a model output result, wherein the load demand prediction model is obtained by training a neural network model based on historical equipment operation information and historical load information corresponding to the historical equipment operation information; A resource scheduling strategy corresponding to the terminal device is determined based on the second load information and the device operation information, and resources are scheduled for at least one of the terminal devices connected to the power operation system based on the resource scheduling strategy.

2. The method according to claim 1, characterized in that: The determining a resource scheduling strategy corresponding to the terminal device based on the second load information and the device operation information includes: Determine load fluctuation trend information of the power operation system within the predicted time based on the second load information; A resource scheduling strategy corresponding to the terminal device is determined based on the load fluctuation trend information and the device operation information.

3. The method according to claim 1, characterized in that The equipment operation information includes data transmission rate, response time and resource utilization; After collecting the equipment operation information of at least one terminal device connected to the power operation system, the method further includes: Performing abnormality detection on the device operation information, and generating abnormality prompt information when the device operation information meets a preset abnormality condition; The preset abnormal condition includes at least one of the following: The data transmission rate is lower than a preset rate threshold; The response time exceeds a preset response threshold; The resource utilization is higher than a preset utilization threshold.

4. The method according to claim 1, characterized in that: Before inputting the device operation information and the first load information into a pre-trained system load prediction model and determining the second load information corresponding to the power operation system based on the model output result, the method further includes: Constructing a sample data set, and dividing the sample data set into a training set and a validation set; wherein the data set includes historical equipment operation information within a preset historical time period, a timestamp corresponding to the historical equipment operation information, and historical load information corresponding to the historical equipment operation information; Iteratively training a pre-built initial neural network model based on the training set and a preset training cycle to obtain predicted load information output by the model, determining a model error based on the predicted load information and the historical load information, and adjusting model parameters based on the model error; After each training cycle, the model performance index of the model is evaluated through the validation set, and the load demand prediction model is determined based on multiple model performance indicators.

5. The method according to claim 1, characterized in that Also includes: For each of the terminal devices, perform security detection on the terminal device to obtain a security detection result; wherein the security detection includes security vulnerability scanning and / or intrusion detection, and the security detection result includes at least one of a vulnerability detection result, a vulnerability type, and a vulnerability risk level; When the vulnerability detection result indicates that a security vulnerability exists, a repair strategy corresponding to the terminal device is determined based on the vulnerability type and the vulnerability risk level.

6. The method according to claim 1, characterized in that Also includes: Tracking a service call chain associated with at least one of the terminal devices to obtain device association information corresponding to the terminal device, wherein the device association information includes at least one of a device model, a hardware configuration, a firmware version, and a network connection parameter; The device association information of at least one of the terminal devices is registered in the power operation system, and a terminal device information library is established based on the registered device association information.

7. The method according to claim 6, characterized in that Also includes: When the solid-state version of the terminal device meets a preset update condition, obtaining a target update version file from a version library, and updating the solid-state version of the terminal device based on the target update version file; The preset update condition includes that the firmware version of the terminal device is inconsistent with the pre-stored standard firmware version, and / or that a version update instruction of the terminal device is received.

8. A terminal equipment management system for a power operation system, characterized in that: include: An information acquisition module, used to collect device operation information of at least one terminal device connected to the power operation system and first load information of the power operation system; A load prediction module, used for inputting the equipment operation information and the first load information into a pre-trained system load prediction model, and determining second load information corresponding to the power operation system based on a model output result, wherein the load demand prediction model is obtained by training a neural network model based on historical equipment operation information and historical load information corresponding to the historical equipment operation information; A resource scheduling module is used to determine a resource scheduling strategy corresponding to the terminal device based on the second load information and the device operation information, and to perform resource scheduling on at least one of the terminal devices connected to the power operation system based on the resource scheduling strategy.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the terminal device management method of the power operation system described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the terminal device management method of the power operation system according to any one of claims 1 to 7 when executed.

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