A cloud-native power production management system

Abstract

The application provides a cloud-native-based power production management system, comprising: an application deployment module, which is used for packaging a power production management application based on cloud native, and deploying the power production management application in a standardized container based on a packaging result; a management module, which is used for analyzing real-time power production data collected based on a deployment result, and scheduling power production based on an analysis result; and a monitoring module, which is used for monitoring a whole life cycle of power production based on a scheduling result, and recording and managing whole life cycle monitoring results. The system running environment deployment is complex, and the maintenance downtime is long. In addition, the power production efficiency is improved. Finally, the whole life cycle of power production in the scheduling process is monitored, and the whole life cycle monitoring results are recorded and managed, so that the scheduling situation can be effectively understood, and the power production management effect is greatly improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to a cloud-native-based power production management system. BACKGROUND

[0002] At present, with the continuous development of the power industry and the promotion of digital transformation, power production management is facing increasingly complex demands and challenges. In the current technical environment, cloud-native technology emerges as the times require and shows great advantages. Cloud-native architecture can provide high flexibility, scalability and reliability, enabling power production management systems to better adapt to dynamic business demands.

[0003] The power production process involves many links, such as power generation, power transmission, power transformation, power distribution, etc. Each link generates a large amount of data. Traditional power production management systems gradually show limitations in dealing with large-scale data processing, high-concurrency access, rapid deployment and elastic expansion, etc.

[0004] In addition, the safe and stable operation of the power system is of great importance, and real-time monitoring and early warning systems are needed to timely detect potential problems and take countermeasures.

[0005] Therefore, in order to overcome the above-mentioned defects, the present application provides a cloud-native-based power production management system. SUMMARY

[0006] The present application provides a cloud-native-based power production management system, which packages power production management applications using cloud-native technology and deploys them in standardized containers, thereby facilitating the management of power production, solving the problem of complex system runtime environment deployment and long maintenance downtime. In addition, real-time power production data collected according to the deployment results are analyzed, and accurate and effective scheduling of power production is realized according to the analysis results, improving the efficiency of power production. Finally, the power production in the scheduling process is monitored throughout its life cycle, and the results of the life cycle monitoring are recorded and managed, facilitating timely response when there are abnormalities in the scheduling process, and facilitating effective understanding of the scheduling situation, greatly improving the effectiveness of power production management.

[0007] The present application provides a cloud-native-based power production management system, which includes:

[0008] An application deployment module for packaging power production management applications based on cloud-native technology and deploying them in standardized containers based on the packaging results;

[0009] A management module for analyzing real-time power production data collected based on the deployment results, and scheduling power production based on the analysis results;

[0010] A monitoring module is configured to monitor the entire life cycle of power generation based on the scheduling result and record and manage the monitoring result of the entire life cycle.

[0011] Preferably, the cloud-native-based power generation management system comprises an application deployment module, which includes:

[0012] An application acquisition unit is configured to acquire the power generation management application, extract attribute parameters of the power generation management application based on the background terminal, and acquire container parameters of the standardized container.

[0013] An application packaging unit is configured to determine a packaging strategy required by the power generation management application based on the attribute parameters and the container parameters, and package the power generation management application into an application image file based on the packaging strategy.

[0014] An uploading unit is configured to upload the application image file to the standardized container.

[0015] Preferably, the cloud-native-based power generation management system comprises an application deployment module, which includes:

[0016] An application processing unit is configured to analyze the packaging result, extract application structure features of the power generation management application, and determine a split node of the power generation management application based on the application structure features and a business execution flow of the power generation management application.

[0017] An application splitting unit is configured to split the power generation management application into N microservices based on the split node, and allocate deployment resources to each microservice based on the splitting result.

[0018] An application deployment unit is configured to distribute the N microservices based on the deployment resources, reserve an extension and an update interface for each microservice based on the distributed deployment result, and complete the deployment of the power generation management application in the standardized container based on the reservation result.

[0019] Preferably, the cloud-native-based power generation management system comprises an application deployment unit, which includes:

[0020] A result acquisition subunit is configured to acquire the deployment result, and start the power generation management application deployed in the standardized container based on the deployment result.

[0021] A state monitoring subunit is configured to acquire running state parameters of the power generation management application in real time based on the starting result, analyze the running state parameters based on preset state evaluation indexes, and obtain a state evaluation result.

[0022] An optimization subunit is configured to determine an optimization direction and an optimization parameter for the deployment result based on the operation state parameters when the state evaluation result does not meet the preset requirement, and to optimize and adjust the deployment result based on the optimization direction and the optimization parameter.

[0023] Preferably, the cloud-native-based power production management system comprises an application deployment unit, which comprises:

[0024] A link opening subunit is configured to obtain a deployment result of the power production management application in the standardized container, and to open a real-time interaction link with the management terminal based on the deployment result.

[0025] An application updating subunit is configured to:

[0026] receive an application updating request and an application updating target issued by the management terminal based on the real-time interaction link, and analyze the application updating target to obtain a microservice to be updated.

[0027] temporarily split and separate the microservice to be updated in the deployment result in the standardized container, and process data cached by the microservice to be updated based on the temporary split and separation result.

[0028] Meanwhile, the microservice to be updated is updated based on the temporary split and separation, and the microservice to be updated is deployed and reset in the standardized container based on the updating result.

[0029] The cached upper-level processing data is processed based on the deployment and reset result.

[0030] Preferably, the cloud-native-based power production management system comprises a management module, which comprises:

[0031] A data collection preparation unit is configured to obtain a spatial structure distribution map of a power production scene, and extract distribution features of each power equipment in the power production scene.

[0032] A parameter mapping unit is configured to extract business parameters of each power equipment, and map and label the business parameters of each power equipment in the spatial structure distribution map of the power production scene based on the distribution features.

[0033] A position determination unit is configured to obtain a business layout map of the power production scene based on the mapping and labeling result, and determine a data collection dimension based on a preset power production management requirement.

[0034] A data collection unit is configured to:

[0035] match the data collection dimension with the business parameters of each power equipment, determine a key data collection point based on the matching result, and guide installation of a target sensor based on the key data collection point.

[0036] Real-time monitoring of each power equipment based on the installed target sensor obtains corresponding real-time power production data.

[0037] Preferably, a cloud-native-based power production management system, the management module comprises:

[0038] The data preprocessing unit is configured to:

[0039] The collected real-time power production data is clustered, and a multi-dimensional power data set corresponding to the real-time power production data is obtained based on the clustering.

[0040] Each dimension of the power data set is traversed, and based on the traversal result, the existing abnormal data is determined, and the sample representation of each dimension of the power data set is extracted, and based on the sample representation, the target strategy is called from the preset cleaning strategy library to clean the abnormal data, and a multi-dimensional standard power data set is obtained.

[0041] The data analysis unit is configured to:

[0042] Based on the power production operation protocol, the dependent limiting relationship between the multi-dimensional standard power data sets is determined, and each dimension of the standard power data set is assigned a corresponding weight value based on the dependent limiting relationship.

[0043] Each dimension of the standard power data set and the corresponding weight value are input into the power demand prediction model, and each dimension of the standard power data set is analyzed based on the input result to obtain the power label of the current time period.

[0044] Based on the power label, the demand floating feature of the current time period of the power grid is determined, and the demand floating feature is corrected based on the weight value corresponding to each dimension of the standard power data set to obtain the demand trend of the next stage of the power grid, and the power load demand is obtained based on the demand trend.

[0045] The power production scheduling unit is configured to:

[0046] The performance parameters of each device in the power grid are analyzed to obtain the performance range of each device, and the power grid is traversed to obtain the topology structure and the power flow distribution of the power grid, and the line transmission capacity of the power grid is obtained based on the topology structure and the power flow distribution.

[0047] Based on the power load demand, the performance range of each device, and the line transmission capacity of the power grid, a preliminary scheduling scheme of power production is obtained, and the preliminary scheduling scheme is simulated and run, and based on the simulation result, the preliminary scheduling scheme is adjusted for vulnerabilities to obtain a final scheduling scheme.

[0048] Based on the final scheduling scheme, the power production is scheduled and managed.

[0049] Preferably, a cloud-native-based power production management system, a power production scheduling unit, comprises:

[0050] A scheme obtaining subunit is configured to obtain a preliminary scheduling scheme and build a virtual power production scene in a computer based on the structural relationship between the power grid and the devices;

[0051] A simulation subunit is configured to:

[0052] add the preliminary scheduling scheme in the background of the virtual power production scene based on the computer, and perform simulated scheduling on the power production of the virtual power production scene based on the preliminary scheduling scheme according to the addition result;

[0053] determine the power production state of each production node based on the simulated scheduling, and determine potential hidden trouble factors and corresponding hidden trouble representations existing in the preliminary scheduling scheme based on the power production state;

[0054] A scheme adjustment subunit is configured to adjust the preliminary scheduling scheme based on the potential hidden trouble factors and the hidden trouble representations to obtain a final scheduling scheme.

[0055] Preferably, a cloud-native-based power production management system, a monitoring module, comprises:

[0056] A monitoring unit is configured to:

[0057] obtain a monitoring requirement for power production, and determine a monitoring dimension and a monitoring node based on the monitoring requirement;

[0058] configure a monitoring mechanism based on the monitoring dimension and the monitoring node, and perform whole-life-cycle monitoring on the scheduling process of the power production based on the monitoring mechanism;

[0059] A classification unit is configured to obtain multi-dimensional scheduling parameters of the power production and state parameters of the devices based on the whole-life-cycle monitoring result, and map and associate the state parameters of the devices and the multi-dimensional scheduling parameters;

[0060] A recording unit is configured to:

[0061] construct a parameter record table, and extract a timestamp corresponding to the whole-life-cycle monitoring result;

[0062] record and manage the state parameters of the devices and the multi-dimensional scheduling parameters in the parameter record table based on the timestamp and the mapping and association result.

[0063] Preferably, a cloud-native-based power production management system, a recording unit, comprises:

[0064] A parameter analysis subunit is configured to extract state parameters of the devices in the power production scheduling process, and determine the state of the devices based on the state parameters;

[0065] Safety monitoring subunit, used for:

[0066] Visualize the status of each device in a visual coordinate system based on the time series, and obtain the status change amplitude of each device based on the visual display;

[0067] Identify risky devices based on the state change amplitude and the benchmark amplitude interval, and reallocate monitoring resources of the monitoring mechanism based on the risky devices;

[0068] Based on the reallocation results, risk equipment is monitored in key areas, and early warning management is carried out when a fault occurs.

[0069] Compared with the prior art, the present invention has the following beneficial effects:

[0070] By adopting cloud-native technology to package power production management applications and deploy them in standardized containers, it facilitates the management and operation of power production, and also solves the problems of complex deployment of the system operating environment and long maintenance downtime. Secondly, the collected real-time power production data is analyzed according to the deployment results, and power production is accurately and effectively dispatched according to the analysis results, thereby improving the efficiency of power production. Finally, the power production in the dispatching process is monitored throughout the entire life cycle, and the monitoring results of the entire life cycle are recorded and managed, which facilitates timely response when there are abnormalities in the dispatching process, and also facilitates effective understanding of the dispatching situation, greatly improving the power production management effect.

[0071] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be achieved and obtained through the structures specifically pointed out in this application document.

[0072] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0074] Figure 1 This is a structural diagram of a cloud-native-based power production management system in an embodiment of the present invention;

[0075] Figure 2 This is a structural diagram of an application deployment module in a cloud-native-based power production management system in an embodiment of the present invention;

[0076] Figure 3A structural diagram of a management module in a cloud-native-based power production management system in an embodiment of the present application. DETAILED DESCRIPTION

[0077] The preferred embodiments of the present application are described below in conjunction with the accompanying drawings, in which it is understood that the preferred embodiments described below are merely used to illustrate and explain the present application, and are not used to limit the present application.

[0078] Embodiment 1

[0079] The present embodiment provides a cloud-native-based power production management system, as shown in the accompanying drawings, which comprises: Figure 1 An application deployment module is configured to package the power production management application based on cloud-native, and deploy the power production management application in a standardized container based on the packaging result.

[0080] A management module is configured to analyze the collected real-time power production data based on the deployment result, and schedule the power production based on the analysis result.

[0081] A monitoring module is configured to monitor the whole life cycle of the power production based on the scheduling result, and record and manage the whole life cycle monitoring result.

[0082] In the present embodiment, cloud-native is a method and concept of constructing and running an application program, which emphasizes the use of the characteristics and advantages of cloud computing to design the application program to be suitable for efficient operation and management in a cloud environment.

[0083] In the present embodiment, the power production management application is known in advance, which is used to analyze and schedule the power production data.

[0084] In the present embodiment, the standardized container is set in advance, which is a space for carrying the power production management application, so as to facilitate effective management and operation and maintenance of the power production management application.

[0085] In the present embodiment, the real-time power production data can be power generation, power load, and power consumption data.

[0086] In the present embodiment, the whole life cycle monitoring refers to monitoring the power production scheduling of each device in the power grid and the running state of each device, so as to facilitate effective understanding of the scheduling of the power production and the running state of the device.

[0087]

[0088] ​The working principle and beneficial effects of the above technical solution are: by adopting cloud native technology to package the power production management application and deploy it in a standardized container, the management operation of power production is facilitated, and the problems of complex system running environment deployment and long maintenance downtime are solved. Secondly, according to the deployment result, the collected real-time power production data is analyzed, the accurate and effective scheduling of power production is realized according to the analysis result, the efficiency of power production is improved, and finally, the whole life cycle monitoring of power production in the scheduling process is carried out, and the whole life cycle monitoring result is recorded and managed, which facilitates timely response when there is an exception in the scheduling process, and also facilitates effective understanding of the scheduling situation, greatly improving the effect of power production management.

[0089] Embodiment 2

[0090] Based on the embodiment 1, the embodiment provides a power production management system based on cloud native, an application deployment module, comprising:

[0091] An application acquisition unit is configured to acquire a power production management application, and extract attribute parameters of the power production management application based on a background terminal, and simultaneously acquire container parameters of a standardized container;

[0092] An application packaging unit is configured to determine a packaging strategy required by the power production management application based on the attribute parameters and the container parameters, and package the power production management application into an application image file based on the packaging strategy;

[0093] An uploading unit is configured to upload the application image file to the standardized container.

[0094] In this embodiment, the attribute parameters refer to the type of the power production management application and the requirements such as the running conditions required to meet during running.

[0095] In this embodiment, the container parameters refer to the space size and structure distribution of the standardized container.

[0096] In this embodiment, the packaging strategy refers to the packaging method of the power production management application.

[0097] In this embodiment, the application image file refers to the result obtained by packaging the power production management application according to the packaging strategy, which is a file that can be directly deployed in the standardized container.

[0098] The working principle and beneficial effects of the technical solution are: by extracting the attribute parameters of the power production management application from the background terminal and determining the container parameters of the standardized container, the packaging strategy required by the power production management application is accurately and effectively determined according to the attribute parameters and the container parameters. Finally, the power production management application is packaged into an application image file according to the packaging strategy, so as to facilitate the deployment of the power production management application in the standardized container, and solve the problem of complex system running environment deployment.

[0099] Embodiment 3:

[0100] Based on the embodiment 1, the embodiment provides a cloud-native-based power production management system, as shown in Figure 2 The application deployment module comprises:

[0101] The application processing unit is configured to analyze the packaging result, extract the application structure features of the power production management application, and determine the splitting nodes of the power production management application based on the application structure features and the business execution process of the power production management application.

[0102] The application splitting unit is configured to split the power production management application into N microservices based on the splitting nodes, and allocate deployment resources to each microservice based on the splitting result.

[0103] The application deployment unit is configured to distribute the N microservices based on the deployment resources, and reserve extension and update interfaces for each microservice based on the distributed deployment result, and complete the deployment of the power production management application in the standardized container based on the reservation result.

[0104] In this embodiment, the application structure features refer to the components contained in the power production management application and the association or structural relationship between the components.

[0105] In this embodiment, the business execution process refers to the execution order between each step corresponding to the power production management application when working.

[0106] In this embodiment, the splitting node refers to the specific position of splitting the power production management application.

[0107] In this embodiment, the microservice refers to splitting the power production management application into multiple branches according to the splitting node, so as to facilitate the deployment of the power production management application.

[0108] In this embodiment, the deployment resource refers to the database and running degree required when deploying the microservice.

[0109] In this embodiment, the reserved extension and update interface refers to an interface reserved for each microservice after successful deployment of each microservice, thereby facilitating the extension and data update operation of the microservice.

[0110] The working principle and beneficial effects of the above technical solution are: by determining the application structure characteristics of the power production management application, the power production management application is split into N microservices according to the application structure characteristics, which provides convenience for application deployment. Secondly, each microservice is allocated deployment resources, and each microservice is effectively deployed according to the deployment resources. Finally, after deployment, an extension and update interface is reserved for each microservice, which facilitates the update and extension management of the microservice, and ensures the reliability and accuracy of the power production management application deployment.

[0111] Embodiment 4:

[0112] Based on embodiment 3, the embodiment provides a cloud-native-based power production management system, and the application deployment unit comprises:

[0113] The result obtaining subunit is configured to obtain the deployment result and start the power production management application deployed in the standardized container based on the deployment result.

[0114] The state monitoring subunit is configured to obtain the running state parameter of the power production management application in real time based on the starting result, analyze the running state parameter based on a preset state evaluation index, and obtain a state evaluation result.

[0115] The optimization subunit is configured to determine an optimization direction and an optimization parameter of the deployment result based on the running state parameter when the state evaluation result does not meet a preset requirement, and to optimize and adjust the deployment result based on the optimization direction and the optimization parameter.

[0116] In this embodiment, the running state parameter refers to the specific running condition of the power production management application in the standardized container.

[0117] In this embodiment, the preset state evaluation index is set in advance, for example, it can be stability and work efficiency.

[0118] In this embodiment, the preset requirement is set in advance, that is, the minimum running standard that the power production management application needs to reach under the preset state evaluation index.

[0119] In this embodiment, the optimization direction and the optimization parameter refer to the parameter type optimized for the deployment result and the specific value of the optimization of the parameter type when the state evaluation result does not meet the preset requirement.

[0120] The working principle and beneficial effects of the above technical solution are: by starting the power production management application after deployment, the running state parameters of the power production management application are comprehensively and effectively obtained, secondly, the running state parameters are analyzed according to the preset state evaluation index, the state evaluation result of the power production management application in the standardized container is accurately and effectively determined, and finally, when the state evaluation result does not meet the preset requirement, the deployment result is optimized and adjusted, thereby ensuring the running reliability of the power production management application, and facilitating effective management of power production.

[0121] Embodiment 5:

[0122] Based on embodiment 3, the embodiment provides a cloud-native-based power production management system, and an application deployment unit, comprising:

[0123] A link opening subunit is configured to obtain the deployment result of the power production management application in the standardized container, and open the real-time interaction link with the management terminal based on the deployment result;

[0124] An application update subunit is configured to:

[0125] receive the application update request and application update target issued by the management terminal based on the real-time interaction link, and analyze the application update target to obtain the to-be-updated microservice;

[0126] temporarily split and separate the deployment result of the to-be-updated microservice in the standardized container, and process the upper-level data cache of the to-be-updated microservice based on the temporary split and separation result;

[0127] Meanwhile, update the to-be-updated microservice based on the temporary split and separation, and deploy the to-be-updated microservice in the standardized container based on the update result;

[0128] Process the cached upper-level processing data based on the deployment reset result.

[0129] In this embodiment, the application update target refers to the specific content that the management terminal needs to update the power production management application.

[0130] In this embodiment, the to-be-updated microservice refers to the microservice that needs to be updated, which is one or more of all microservices of the power production management application.

[0131] In this embodiment, temporary split and separation refers to temporarily disconnecting the connection relationship and work acceptance relationship between the to-be-updated microservice and other microservices, the purpose being to update the to-be-updated microservice without affecting the work of other microservices.

[0132] In the embodiment, the upper processing data cache refers to temporarily storing the processing data of the last microservice of the to-be-updated microservice, so as to process the cached data in time after the to-be-updated microservice is updated.

[0133] In the embodiment, the deployment reset refers to resetting the updated to-be-updated microservice in the standardized container, so as to facilitate the continuous execution of the corresponding management operation.

[0134] The working principle and beneficial effects of the above technical solution are as follows: by opening the real-time interaction link between the standardized container and the management terminal according to the deployment result, the application update request and the application update target issued by the management terminal can be effectively received; secondly, the to-be-updated microservice is effectively determined according to the received application update target, and the to-be-updated microservice is temporarily split and independent, so as to facilitate the update operation of only the to-be-updated microservice, and the normal operation of other microservices is not affected; at the same time, after the to-be-updated microservice is updated, the deployment reset is performed in the standardized container, so as to realize the convenient and rapid update of the power production management application.

[0135] Embodiment 6:

[0136] Based on the embodiment 1, the embodiment provides a cloud-native-based power production management system, and the management module comprises:

[0137] The data collection preparation unit is configured to obtain a spatial structure distribution map of the power production scene, and extract distribution characteristics of each power equipment in the power production scene.

[0138] The parameter mapping unit is configured to extract business parameters of each power equipment, and map and label the business parameters of each power equipment in the spatial structure distribution map of the power production scene based on the distribution characteristics.

[0139] The position determination unit is configured to obtain a business layout map of the power production scene based on the mapping and labeling result, and determine a data collection dimension based on a preset power production management requirement.

[0140] The data collection unit is configured to:

[0141] Match the data collection dimension with the business parameters of each power equipment, determine a key data collection point based on the matching result, and guide the installation of a target sensor based on the key data collection point.

[0142] Real-time monitor each power equipment based on the installed target sensor, and obtain corresponding real-time power production data.

[0143] In this embodiment, the spatial structure distribution diagram refers to the spatial situation corresponding to the power production scene, including the position distribution of the power equipment and the spatial size of the power production scene, etc.

[0144] In this embodiment, the distribution feature refers to the position distribution of each power equipment in the power production scene.

[0145] In this embodiment, the business parameter refers to the working type corresponding to each power equipment, etc.

[0146] In this embodiment, the mapping annotation refers to annotating the business parameter of each power equipment at the power equipment in the spatial structure distribution diagram of the power production scene, so as to facilitate the intuitive determination of the specific situation of each power equipment through the spatial structure distribution diagram, wherein the business layout diagram is the result of the mapping annotation, including the position of the power equipment and the corresponding business parameter.

[0147] In this embodiment, the preset power production management requirement is known in advance.

[0148] In this embodiment, the data collection dimension refers to the type of data that needs to be collected.

[0149] In this embodiment, the key data collection point refers to a specific position that can effectively monitor the power equipment when the power equipment is monitored in real time.

[0150] In this embodiment, the target sensor refers to a plurality of different types of sensors that can monitor the operation of the power equipment.

[0151] The working principle and beneficial effects of the above technical solution are: by analyzing the spatial structure distribution diagram of the power production scene, the distribution feature of each power equipment is accurately and effectively determined, at the same time, the business parameter of each power equipment is obtained, the business parameter and the power equipment in the spatial structure distribution diagram are associated according to the distribution feature, then the data collection dimension of the power equipment is locked according to the obtained business layout diagram, so as to realize the locking of the key data collection point according to the data collection dimension, finally, the target sensor is installed according to the data collection point, and finally the power equipment is monitored in real time through the target sensor, so as to realize the accurate and effective acquisition of the real-time power production data, and ensure the real-time and reliability of the obtained real-time power production data.

[0152] Embodiment 7:

[0153] On the basis of embodiment 1, this embodiment provides a cloud-native-based power production management system, as shown in Figure 3 The management module comprises:

[0154] The data preprocessing unit is configured to:

[0155] The collected real-time power production data is clustered, and a multi-dimensional power data set corresponding to the real-time power production data is obtained based on the clustering;

[0156] Each dimension power data set is traversed, and based on the traversal result, the existing abnormal data is determined, at the same time, the sample representation of each dimension power data set is extracted, and the target strategy is recalled from the preset cleaning strategy library based on the sample representation to clean the abnormal data, and a multi-dimensional standard power data set is obtained;

[0157] The data analysis unit is used to:

[0158] Based on the power production operation protocol, the dependent limiting relationship between the multi-dimensional standard power data sets is determined, and each dimension standard power data set is assigned a corresponding weight value based on the dependent limiting relationship;

[0159] Each dimension standard power data set and the corresponding weight value are input into the power demand prediction model, and each dimension standard power data set is analyzed based on the input result, and the power label of the current time period is obtained;

[0160] Based on the power label, the demand floating feature of the current time period power grid is determined, and the demand floating feature is corrected based on the weight value corresponding to each dimension standard power data set, and the demand trend of the next stage of the power grid is obtained, and the power load demand is obtained based on the demand trend;

[0161] The power production scheduling unit is used to:

[0162] The performance parameters of each device in the power grid are analyzed to obtain the performance range of each device, and the power grid is traversed to obtain the topology structure and flow distribution of the power grid, and the line transmission capacity of the power grid is obtained based on the topology structure and flow distribution;

[0163] Based on the power load demand, the performance range of each device and the line transmission capacity of the power grid, a preliminary scheduling scheme of power production is obtained, and the preliminary scheduling scheme is simulated and run, and based on the simulation result, the preliminary scheduling scheme is adjusted for vulnerabilities, and a final scheduling scheme is obtained;

[0164] Based on the final scheduling scheme, the power production is scheduled and managed.

[0165] In this embodiment, the multi-dimensional power data set refers to different kinds of power data obtained after clustering operation (classification) of real-time power production data.

[0166] In this embodiment, the abnormal data refers to data with abnormal values or distortion existing in each dimension power data set.

[0167] In this embodiment, the sample representation refers to the data form of each dimension power data set and the corresponding specific type, etc.

[0168] In this embodiment, the preset cleaning strategy library is known in advance and internally stores a plurality of different data cleaning strategies, wherein the target strategy refers to a strategy suitable for cleaning the current dimension power data set.

[0169] In this embodiment, the multi-dimension standard power data set refers to the result obtained by cleaning the abnormal data in each dimension power data set through the target strategy.

[0170] In this embodiment, the power production operation protocol refers to the execution standard that needs to be followed by the power equipment during power production, etc.

[0171] In this embodiment, the dependent limitation relationship refers to the association relationship or the cooperative relationship between the multi-dimension standard power data sets, etc.

[0172] In this embodiment, the allocation of the corresponding weight value to each dimension standard power data set based on the dependent limitation relationship refers to the allocation of the weight value according to the importance of each dimension standard power data set in the overall operation process.

[0173] In this embodiment, the power label refers to the power production amount, the power consumption amount and the power consumption time period distribution of the current time period, etc.

[0174] In this embodiment, the demand floating feature refers to the change of the power demand amount in the power grid in the current time period,

[0175] In this embodiment, the performance parameter refers to the power production amount of each device per unit time, etc., and the performance range is the energy output interval finally obtained.

[0176] In this embodiment, the power flow distribution refers to the flow direction of the power produced by each device in the power grid.

[0177] The working principle and beneficial effects of the above technical solution are: by preprocessing the real-time power production data, the accuracy and reliability of the final power production data are ensured, secondly, the multi-dimensional standard power data set after preprocessing is analyzed, the accurate and effective determination of the power load demand of the power grid at different stages is realized, so as to facilitate the determination of the corresponding scheduling scheme according to the power load demand, finally, the performance parameters of each device in the power grid are analyzed, the performance range of each device is determined, and the topology structure and power flow distribution of the power grid are analyzed, the line transmission capacity of the power grid is locked, and finally the preliminary scheduling scheme of power production is obtained according to the power load demand, the performance range of each device and the line transmission capacity of the power grid, and the obtained preliminary scheduling scheme is simulated and adjusted, the final scheduling scheme is accurately and reliably formulated, thereby ensuring the effect and reliability of the scheduling management of power production.

[0178] Embodiment 8:

[0179] Based on embodiment 7, the embodiment provides a cloud-native-based power production management system, and a power production scheduling unit, which comprises:

[0180] A scheme obtaining sub-unit is configured to obtain the preliminary scheduling scheme, and build a virtual power production scene in the computer based on the structural relationship between the power grid and each device;

[0181] A simulation sub-unit is configured to:

[0182] The preliminary scheduling scheme is added in the background of the virtual power production scene based on the computer, and the power production of the virtual power production scene is simulated and scheduled based on the preliminary scheduling scheme according to the addition result;

[0183] The power production state of each production node is determined based on the simulation scheduling, and the potential hidden trouble factors and the corresponding hidden trouble representations existing in the preliminary scheduling scheme are determined based on the power production state;

[0184] A scheme adjusting sub-unit is configured to adjust the preliminary scheduling scheme based on the potential hidden trouble factors and the hidden trouble representations to obtain the final scheduling scheme.

[0185] In this embodiment, the virtual power production scene refers to the simulation model of the power grid and each device constructed in the computer.

[0186] In this embodiment, the power production state refers to the actual situation of power production of each production node under the action of the scheduling scheme, wherein the production node refers to each power device.

[0187] In this embodiment, the hidden trouble representation refers to the failure phenomenon caused by the potential hidden trouble factors.

[0188] The working principle and beneficial effects of the above technical solution are: by constructing a virtual power production scene in the computer and simulating the preliminary scheduling, while monitoring and analyzing the power production state of each production node in the process of simulation scheduling, timely and accurate determination of potential hidden trouble factors and corresponding hidden trouble representations is realized, so as to realize the vulnerability adjustment of the preliminary scheduling scheme according to the potential hidden trouble factors and the corresponding hidden trouble representations, and ensure the reliability of the final obtained scheduling scheme.

[0189] Embodiment 9:

[0190] Based on the embodiment 1, the embodiment provides a cloud-native-based power production management system, a monitoring module, comprising:

[0191] The monitoring unit is configured to:

[0192] Obtain the monitoring requirement of power production, and determine the monitoring dimension and monitoring node based on the monitoring requirement;

[0193] Configure the monitoring mechanism based on the monitoring dimension and the monitoring node, and perform full-life-cycle monitoring on the scheduling process of the power production based on the monitoring mechanism;

[0194] The classification unit is configured to obtain the multi-dimensional scheduling parameters of the power production and the state parameters of each device based on the full-life-cycle monitoring result, and map and associate the state parameters of each device and the multi-dimensional scheduling parameters;

[0195] The recording unit is configured to:

[0196] Construct a parameter record table, and extract the timestamp corresponding to the full-life-cycle monitoring result;

[0197] Record and manage the state parameters of each device and the multi-dimensional scheduling parameters in the parameter record table based on the timestamp and the mapping and association result.

[0198] In this embodiment, the monitoring requirement refers to the rigorousness and dimension of monitoring the power production, wherein the monitoring dimension refers to the type to be monitored.

[0199] In this embodiment, the monitoring node refers to the specific location of the power production to be monitored.

[0200] In this embodiment, the full-life-cycle monitoring refers to comprehensive monitoring of the scheduling process of the power production, i.e. monitoring from the beginning to the end.

[0201] In this embodiment, the multi-dimensional scheduling parameter refers to the specific condition corresponding to the scheduling of electric energy and resources in the power production process.

[0202] The working principle and beneficial effects of the above technical solution are: by constructing a monitoring mechanism, the whole life cycle monitoring of the scheduling process of power production through the monitoring mechanism is realized, so as to accurately and effectively determine the multi-dimensional scheduling parameters of power production and the state parameters of each device. Secondly, a parameter record table is constructed, and the time stamp corresponding to the whole life cycle monitoring result is determined, so as to effectively record and manage the multi-dimensional scheduling parameters of power production and the state parameters of each device in the parameter record table according to the time stamp, thereby facilitating data traceability.

[0203] Embodiment 10:

[0204] On the basis of Embodiment 9, the present embodiment provides a cloud-native-based power production management system, a recording unit comprising:

[0205] A parameter analysis sub-unit is configured to extract the state parameters of each device in the power production scheduling process, and determine the state of each device based on the state parameters.

[0206] A safety monitoring sub-unit is configured to:

[0207] Visualize the state of each device in the visual coordinate system based on the time series, and obtain the state change amplitude of each device based on the visualization.

[0208] Determine the risk device based on the state change amplitude and the reference amplitude interval, and re-allocate the monitoring resources of the monitoring mechanism based on the risk device.

[0209] Based on the re-allocation result, the risk device is monitored, and when there is a fault, pre-warning management is performed.

[0210] In this embodiment, the state change amplitude refers to the change trend of the state of each device in the power production process.

[0211] In this embodiment, the reference amplitude interval is set in advance and is a standard for measuring whether a device is a risk device. It can be adjusted.

[0212] In this embodiment, the monitoring resource refers to the database or network requirements required for performing the monitoring operation.

[0213] The working principle and beneficial effects of the technical scheme are as follows: by extracting and analyzing the state parameters of each device in the power production scheduling process, the state of each device is effectively determined, then the state of each device is visually displayed in the visual coordinate system, the state change of each device is effectively determined according to the visual display result, so as to determine the risk device according to the state change, finally, the monitoring resources of the monitoring mechanism are redistributed, the risk device is monitored according to the redistribution result, so as to ensure the operation stability and safety of the device, and improve the management effect of the power production.

[0214] Obviously, those skilled in the art can make various modifications and variations to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the claims of the present application and their equivalent technologies, the present application also intends to include these modifications and variations.

Claims

1. A cloud-native power production management system, characterized by, The application deployment module is used for packaging the power production management application based on cloud native, and deploying the power production management application in a standardized container based on the packaging result. The management module is used for analyzing the collected real-time power production data based on the deployment result, and scheduling the power production based on the analysis result. The monitoring module is used for monitoring the whole life cycle of the power production based on the scheduling result, and recording and managing the whole life cycle monitoring result. The application deployment module includes: The application processing unit is used for analyzing the packaging result, extracting the application structure features of the power production management application, and determining the splitting nodes of the power production management application based on the application structure features and the business execution process of the power production management application. The application splitting unit is used for splitting the power production management application into N microservices based on the splitting nodes, and allocating deployment resources to each microservice based on the splitting result. The application deployment unit is used for distributing the N microservices based on the deployment resources, reserving extension and update interfaces for each microservice based on the distributed deployment result, and completing the deployment of the power production management application in the standardized container based on the reservation result. The management module includes: The data preprocessing unit is used for: The data analysis unit is used for: The power production scheduling unit is used for: The application deployment module includes: ​ ​ ​ ​ ​ ​ ​ ​ 2. The cloud-native based power production management system of claim 1, wherein, ​ The application acquisition unit is configured to acquire a power production management application, extract attribute parameters of the power production management application based on the background terminal, and acquire container parameters of a standardized container; The application packaging unit is configured to determine a packaging strategy required by the power production management application based on the attribute parameters and the container parameters, and package the power production management application into an application image file based on the packaging strategy; The uploading unit is configured to upload the application image file to the standardized container. 3.The cloud-native based power production management system of claim 1, wherein, The application deployment unit includes: The result acquisition subunit is configured to acquire a deployment result, and start the power production management application deployed in the standardized container based on the deployment result; The state monitoring subunit is configured to acquire running state parameters of the power production management application in real time based on a starting result, analyze the running state parameters based on preset state evaluation indexes, and obtain a state evaluation result; The optimization subunit is configured to determine an optimization direction and an optimization parameter of the deployment result based on the running state parameters when the state evaluation result does not satisfy preset requirements, and optimize and adjust the deployment result based on the optimization direction and the optimization parameter.

4. The cloud-native based power production management system of claim 1, wherein, The application deployment unit includes: The link opening subunit is configured to acquire a deployment result of the power production management application in the standardized container, and open a real-time interaction link with the management terminal based on the deployment result; The application updating subunit is configured to: receive an application updating request and an application updating target issued by the management terminal based on the real-time interaction link, analyze the application updating target to obtain a microservice to be updated, temporarily split and separate the deployment result of the microservice to be updated in the standardized container, and process data cached by the microservice to be updated based on a temporary split and separation result; update the microservice to be updated based on the temporary split and separation, and deploy and reset the microservice to be updated in the standardized container based on an updating result; process the cached data based on a deployment reset result.

5. The cloud-native based power production management system of claim 1, wherein, The management module includes: The data acquisition preparation unit is configured to acquire a spatial structure distribution map of a power production scene, and extract distribution characteristics of each power device in the power production scene; The parameter mapping unit is configured to extract business parameters of each power device, and map and label the business parameters of each power device in the spatial structure distribution map of the power production scene based on the distribution characteristics; The position determination unit is configured to obtain a business layout map of the power production scene based on a mapping and labeling result, and determine a data acquisition dimension based on preset power production management requirements; The data acquisition unit is configured to: match the data acquisition dimension with the business parameters of each power device, determine a key data acquisition point based on a matching result, and guide installation of a target sensor based on the key data acquisition point; monitor each power device in real time based on the installed target sensor, and obtain corresponding real-time power production data.

6. The cloud-native based power production management system of claim 1, wherein, The power production scheduling unit includes: The scheme acquisition subunit is configured to acquire a preliminary scheduling scheme, and build a virtual power production scene in a computer based on a structural relationship between a power grid and each device; The simulation subunit is configured to: The computer-based preliminary scheduling scheme is added in the background of the virtual power production scene, and the power production of the virtual power production scene is simulated and scheduled according to the preliminary scheduling scheme based on the addition result; Determine the power production state of each production node based on the simulation scheduling, and determine the potential hidden danger factors and corresponding hidden danger representations existing under the preliminary scheduling scheme based on the power production state; The scheme adjustment sub-unit is used for adjusting the preliminary scheduling scheme based on the potential hidden danger factors and the hidden danger representations, and obtaining the final scheduling scheme.

7. The cloud-native based power production management system of claim 1, wherein, The monitoring module comprises: The monitoring unit is used for: Obtain the monitoring demand of power production, and determine the monitoring dimension and monitoring node based on the monitoring demand; Configure the monitoring mechanism based on the monitoring dimension and the monitoring node, and monitor the whole life cycle of the scheduling process of the power production based on the monitoring mechanism; The classification unit is used for obtaining the multi-dimensional scheduling parameters of the power production and the state parameters of each device based on the whole life cycle monitoring result, and mapping and associating the state parameters of each device and the multi-dimensional scheduling parameters; The recording unit is used for: Construct a parameter record table, and extract the time stamp corresponding to the whole life cycle monitoring result; Record and manage the state parameters of each device and the multi-dimensional scheduling parameters in the parameter record table based on the time stamp and the mapping and association result.

8. The cloud-native based power production management system of claim 7, wherein, The recording unit comprises: The parameter analysis sub-unit is used for extracting the state parameters of each device in the power production scheduling process, and determining the state of each device based on the state parameters; The safety monitoring sub-unit is used for: Visualize the state of each device in the visualization coordinate system based on the time sequence, and obtain the state change amplitude of each device based on the visualization; Determine the risk device based on the state change amplitude and the reference amplitude interval, and redistribute the monitoring resources of the monitoring mechanism based on the risk device; Based on the redistribution result, the risk device is monitored, and when there is a fault, the pre-warning management is performed.

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