CN117437083A - Power grid edge cluster monitoring method based on cloud edge fusion intelligent scheduling operation platform - Google Patents
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Abstract
The application relates to a power grid edge cluster monitoring method based on a cloud edge fusion intelligent scheduling operation platform. The method comprises the following steps: acquiring edge cluster monitoring data corresponding to the power grid edge clusters according to the data monitoring rule data of the power grid edge clusters; performing abnormal data inspection on the edge cluster monitoring data to obtain data inspection information corresponding to the power grid edge clusters; extracting edge cluster abnormal data from the edge cluster monitoring data under the condition that the data inspection information represents that the abnormal data exists in the edge cluster monitoring data; inputting the edge cluster abnormal data into an abnormal data analysis model of the power grid edge cluster to obtain abnormal data analysis information; and generating cluster monitoring strategy information corresponding to the power grid edge clusters according to the abnormal data analysis information. The method can achieve the state of 'checking when checking', and improves the abnormal condition discovery efficiency of the power grid edge cluster.
Description
Technical Field
The application relates to the technical field of computers, in particular to a power grid edge cluster monitoring method, device, computer equipment, storage medium and computer program product based on a cloud edge fusion intelligent scheduling operation platform.
Background
With the development of computer technology, a smart grid monitoring platform is developed, and the monitoring platform is based on an integrated high-speed two-way communication network, and is an information transmission and management system based on the smart grid in a full-digital mode through application of advanced sensing and measuring technologies, advanced equipment technologies, advanced control methods and advanced decision support system technologies. On the basis of the construction of the intelligent power grid monitoring platform, a power grid edge cluster appears and is used as an intermediate medium between the intelligent power grid monitoring platform and power grid equipment.
However, in the using process of the power grid edge cluster, abnormal conditions are inevitably generated, so that monitoring of the power grid edge cluster is particularly important, in the traditional technology, the power grid edge cluster is monitored by adopting a 'regular monitoring' operation mode, but the monitoring process is time-consuming and labor-consuming, the period is long, the reliability and the instantaneity of a monitoring result are poor, and the abnormal conditions of the power grid edge cluster are found to be low in efficiency.
Disclosure of Invention
Based on the foregoing, it is necessary to provide a method, an apparatus, a computer device, a computer readable storage medium and a computer program product for monitoring a grid edge cluster based on a cloud edge fusion intelligent scheduling operation platform, which can improve the efficiency of discovering abnormal situations of the grid edge cluster.
In a first aspect, the application provides a power grid edge cluster monitoring method based on a cloud edge fusion intelligent scheduling operation platform. The method comprises the following steps: acquiring edge cluster monitoring data corresponding to a power grid edge cluster according to data monitoring rule data of the power grid edge cluster; performing abnormal data inspection on the edge cluster monitoring data to obtain data inspection information corresponding to the power grid edge clusters; extracting edge cluster abnormal data from the edge cluster monitoring data under the condition that the data checking information characterizes that the abnormal data exists in the edge cluster monitoring data; inputting the edge cluster abnormal data into an abnormal data analysis model of the power grid edge cluster to obtain abnormal data analysis information; and generating cluster monitoring strategy information corresponding to the power grid edge clusters according to the abnormal data analysis information.
In a second aspect, the application further provides a power grid edge cluster monitoring device based on the cloud edge fusion intelligent scheduling operation platform. The device comprises: the monitoring data acquisition module is used for acquiring edge cluster monitoring data corresponding to the power grid edge clusters according to data monitoring rule data of the power grid edge clusters; the abnormal data checking module is used for checking the abnormal data of the edge cluster monitoring data to obtain data checking information corresponding to the power grid edge cluster; the abnormal data extraction module is used for extracting edge cluster abnormal data from the edge cluster monitoring data under the condition that the data inspection information characterizes that the edge cluster monitoring data exist abnormal data; the abnormal data analysis module is used for inputting the edge cluster abnormal data into an abnormal data analysis model of the power grid edge cluster to obtain abnormal data analysis information; and the monitoring strategy generation module is used for generating cluster monitoring strategy information corresponding to the power grid edge clusters according to the abnormal data analysis information.
In a third aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor which when executing the computer program performs the steps of: acquiring edge cluster monitoring data corresponding to a power grid edge cluster according to data monitoring rule data of the power grid edge cluster; performing abnormal data inspection on the edge cluster monitoring data to obtain data inspection information corresponding to the power grid edge clusters; extracting edge cluster abnormal data from the edge cluster monitoring data under the condition that the data checking information characterizes that the abnormal data exists in the edge cluster monitoring data; inputting the edge cluster abnormal data into an abnormal data analysis model of the power grid edge cluster to obtain abnormal data analysis information; and generating cluster monitoring strategy information corresponding to the power grid edge clusters according to the abnormal data analysis information.
In a fourth aspect, the present application also provides a computer-readable storage medium. The computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of: acquiring edge cluster monitoring data corresponding to a power grid edge cluster according to data monitoring rule data of the power grid edge cluster; performing abnormal data inspection on the edge cluster monitoring data to obtain data inspection information corresponding to the power grid edge clusters; extracting edge cluster abnormal data from the edge cluster monitoring data under the condition that the data checking information characterizes that the abnormal data exists in the edge cluster monitoring data; inputting the edge cluster abnormal data into an abnormal data analysis model of the power grid edge cluster to obtain abnormal data analysis information; and generating cluster monitoring strategy information corresponding to the power grid edge clusters according to the abnormal data analysis information.
In a fifth aspect, the present application also provides a computer program product. The computer program product comprises a computer program which, when executed by a processor, implements the steps of: acquiring edge cluster monitoring data corresponding to a power grid edge cluster according to data monitoring rule data of the power grid edge cluster; performing abnormal data inspection on the edge cluster monitoring data to obtain data inspection information corresponding to the power grid edge clusters; extracting edge cluster abnormal data from the edge cluster monitoring data under the condition that the data checking information characterizes that the abnormal data exists in the edge cluster monitoring data; inputting the edge cluster abnormal data into an abnormal data analysis model of the power grid edge cluster to obtain abnormal data analysis information; and generating cluster monitoring strategy information corresponding to the power grid edge clusters according to the abnormal data analysis information.
According to the power grid edge cluster monitoring method, the device, the computer equipment, the storage medium and the computer program product based on the cloud edge fusion intelligent scheduling operation platform, edge cluster monitoring data corresponding to the power grid edge clusters are obtained according to the data monitoring rule data of the power grid edge clusters; performing abnormal data inspection on the edge cluster monitoring data to obtain data inspection information corresponding to the power grid edge clusters; extracting edge cluster abnormal data from the edge cluster monitoring data under the condition that the data inspection information represents that the abnormal data exists in the edge cluster monitoring data; inputting the edge cluster abnormal data into an abnormal data analysis model of the power grid edge cluster to obtain abnormal data analysis information; and generating cluster monitoring strategy information corresponding to the power grid edge clusters according to the abnormal data analysis information.
And carrying out abnormal data inspection on the edge cluster monitoring data, and carrying out reasoning analysis and state evaluation on the abnormal data if the abnormal data are found to be stored, so as to obtain abnormal data analysis information of the power grid edge cluster, and generating cluster monitoring strategy information. The cloud edge fusion intelligent scheduling operation platform can perform longitudinal disturbance discrimination and transverse comparison and investigation big data normalization monitoring on the power grid edge clusters, lock faults of the power grid edge clusters, achieve the state of 'checking when checking', and improve the abnormal condition discovery efficiency of the power grid edge clusters.
Drawings
FIG. 1 is an application environment diagram of a power grid edge cluster monitoring method based on a cloud edge fusion intelligent scheduling operation platform in one embodiment;
fig. 2 is a schematic flow diagram of a method for monitoring a power grid edge cluster based on a cloud edge fusion intelligent scheduling operation platform in an embodiment;
FIG. 3 is a flow chart of a method for obtaining data inspection information in one embodiment;
FIG. 4 is a flowchart illustrating a method for extracting edge cluster anomaly data in one embodiment;
FIG. 5 is a flow chart of a method for obtaining information of analysis of abnormal data in one embodiment;
FIG. 6 is a flowchart of a method for obtaining analysis data of a cluster continuous operation state in one embodiment;
FIG. 7 is a flowchart of a method for obtaining cluster monitoring policy information in one embodiment;
FIG. 8 is a flow chart of a method for generating device rejection policy information in one embodiment;
FIG. 9 is a schematic diagram of a grid edge cluster monitoring interface in one embodiment;
FIG. 10 is a block diagram of a power grid edge cluster monitoring device based on a cloud edge fusion intelligent scheduling operation platform in an embodiment;
FIG. 11 is an internal block diagram of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
The power grid edge cluster monitoring method based on the cloud edge fusion intelligent scheduling operation platform can be applied to an application environment shown in fig. 1. Wherein the grid edge cluster 102 communicates with the server 104 over a network. The data storage system may store data that the server 104 needs to process. The data storage system may be integrated on the server 104 or may be located on a cloud or other network server. The server 104 acquires edge cluster monitoring data corresponding to the power grid edge clusters according to the data monitoring rule data of the power grid edge clusters 102; performing abnormal data inspection on the edge cluster monitoring data to obtain data inspection information corresponding to the power grid edge clusters; extracting edge cluster abnormal data from the edge cluster monitoring data under the condition that the data inspection information represents that the abnormal data exists in the edge cluster monitoring data; inputting the edge cluster abnormal data into an abnormal data analysis model of the power grid edge cluster to obtain abnormal data analysis information; and generating cluster monitoring strategy information corresponding to the power grid edge clusters according to the abnormal data analysis information. The power grid edge cluster 102 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, internet of things devices and portable wearable devices, where the internet of things devices may be smart speakers, smart televisions, smart air conditioners, smart vehicle devices, and the like. The portable wearable device may be a smart watch, smart bracelet, headset, or the like. The server 104 may be implemented as a stand-alone server or as a server cluster of multiple servers.
In one embodiment, as shown in fig. 2, a method for monitoring a power grid edge cluster based on cloud edge fusion intelligent scheduling operation platform is provided, and the method is applied to a server in fig. 1 for illustration, and includes the following steps:
step 202, according to the data monitoring rule data of the power grid edge clusters, obtaining the edge cluster monitoring data corresponding to the power grid edge clusters.
Wherein the grid edge cluster may be a collection of computing nodes located at the edge of the computing architecture or near the grid data source. These computing nodes are typically located at distributed locations in the grid equipment, such as power stations, substations, transmission lines, etc., rather than being concentrated on a cloud-edge fusion intelligent dispatch operation platform.
The data monitoring rule data can be a rule that the cloud edge fusion intelligent scheduling operation platform monitors the power grid edge cluster, wherein the cloud edge fusion intelligent scheduling operation platform can be a resource scheduling platform with two-stage cooperative operation of 'cloud brain and edge node'.
The edge cluster monitoring data can be monitoring data acquired in the process of operating the grid edge cluster by the cloud edge fusion intelligent scheduling operation platform.
Specifically, the data monitoring rule data of the grid edge cluster is input to the server 104 through the terminal, where the data monitoring rule data is determined according to the requirement of "checking the grid edge cluster when checking the grid edge cluster, and thus, the data monitoring rule data may be dynamic monitoring rule data or static monitoring rule data. The dynamic monitoring rule data can be a monitoring rule which changes in real time along with the operation data acquired from the power grid edge cluster in real time, and the static monitoring rule data can be a monitoring rule determined according to the operation data of the power grid edge cluster in the last three months. On the premise that data monitoring rule data of the power grid edge clusters is used as constraint conditions, the server 104 acquires edge cluster monitoring data corresponding to the power grid edge clusters from the power grid edge clusters 102 through data interaction with the power grid edge clusters 102, stores the acquired edge cluster monitoring data in a storage unit, and when the server needs to process the edge cluster monitoring data, retrieves volatile storage resources from the storage unit for calculation by a central processing unit. The edge cluster monitoring data can be single data input to the central processing unit, or multiple data can be simultaneously input to the central processing unit.
And 204, performing abnormal data inspection on the edge cluster monitoring data to obtain data inspection information corresponding to the power grid edge cluster.
The data inspection information may be inspection data generated by performing abnormal data inspection on the edge cluster monitoring data, where the data inspection information includes inspection information of normal data, inspection information of abnormal data, inspection information of suspicious data, and the like in the edge cluster monitoring data.
Specifically, the edge cluster monitoring data are unfolded to obtain cluster base monitoring data, cluster product monitoring data, cluster application monitoring data and cluster resource monitoring data. The cluster base monitoring data can be detection data acquired in the running process of the base of the power grid edge cluster; the cluster product monitoring data can be detection data acquired in the running process of the power grid products clustered at the edge of the power grid; the cluster application monitoring data can be detection data acquired by application programs of the power grid edge clusters in the interaction process; the cluster resource monitoring data may be detection data of resource changes during operation of the grid edge cluster. First: under the condition that the cluster base monitoring data, the cluster product monitoring data, the cluster application monitoring data and the cluster resource monitoring data are all static, combining the data monitoring rule data and the product characteristic parameter information of the power grid edge cluster, and determining an abnormal data checking sequence aiming at the edge cluster monitoring data according to at least one of the cluster base monitoring data, the cluster product monitoring data, the cluster application monitoring data and the cluster resource monitoring data in a random arrangement and combination mode, wherein the abnormal data checking sequence is static (namely, the abnormal data checking sequence is not changed in a period of time). For example: combining the data monitoring rule data and the product characteristic parameter information of the power grid edge cluster, and generating an abnormal data checking sequence according to the modes of 1, the cluster base monitoring data, 2, the cluster product monitoring data, 3, the cluster application monitoring data, 4 and the cluster resource monitoring data; or: 1. and generating an abnormal data checking sequence in the mode of cluster product monitoring data, cluster resource monitoring data, cluster application monitoring data and cluster base monitoring data. Second,: and under the condition that at least one of the cluster base monitoring data, the cluster product monitoring data, the cluster application monitoring data and the cluster resource monitoring data is dynamic, combining the data monitoring rule data and the product characteristic parameter information of the power grid edge cluster, and determining an abnormal data checking sequence aiming at the edge cluster monitoring data according to the random permutation and combination of at least one of the cluster base monitoring data, the cluster product monitoring data, the cluster application monitoring data and the cluster resource monitoring data, wherein the abnormal data checking sequence is dynamic (namely, the abnormal data checking sequence changes along with the dynamic change of the monitoring data). The product characteristic parameter information may be a parameter capable of reflecting characteristic information of the power grid product, and may be classified into professional parameter information and general parameter information. The professional parameter information may be voltage, current, frequency, power factor, system inertia, capacity factor, etc., and the general parameter information may be a function brief introduction, version, change log, technical operation manual, etc.
Because the abnormal data checking sequence comprises checking sequence and monitoring data needing to be checked, according to the abnormal data checking sequence, the monitoring data needing to be checked is selected from the cluster base monitoring data, the cluster product monitoring data, the cluster application monitoring data and the cluster resource monitoring data, and the abnormal data checking is carried out according to the checking sequence, so that data checking information is obtained.
In step 206, in case that the data inspection information characterizes that the edge cluster monitoring data has abnormal data, the edge cluster abnormal data is extracted from the edge cluster monitoring data.
The edge cluster abnormal data may be data in which an abnormality exists in the edge cluster monitoring data.
Specifically, if the data inspection information characterizes that the edge cluster monitoring data has abnormal data, traversing the edge cluster monitoring data according to the data inspection information to obtain a cluster monitoring data traversing result, wherein the cluster monitoring data traversing result comprises a mapping relation between the data inspection information and the edge cluster monitoring data. And according to the traversing result of the cluster monitoring data, positioning at least one of the cluster base monitoring data, the cluster product monitoring data, the cluster application monitoring data and the cluster resource monitoring data in the edge cluster monitoring data as cluster abnormal monitoring data. Further, the abnormal data is separated from the cluster abnormal monitoring data, and the separated abnormal data is used as edge cluster abnormal data.
And step 208, inputting the abnormal data of the edge cluster into an abnormal data analysis model of the grid edge cluster to obtain abnormal data analysis information.
The abnormal data analysis model can be a neural network for carrying out reasoning analysis and state evaluation on the edge cluster abnormal data, and can also be a professional simulation model for carrying out reasoning analysis and state evaluation on the edge cluster abnormal data.
The abnormal data analysis information can be data obtained by reasoning analysis and state evaluation of the edge cluster abnormal data.
Specifically, according to the cluster anomaly data, the current running state of the power grid edge cluster is analyzed, wherein the running state analysis items comprise the items of performance data, errors, warnings, abnormal conditions, running conditions, stability, response time, safety conditions and the like of a power grid cluster base, a power grid product, an application program and a resource use condition at each node at the current time, and the cluster current running state analysis data is obtained.
Traversing the edge cluster monitoring data according to the current running state analysis data of the cluster to obtain a current running state analysis data traversing result, wherein the current running state analysis data traversing result comprises the mapping relation between the current running state analysis data of the cluster and the edge cluster monitoring data. And positioning abnormal characteristic information under the current condition from the edge cluster monitoring data according to the cluster monitoring data traversing result. And (3) carrying out characteristic extraction of the equipment information on the abnormal characteristic information by using a Transformer (converter), and extracting the equipment abnormal operation characteristic information in the power grid edge cluster from an extraction result by a self-attention mechanism. Finally, the current running state analysis data of the cluster and the abnormal running characteristic information of the equipment are input into a continuous running state prediction layer in an abnormal data analysis model, and one or more of the continuous running state prediction layer, namely a linear regression (Linear Regression), a Decision tree (Decision tree), a Random Forest (Random Forest), a Bayesian model, a support vector machine (Support Vector Machine, SVM), a pre-training language model and the like are used for predicting to obtain the continuous running state analysis data of the cluster.
Because the current running state analysis data of the cluster and the continuous running state analysis data of the cluster exist in a static mode and a dynamic mode, time is introduced as a parameter, and the current running state analysis data of the cluster and the continuous running state analysis data of the cluster are fused to obtain abnormal data analysis information.
And 210, generating cluster monitoring strategy information corresponding to the power grid edge clusters according to the abnormal data analysis information.
The cluster monitoring policy information can be a mode of monitoring the power grid edge clusters by the cloud edge fusion intelligent scheduling operation platform.
Specifically, according to analysis data of the current running state of the cluster, a cloud edge fusion intelligent scheduling running platform is used for adjusting a mode of monitoring the power grid edge cluster, so that current cluster monitoring strategy information is obtained; and adjusting the mode of monitoring the power grid edge cluster by using the cloud edge fusion intelligent scheduling operation platform according to the cluster continuous operation state analysis data to obtain continuous cluster monitoring strategy information. Because the current cluster monitoring policy information and the continuous cluster monitoring policy information have two modes of static state and dynamic state, time is introduced as a parameter, and the current cluster monitoring policy information and the continuous cluster monitoring policy information are fused to obtain the cluster monitoring policy information. Fig. 9 is a schematic diagram of a grid edge cluster monitoring interface in a cloud-edge fusion intelligent scheduling operation platform.
According to the power grid edge cluster monitoring method based on the cloud edge fusion intelligent scheduling operation platform, edge cluster monitoring data corresponding to the power grid edge clusters are obtained according to the data monitoring rule data of the power grid edge clusters; performing abnormal data inspection on the edge cluster monitoring data to obtain data inspection information corresponding to the power grid edge clusters; extracting edge cluster abnormal data from the edge cluster monitoring data under the condition that the data inspection information represents that the abnormal data exists in the edge cluster monitoring data; inputting the edge cluster abnormal data into an abnormal data analysis model of the power grid edge cluster to obtain abnormal data analysis information; and generating cluster monitoring strategy information corresponding to the power grid edge clusters according to the abnormal data analysis information.
And carrying out abnormal data inspection on the edge cluster monitoring data, and carrying out reasoning analysis and state evaluation on the abnormal data if the abnormal data are found to be stored, so as to obtain abnormal data analysis information of the power grid edge cluster, and generating cluster monitoring strategy information. The cloud edge fusion intelligent scheduling operation platform can perform longitudinal disturbance discrimination and transverse comparison and investigation big data normalization monitoring on the power grid edge clusters, lock faults of the power grid edge clusters, achieve the state of 'checking when checking', and improve the abnormal condition discovery efficiency of the power grid edge clusters.
In one embodiment, as shown in fig. 3, performing abnormal data inspection on edge cluster monitoring data to obtain data inspection information corresponding to an edge cluster of a power grid, including:
step 302, determining an abnormal data checking sequence according to the cluster base monitoring data, the cluster product monitoring data, the cluster application monitoring data and the cluster resource monitoring data.
The abnormal data checking sequence may be the checking sequence and the index of the monitoring data needed to check the abnormal data.
Specifically, the edge cluster monitoring data are unfolded to obtain cluster base monitoring data, cluster product monitoring data, cluster application monitoring data and cluster resource monitoring data. First: under the condition that the cluster base monitoring data, the cluster product monitoring data, the cluster application monitoring data and the cluster resource monitoring data are all static, combining the data monitoring rule data and the product characteristic parameter information of the power grid edge cluster, and determining an abnormal data checking sequence aiming at the edge cluster monitoring data according to at least one of the cluster base monitoring data, the cluster product monitoring data, the cluster application monitoring data and the cluster resource monitoring data in a random arrangement and combination mode, wherein the abnormal data checking sequence is static (namely, the abnormal data checking sequence is not changed in a period of time). Second,: and under the condition that at least one of the cluster base monitoring data, the cluster product monitoring data, the cluster application monitoring data and the cluster resource monitoring data is dynamic, combining the data monitoring rule data and the product characteristic parameter information of the power grid edge cluster, and determining an abnormal data checking sequence aiming at the edge cluster monitoring data according to the random permutation and combination of at least one of the cluster base monitoring data, the cluster product monitoring data, the cluster application monitoring data and the cluster resource monitoring data, wherein the abnormal data checking sequence is dynamic (namely, the abnormal data checking sequence changes along with the dynamic change of the monitoring data).
And step 304, performing abnormal data inspection on the cluster base monitoring data, the cluster product monitoring data, the cluster application monitoring data and the cluster resource monitoring data according to the abnormal data inspection sequence to obtain data inspection information.
Specifically, since the abnormal data inspection sequence includes an inspection order and a monitoring data index for performing abnormal data inspection, according to the abnormal data inspection sequence, monitoring data to be inspected is selected from the cluster base monitoring data, the cluster product monitoring data, the cluster application monitoring data and the cluster resource monitoring data, and the abnormal data inspection is performed according to the inspection order, so as to obtain data inspection information.
In this embodiment, by determining the abnormal data inspection sequence according to different monitoring data, each monitoring data is further inspected by using the abnormal data inspection sequence, so that different inspection modes can be implemented for the monitoring data at different times, and the efficiency and accuracy of data inspection are improved.
In one embodiment, as shown in fig. 4, extracting edge cluster anomaly data from edge cluster monitoring data includes:
step 402, locating cluster abnormal monitoring data according to the edge cluster monitoring data and the data checking information.
The cluster anomaly monitoring data may be at least one of cluster base monitoring data, cluster product monitoring data, cluster application monitoring data, and cluster resource monitoring data.
Specifically, if the data inspection information characterizes that the edge cluster monitoring data has abnormal data, traversing the edge cluster monitoring data according to the data inspection information to obtain a cluster monitoring data traversing result, wherein the cluster monitoring data traversing result comprises a mapping relation between the data inspection information and the edge cluster monitoring data. And according to the traversing result of the cluster monitoring data, positioning at least one of the cluster base monitoring data, the cluster product monitoring data, the cluster application monitoring data and the cluster resource monitoring data in the edge cluster monitoring data as cluster abnormal monitoring data.
And step 404, traversing the cluster anomaly monitoring data and extracting edge cluster anomaly data.
Specifically, the abnormal data is separated from the cluster abnormal monitoring data, and the separated abnormal data is used as edge cluster abnormal data.
In this embodiment, by locating cluster anomaly monitoring data with anomaly data using the edge cluster monitoring data and the data inspection information and extracting the edge cluster anomaly data from the cluster anomaly monitoring data, only the monitoring data with anomaly data can be subjected to deep search, and the calculation amount of the cloud edge fusion intelligent scheduling operation platform is reduced.
In one embodiment, as shown in fig. 5, the inputting the abnormal data of the edge cluster into the abnormal data analysis model of the grid edge cluster, to obtain abnormal data analysis information, includes:
step 502, analyzing the current running state of the power grid edge cluster according to the edge cluster abnormal data to obtain cluster current running state analysis data.
The current running state analysis data of the cluster may be data obtained by analyzing the current running state of the power grid edge cluster.
Specifically, according to the cluster anomaly data, the current running state of the power grid edge cluster is analyzed, wherein the running state analysis items comprise the items of performance data, errors, warnings, abnormal conditions, running conditions, stability, response time, safety conditions and the like of a power grid cluster base, a power grid product, an application program and a resource use condition at each node at the current time, and the cluster current running state analysis data is obtained.
And step 504, analyzing the continuous operation state of the power grid edge cluster according to the current operation state analysis data of the cluster to obtain the cluster continuous operation state analysis data.
The cluster continuous operation state analysis data may be data obtained by analyzing a continuous operation state of a power grid edge cluster.
Specifically, traversing edge cluster monitoring data according to current running state analysis data of the clusters to obtain current running state analysis data traversing results, wherein the current running state analysis data traversing results comprise mapping relations between the current running state analysis data of the clusters and the edge cluster monitoring data. And positioning abnormal characteristic information under the current condition from the edge cluster monitoring data according to the cluster monitoring data traversing result. And (3) carrying out characteristic extraction of the equipment information on the abnormal characteristic information by using a Transformer (converter), and extracting the equipment abnormal operation characteristic information in the power grid edge cluster from an extraction result by a self-attention mechanism. Finally, the current running state analysis data of the cluster and the abnormal running characteristic information of the equipment are input into a continuous running state prediction layer in an abnormal data analysis model, and one or more of the continuous running state prediction layer, namely a linear regression (Linear Regression), a Decision tree (Decision tree), a Random Forest (Random Forest), a Bayesian model, a support vector machine (Support Vector Machine, SVM), a pre-training language model and the like are used for predicting to obtain the continuous running state analysis data of the cluster.
Step 506, obtaining abnormal data analysis information according to the current running state analysis data of the cluster and the continuous running state analysis data of the cluster.
Specifically, because the current running state analysis data of the cluster and the continuous running state analysis data of the cluster exist in two ways of static state and dynamic state, time is introduced as a parameter, and the current running state analysis data of the cluster and the continuous running state analysis data of the cluster are fused to obtain abnormal data analysis information.
In the embodiment, the current running state and the continuous running state of the power grid edge cluster are analyzed, and abnormal data analysis information is generated according to the analysis results of the current running state and the continuous running state of the power grid edge cluster, so that the current running condition of the power grid edge cluster can be focused, the continuous running condition of the power grid edge cluster is predicted, the possible occurrence condition of the power grid edge cluster is prepared in advance, and the safety of the power grid edge cluster is improved.
In one embodiment, as shown in fig. 6, according to analysis data of current operation states of clusters, continuous operation states of the power grid edge clusters are analyzed to obtain analysis data of continuous operation states of clusters, including:
step 602, determining abnormal characteristic information of the power grid edge cluster according to the current running state analysis data of the cluster.
The abnormal characteristic information may be data information which can represent abnormal characteristics in the analysis data of the current running state of the cluster.
Specifically, traversing edge cluster monitoring data according to current running state analysis data of the clusters to obtain current running state analysis data traversing results, wherein the current running state analysis data traversing results comprise mapping relations between the current running state analysis data of the clusters and the edge cluster monitoring data. And positioning abnormal characteristic information under the current condition from the edge cluster monitoring data according to the cluster monitoring data traversing result.
Step 604, determining the abnormal operation characteristic information of the equipment in the power grid edge cluster according to the abnormal characteristic information.
The abnormal operation feature information of the device may be text feature information of the device representing abnormal operation of the grid edge cluster in the abnormal feature information, for example: model, component, etc. of the device that is operating abnormally.
Specifically, a Transformer (Transformer) is used for extracting the characteristics of the equipment information from the abnormal characteristic information, and the equipment abnormal operation characteristic information in the power grid edge cluster is extracted from the extraction result through a self-attention mechanism.
Step 606, obtaining the analysis data of the cluster continuous operation state according to the analysis data of the current operation state of the cluster and the abnormal operation characteristic information of the equipment.
Specifically, the current running state analysis data of the cluster and the abnormal running characteristic information of the equipment are input into a continuous running state prediction layer in an abnormal data analysis model, and one or more of the continuous running state prediction layer is predicted through linear regression (Linear Regression), decision Trees (Decision Trees), random forests (Random Forest), bayesian models, support vector machines (Support Vector Machine, SVM), pre-training language models and the like, so that the cluster continuous running state analysis data are obtained.
In the embodiment, the text information of the current running state analysis data of the cluster is extracted to obtain the abnormal running characteristic information of the equipment in the power grid edge cluster, and the current running state analysis data is further combined to obtain the continuous running state analysis data of the cluster, so that the abnormal condition and the current running state of the equipment can be combined, the condition possibly encountered by the continuous running of the power grid edge cluster can be predicted, and the emergency capability of the cloud edge fusion intelligent scheduling running platform is improved.
In one embodiment, as shown in fig. 7, generating cluster monitoring policy information corresponding to a grid edge cluster according to the abnormal data analysis information includes:
Step 702, generating current cluster monitoring strategy information of the power grid edge cluster according to the current running state analysis data of the cluster.
The current cluster monitoring policy information may be a monitoring method in a short time with the current as a starting point under the condition that an abnormality occurs in a cluster at the edge of the power grid.
Specifically, according to the cluster continuous operation state analysis data, a mode that the cloud edge fusion intelligent scheduling operation platform monitors the power grid edge clusters is adjusted, and current cluster monitoring strategy information is obtained.
And step 704, generating continuous cluster monitoring strategy information of the power grid edge clusters according to the cluster continuous running state analysis data.
The continuous cluster monitoring policy information may be a monitoring method with the current starting point for a long time under the condition that an abnormality occurs in the grid edge cluster.
Specifically, according to the cluster continuous operation state analysis data, a mode that the cloud edge fusion intelligent scheduling operation platform monitors the power grid edge clusters is adjusted, and continuous cluster monitoring strategy information is obtained.
And step 706, optimizing the current cluster monitoring strategy information and the continuous cluster monitoring strategy information according to the edge cluster abnormal data to obtain the cluster monitoring strategy information.
Specifically, because the current cluster monitoring policy information and the continuous cluster monitoring policy information have two modes of static and dynamic, time is introduced as a parameter, and the current cluster monitoring policy information and the continuous cluster monitoring policy information are fused to obtain the cluster monitoring policy information. If the data inspection information characterizes that the edge cluster monitoring data does not have abnormal data, the power grid edge cluster is continuously monitored according to the original monitoring method.
In the embodiment, by adopting new current cluster monitoring policy information for the current running state and adopting continuous cluster monitoring policy information for the continuous running state, the running information of the power grid edge clusters can be monitored on different scales, the cloud edge fusion intelligent scheduling running platform can find abnormal conditions in time, and the safety of a power grid system is ensured.
In one embodiment, as shown in fig. 8, the method further comprises:
step 802, performing abnormal data inspection on the operation monitoring data of the power grid equipment to obtain equipment inspection information corresponding to the power grid equipment.
The power grid equipment operation monitoring data can be detection data acquired in the operation process of the power grid equipment.
The device inspection information may be inspection data generated by performing abnormal data inspection on the power grid device operation monitoring data, where the device inspection information includes inspection information of normal data, inspection information of abnormal data, inspection information of suspicious data, and the like in the power grid device operation monitoring data, and is used for reflecting the operation condition of the power grid device.
Specifically, the operation data information of each power grid device in the power grid device operation monitoring data is traversed, and the operation data information in the power grid device operation monitoring data is compared with the normal operation data threshold value in the power grid device aiming at each power grid device to obtain device inspection information.
And step 804, analyzing the operation monitoring data of the power grid equipment under the condition that the equipment inspection information represents that the power grid equipment has abnormality, and generating equipment abnormality analysis information corresponding to the power grid equipment.
The equipment abnormality analysis information can be data obtained by reasoning analysis and state evaluation of power grid equipment operation monitoring data.
Specifically, if the equipment inspection information characterizes that the power grid equipment has an abnormality, that is, the operation data information in the operation monitoring data of the power grid equipment does not fall into the range of the normal operation data threshold value in the power grid equipment, the operation data information in the operation monitoring data of the power grid equipment, which does not fall into the corresponding normal operation data threshold value in the power grid equipment, is extracted, and the operation abnormality data of the power grid equipment is obtained. And analyzing the abnormal condition of the power grid equipment according to the abnormal operation data of the power grid equipment to obtain equipment abnormality analysis information.
Step 806, generating device rejection policy information according to the device exception analysis information.
The device rejection policy information may be a manner of eliminating an anomaly of the power grid device by the cloud-edge fusion intelligent scheduling operation platform.
Specifically, the equipment abnormality analysis information is input to a cloud-edge fusion intelligent scheduling operation platform, a solution for the equipment abnormality analysis information is found through the cloud-edge fusion intelligent scheduling operation platform, and equipment rejection strategy information is generated. The solution to the equipment abnormality analysis information is stored in a storage space of the cloud edge fusion intelligent scheduling operation platform in advance, and is selected from all solutions according to the principle that analysis data are matched with solution data.
In this embodiment, by using the power grid equipment operation monitoring data and using the power grid edge cluster as an intermediate medium, the monitoring of the cloud edge fusion intelligent scheduling operation platform on the power grid equipment is achieved, and the integrated monitoring can be realized under the overall arrangement of the cloud edge fusion intelligent scheduling operation platform, so that the solution can be timely proposed under the condition that the power grid system has a problem.
It should be understood that, although the steps in the flowcharts related to the above embodiments are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides a power grid edge cluster monitoring device based on the cloud edge fusion intelligent scheduling operation platform, which is used for realizing the power grid edge cluster monitoring method based on the cloud edge fusion intelligent scheduling operation platform. The implementation scheme of the device for solving the problems is similar to the implementation scheme recorded in the method, so the specific limitation in the embodiment of the power grid edge cluster monitoring device based on the cloud edge fusion intelligent scheduling operation platform can be seen from the above description, and the description is omitted here.
In one embodiment, as shown in fig. 10, a power grid edge cluster monitoring device based on cloud edge fusion intelligent scheduling operation platform is provided, including: a monitoring data acquisition module 1002, an abnormal data inspection module 1004, an abnormal data extraction module 1006, an abnormal data analysis module 1008, and a monitoring policy generation module 1010, wherein:
the monitoring data obtaining module 1002 is configured to obtain edge cluster monitoring data corresponding to a power grid edge cluster according to data monitoring rule data of the power grid edge cluster;
The abnormal data checking module 1004 is configured to perform abnormal data checking on the edge cluster monitoring data to obtain data checking information corresponding to the grid edge cluster;
an abnormal data extraction module 1006, configured to extract edge cluster abnormal data from the edge cluster monitoring data when the data inspection information characterizes that the edge cluster monitoring data has abnormal data;
the abnormal data analysis module 1008 is used for inputting the edge cluster abnormal data into an abnormal data analysis model of the grid edge cluster to obtain abnormal data analysis information;
and the monitoring policy generating module 1010 is configured to generate cluster monitoring policy information corresponding to the grid edge clusters according to the abnormal data analysis information.
In one embodiment, the abnormal data checking module 1004 is further configured to determine an abnormal data checking sequence according to the cluster base monitoring data, the cluster product monitoring data, the cluster application monitoring data, and the cluster resource monitoring data; and according to the abnormal data checking sequence, checking the abnormal data of the cluster base monitoring data, the cluster product monitoring data, the cluster application monitoring data and the cluster resource monitoring data to obtain data checking information.
In one embodiment, the abnormal data extraction module 1006 is further configured to locate cluster abnormal monitoring data according to the edge cluster monitoring data and the data inspection information; the cluster anomaly monitoring data is at least one of cluster base monitoring data, cluster product monitoring data, cluster application monitoring data and cluster resource monitoring data; and traversing the cluster abnormity monitoring data to extract edge cluster abnormity data.
In one embodiment, the abnormal data analysis module 1008 is further configured to analyze a current operation state of the grid edge cluster according to the edge cluster abnormal data, to obtain cluster current operation state analysis data; analyzing the continuous operation state of the power grid edge cluster according to the current operation state analysis data of the cluster to obtain cluster continuous operation state analysis data; and obtaining abnormal data analysis information according to the current running state analysis data of the cluster and the continuous running state analysis data of the cluster.
In one embodiment, the abnormal data analysis module 1008 is further configured to determine abnormal feature information of the grid edge cluster according to the current running state analysis data of the cluster; according to the abnormal characteristic information, determining equipment abnormal operation characteristic information in the power grid edge cluster; and obtaining the analysis data of the continuous running state of the cluster according to the analysis data of the current running state of the cluster and the abnormal running characteristic information of the equipment.
In one embodiment, the abnormal data analysis module 1008 is further configured to analyze data according to a current running state of the cluster, and generate current cluster monitoring policy information of the grid edge cluster; according to the cluster continuous running state analysis data, continuous cluster monitoring strategy information of the power grid edge clusters is generated; and optimizing the current cluster monitoring strategy information and the continuous cluster monitoring strategy information according to the edge cluster abnormal data to obtain the cluster monitoring strategy information.
In one embodiment, the monitoring policy generating module 1010 is further configured to perform abnormal data inspection on the operation monitoring data of the power grid device to obtain device inspection information corresponding to the power grid device; under the condition that the equipment inspection information represents that the power grid equipment is abnormal, analyzing the operation monitoring data of the power grid equipment to generate equipment abnormality analysis information corresponding to the power grid equipment; generating equipment rejection strategy information according to the equipment anomaly analysis information, and sending the equipment rejection strategy information to a power grid edge cluster; the power grid edge cluster is used for sending equipment rejection policy information to the power grid maintenance unit.
All or part of each module in the power grid edge cluster monitoring device based on the cloud edge fusion intelligent scheduling operation platform can be realized through software, hardware and a combination of the software and the hardware. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a server, and the internal structure of which may be as shown in fig. 11. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is for storing server data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to realize a power grid edge cluster monitoring method based on cloud edge fusion intelligent scheduling operation platform.
It will be appreciated by those skilled in the art that the structure shown in fig. 11 is merely a block diagram of a portion of the structure associated with the present application and is not limiting of the computer device to which the present application applies, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In an embodiment, there is also provided a computer device comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the method embodiments described above when the computer program is executed.
In one embodiment, a computer-readable storage medium is provided, storing a computer program which, when executed by a processor, implements the steps of the method embodiments described above.
In one embodiment, a computer program product or computer program is provided that includes computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the steps in the above-described method embodiments.
It should be noted that, user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, presented data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the various embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the various embodiments provided herein may include at least one of relational databases and non-relational databases. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic units, quantum computing-based data processing logic units, etc., without being limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application shall be subject to the appended claims.
Claims (10)
1. A power grid edge cluster monitoring method based on cloud edge fusion intelligent scheduling operation platform is characterized by comprising the following steps:
acquiring edge cluster monitoring data corresponding to a power grid edge cluster according to data monitoring rule data of the power grid edge cluster;
performing abnormal data inspection on the edge cluster monitoring data to obtain data inspection information corresponding to the power grid edge clusters;
Extracting edge cluster abnormal data from the edge cluster monitoring data under the condition that the data checking information characterizes that the abnormal data exists in the edge cluster monitoring data;
inputting the edge cluster abnormal data into an abnormal data analysis model of the power grid edge cluster to obtain abnormal data analysis information;
and generating cluster monitoring strategy information corresponding to the power grid edge clusters according to the abnormal data analysis information.
2. The method of claim 1, wherein the edge cluster monitoring data comprises cluster base monitoring data, cluster artifact monitoring data, cluster application monitoring data, and cluster resource monitoring data; the abnormal data inspection is performed on the edge cluster monitoring data to obtain data inspection information corresponding to the grid edge cluster, and the abnormal data inspection comprises the following steps:
determining an abnormal data checking sequence according to the cluster base monitoring data, the cluster product monitoring data, the cluster application monitoring data and the cluster resource monitoring data;
and according to the abnormal data checking sequence, checking the abnormal data of the cluster base monitoring data, the cluster product monitoring data, the cluster application monitoring data and the cluster resource monitoring data to obtain the data checking information.
3. The method of claim 2, wherein the extracting edge cluster anomaly data from the edge cluster monitor data comprises:
locating cluster abnormal monitoring data according to the edge cluster monitoring data and the data checking information; the cluster anomaly monitoring data is at least one of the cluster base monitoring data, the cluster product monitoring data, the cluster application monitoring data and the cluster resource monitoring data;
traversing the cluster abnormity monitoring data and extracting the edge cluster abnormity data.
4. The method according to claim 1, wherein the inputting the edge cluster anomaly data into the anomaly data analysis model of the grid edge cluster to obtain anomaly data analysis information comprises:
analyzing the current running state of the power grid edge cluster according to the edge cluster abnormal data to obtain cluster current running state analysis data;
analyzing the continuous running state of the power grid edge cluster according to the current running state analysis data of the cluster to obtain cluster continuous running state analysis data;
And obtaining the abnormal data analysis information according to the current running state analysis data of the cluster and the continuous running state analysis data of the cluster.
5. The method according to claim 4, wherein analyzing the continuous operation state of the grid edge cluster according to the analysis data of the current operation state of the cluster to obtain analysis data of the continuous operation state of the cluster comprises:
according to the current running state analysis data of the clusters, determining abnormal characteristic information of the power grid edge clusters;
according to the abnormal characteristic information, determining equipment abnormal operation characteristic information in the power grid edge cluster;
and obtaining cluster continuous operation state analysis data according to the cluster current operation state analysis data and the equipment abnormal operation characteristic information.
6. The method of claim 4, wherein generating cluster monitoring policy information corresponding to the grid edge clusters according to the anomaly data analysis information comprises:
according to the current running state analysis data of the clusters, current cluster monitoring strategy information of the power grid edge clusters is generated;
generating continuous cluster monitoring strategy information of the power grid edge clusters according to the cluster continuous running state analysis data;
And optimizing the current cluster monitoring strategy information and the continuous cluster monitoring strategy information according to the edge cluster abnormal data to obtain the cluster monitoring strategy information.
7. The method of claim 1, wherein the edge cluster monitoring data further comprises grid device operation monitoring data, the method further comprising:
abnormal data inspection is carried out on the operation monitoring data of the power grid equipment to obtain equipment inspection information corresponding to the power grid equipment;
analyzing the operation monitoring data of the power grid equipment under the condition that the equipment inspection information represents that the power grid equipment is abnormal, and generating equipment abnormality analysis information corresponding to the power grid equipment;
generating equipment rejection strategy information according to the equipment anomaly analysis information, and sending the equipment rejection strategy information to the power grid edge cluster; the power grid edge cluster is used for sending the equipment rejection strategy information to a power grid maintenance unit.
8. Electric wire netting edge cluster monitoring device based on cloud limit fuses intelligent scheduling operation platform, its characterized in that, the device includes:
the monitoring data acquisition module is used for acquiring edge cluster monitoring data corresponding to the power grid edge clusters according to data monitoring rule data of the power grid edge clusters;
The abnormal data checking module is used for checking the abnormal data of the edge cluster monitoring data to obtain data checking information corresponding to the power grid edge cluster;
the abnormal data extraction module is used for extracting edge cluster abnormal data from the edge cluster monitoring data under the condition that the data inspection information characterizes that the edge cluster monitoring data exist abnormal data;
the abnormal data analysis module is used for inputting the edge cluster abnormal data into an abnormal data analysis model of the power grid edge cluster to obtain abnormal data analysis information;
and the monitoring strategy generation module is used for generating cluster monitoring strategy information corresponding to the power grid edge clusters according to the abnormal data analysis information.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
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