Digital operation management and control platform based on shared service operation management

Abstract

The invention provides a digital operation management and control platform based on shared service operation management. The digital operation management and control platform comprises a data acquisition module, a data management module and a resource allocation module, wherein the data acquisition module is used for acquiring business data in different business scenes in real time, and performing standardization processing on the acquired business data; the data management module is used for building a shared database according to the standardized business data and performing association identification management on the business data in the shared database; the resource allocation module is used for acquiring a data calling request initiated by a requester based on a service scene, responding to the data calling request according to a dynamic resource allocation rule, acquiring a priority level of the data calling request, and allocating a corresponding processing node according to the priority level to call corresponding data; and the processing node returns the data processing result to the requester. According to the invention, the intelligent level of data resource allocation in the service sharing process is improved, and the quality and effect of the service sharing are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power grid digital operation, in particular to a digital operation management and control platform based on shared service operation management. BACKGROUND

[0002] With the acceleration of the digital transformation process of power grid business, the demand for data sharing in the power grid business scenario presents an exponential growth. The traditional power grid digital operation management mode faces the following core problems: the static and single resource allocation mode is difficult to cope with the dynamic load demand in the business peak period, often causing service response delay or even non-response, resulting in that the demand for shared data acquisition of shared services cannot be met in the actual application process. SUMMARY

[0003] In view of the above problems, the present application aims to provide a digital operation management and control platform based on shared service operation management.

[0004] The purpose of the present application is achieved by using the following technical solutions: The present application provides a digital operation management and control platform based on shared service operation management, comprising a data acquisition module, a data management module and a resource allocation module; wherein, The data acquisition module is used to access heterogeneous data sources of different business scenarios, collect business data in different business scenarios in real time, and standardize the acquired business data to obtain standardized business data; The data management module is used to build a shared database according to the standardized business data, and to manage the associated identification of the business data in the shared database; The resource allocation module is used to obtain a data call request initiated by a request party based on a business scenario, to respond to the data call request according to a dynamic resource allocation rule, to obtain the priority level of the data call request, and to allocate a corresponding processing node according to the priority level to call the corresponding data and complete data processing, and to return the data processing result to the request party by the processing node.

[0005] Preferably, the system further comprises a permission management module; wherein, The permission management module is used to verify the identity information of the request party initiating the data call request, and to obtain the corresponding data call permission of the request party based on the verified identity information.

[0006] Preferably, the data acquisition module comprises a data access unit, a metadata extraction unit and a standardization processing unit; wherein, The data access unit is used to connect with the data interface of each business scenario to obtain the original data stream of each business scenario; The metadata extraction unit is configured to extract metadata from the obtained raw data stream to form a feature vector corresponding to the raw data stream, wherein the extracted metadata comprises data type, update frequency, single data volume, transmission delay, etc. The standardization processing unit is configured to call a corresponding standardization processing standard to perform standardization processing on the raw data stream according to the obtained feature vector, to obtain service data after standardization processing.

[0007] Preferably, the data management module comprises a database unit and an association unit. The database unit is configured to build a shared database according to the service data after standardization processing, and to perform classified storage management on the service data after standardization processing and the corresponding metadata. The association unit is configured to extract associated features according to the service data in the shared database, and to perform association identification on the service data that have mutual association according to the extracted features.

[0008] Preferably, the resource allocation module comprises a request acquisition unit, an allocation response unit and a dynamic allocation unit. The request acquisition unit is configured to acquire a data call request initiated by a requestor based on a service scenario, wherein the data call request comprises requestor information, service scenario description, request data range, etc. The allocation response unit is configured to analyze the requestor information, the service scenario description and the request data range carried in the data call request to obtain a data call priority intensity. The dynamic allocation unit is configured to allocate a data processing node of a corresponding level to complete a corresponding data call task and a further data processing task according to the obtained priority intensity, and to acquire a corresponding service data memory image from the shared database by the data processing node and return the image to the requestor.

[0009] Preferably, the dynamic allocation unit specifically comprises: According to the data call priority intensity of the data call request, when the data call priority intensity is within a set first priority range, a first-level data processing node is allocated to respond to the data call request, so that the first-level data processing node can directly locate the shared database according to the data call request, directly acquire a mirror image of the required service data from the shared database, and transmit the mirror image to the requestor. When the data call priority intensity is within a set second priority range, a second-level data processing node is allocated to complete the response to the data call request, so that the second data processing node uses a load balancer to allocate an idle second-level data processing node to respond to the data call request, and completes forwarding of the required data. When the data call priority intensity is in a set third priority range, a third data processing node is allocated to complete the response to the data call request, so that the third data processing node asynchronously acquires the required data through edge processing, and then transmits the acquired data to the requestor.

[0010] The application has the advantages that: the application can guarantee the quality of data when collecting business data by first collecting and preprocessing heterogeneous source data under different business scenarios through the data acquisition module; for the standardized business data, a shared database is built to centrally store and manage the business data, and the associated data in the business data is identified to facilitate subsequent calling; the data call request issued by the requestor is responded to through the resource allocation module, the corresponding data processing node is allocated according to the characteristics of the data call request to complete the extraction and calling of data, which helps to real-time control based on the priority of the data call request and the current load, improves the intelligent level of data resource allocation in the shared service process, and improves the quality and effect of the shared service. BRIEF DESCRIPTION OF DRAWINGS

[0011] The application is further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of the following drawings.

[0012] Figure 1 Fig. 1 is a framework structure diagram of a digital operation management and control platform based on shared service operation management according to an embodiment of the application; Figure 2 Fig. 2 is a specific setting framework diagram of the functional modules in the embodiment. Figure 1 Fig. 2 is a specific setting framework diagram of the functional modules in the embodiment. DETAILED DESCRIPTION

[0013] The application is further described in combination with the following application scenarios.

[0014] Reference is made to Fig. 1, which shows a digital operation management and control platform based on shared service operation management, comprising a data acquisition module, a data management module and a resource allocation module; wherein, Figure 1 The data acquisition module is used to access heterogeneous data sources of different business scenarios, collect business data under different business scenarios in real time, and standardize the acquired business data to obtain standardized business data. The data management module is used to build a shared database according to the standardized business data, and to manage the associated identification of the business data in the shared database. The data management module is used to build a shared database according to the standardized business data, and to manage the associated identification of the business data in the shared database. The resource allocation module is configured to acquire a data call request initiated by a requester based on a service scenario, respond to the data call request according to a dynamic resource allocation rule, acquire a priority level of the data call request, and allocate a corresponding processing node according to the priority level to call corresponding data and complete data processing, and return the data processing result to the requester by the processing node.

[0015] The above embodiment of the present application can guarantee the quality of data when collecting service data by first collecting and preprocessing heterogeneous source data under different service scenarios; for the standardized service data, a shared database is built to centrally store and manage the service data, and the associated data in the service data is identified to facilitate subsequent calling. For the data call request initiated by the requester based on the service scenario, the resource allocation module responds to the data call request issued by the requester, and can allocate corresponding data processing nodes to complete data extraction and calling according to the characteristics of the data call request, which helps to real-time control based on the priority of the data call request and the current load condition, improves the intelligent level of data resource allocation in the shared service process, and improves the quality and effect of the shared service.

[0016] In one scenario, the digital operation control platform is built based on a shared database server (server), which can acquire and store data of the shared database, and respond to the requester's request for service data in the shared database, and complete the transmission and scheduling of the required data.

[0017] Different data processing nodes have different performance and data scheduling modes when completing shared data. In one scenario, the data processing nodes can be divided into dedicated data nodes, general data nodes and edge data nodes, and the three types of nodes use three different data scheduling modes to complete data scheduling tasks.

[0018] The dedicated data node and the shared database establish a physical channel (such as an RDMA network connection), which enables the dedicated data node to directly access the data mirror in the shared database and assist in completing the corresponding data scheduling task through the "exclusive" computing node, to obtain the shared data resources required by the requester in the highest priority and lowest delay.

[0019] The general data node and the load balancing module first establish a connection, and the load balancing module controls the access performance of the general data node to the shared data in the shared database. The load balancing module coordinates the access priority and performance (data transmission rate and data transmission resources) of each general data node according to the current access and data acquisition load of the shared database, to maximize the shared data scheduling request of each general data node.

[0020] Edge data nodes retrieve relevant business data from the shared database using low-priority methods (such as asynchronous scheduling). Based on the load of the shared server or the set idle time period, they retrieve data mirrors from the shared database and store them in the local edge node. They complete the corresponding data scheduling tasks based on the asynchronous data. In other words, edge data nodes can handle real-time, low-priority data scheduling tasks, and additional edge node settings can share the computing power and data transmission pressure of the shared database.

[0021] In one scenario, the business scenarios include: power plant load monitoring, substation operation monitoring, line monitoring, load forecasting center, new energy power generation forecasting, meteorological monitoring and forecasting, equipment asset records, fault repair, emergency command, customer service, electricity management, power grid planning, safety and operation management, integrated energy services, and other data sources corresponding to different business scenarios. The shared data acquired includes real-time / near real-time SCADA measurement data, PMU data, equipment condition monitoring data (online monitoring), power generation plans, load forecasting, new energy power forecasting, power grid models, topology information, and equipment management departments need to comprehensively assess equipment health status, predict fault risks, and optimize maintenance plans by integrating equipment ledger information, historical defect / fault records, inspection reports, online monitoring data (such as oil chromatography, partial discharge, temperature, vibration), test data, environmental data, etc., based on equipment basic information (ledger), historical operation and maintenance records (defects, faults, maintenance), real-time / historical online monitoring data, inspection results, preventive test reports, environmental data (meteorology, pollution), and GIS spatial information. The data includes: fault trip signals, protection action information, SCADA remote signaling changes, fault indicator signals, power outage range analysis results (based on topology), affected customer information (customer-transformer relationship), GIS geographic information, emergency repair resource status and trajectory, emergency repair progress, power restoration notice, customer basic files, real-time / historical metered electricity (AMI), electricity bill information, power outage plans and fault outage information, application process status, service work order status, adjustable load resource information (capacity, status), electricity price information, long-term historical load data, load density data, load forecast results, new energy development plans and output characteristics, power grid equipment ledger and capacity, power grid topology model, GIS spatial data and land information, regional economic development data, work plans, work permit / operation ticket information, personnel qualification information, real-time personnel location and trajectory, on-site video images, equipment energized / de-energized status, GIS geographic information and risk labeling, user electricity consumption data (refined), power grid operation status (affecting access), distributed energy output data, meteorological data, and other user energy data (if any), etc.

[0022] Preferably, the system also includes a permission management module; wherein, The permission management module is configured to verify the identity information of the requestor initiating the data call request, and obtain the data call permission of the requestor based on the verified identity information.

[0023] By setting the permission management module, the identity information of the requestor can be verified first, and the security of data scheduling and data resource distribution in the shared database can be improved.

[0024] Preferably, referring to Figure 2 The data acquisition module includes a data access unit, a metadata extraction unit, and a standardization processing unit; wherein, The data access unit is configured to connect with the data interfaces of various business scenarios to obtain the original data streams of the various business scenarios. The metadata extraction unit is configured to extract metadata from the obtained original data streams to form feature vectors corresponding to the original data streams, wherein the extracted metadata includes data types, update frequencies, single data volumes, transmission delays, etc. The standardization processing unit is configured to call corresponding standardization processing standards to perform standardization processing on the original data streams based on the obtained feature vectors to obtain the business data after standardization processing.

[0025] For the original data streams obtained from different business scenarios, the original data streams are first standardized, and the data resources are labeled to facilitate subsequent classification storage management in the shared database and subsequent data resource calling, thereby improving data quality.

[0026] Considering that the business data obtained from different scenarios is large in quantity, when performing standardization processing on the original data streams, in order to ensure data quality, if the same standard is used to process the original data streams (for example, data cleaning, error correction, and other preprocessing), a large amount of data processing resources will be consumed, thereby increasing the pressure of the system on the data acquisition stage. Therefore, when performing standardization preprocessing, a quality weight judgment is first proposed for the characteristics of the original data streams, so that different standards are adaptively used to complete the standardization processing of the original data streams to improve system performance.

[0027] Preferably, in the standardization preprocessing unit, the corresponding standardization processing standards are called to perform standardization processing on the original data streams, specifically including: The quality weight of the current original data stream is determined according to the historical data quality of the data source to which the original data stream belongs, wherein the quality weight acquisition function used is: In the formula, Qi represents the quality weight of the i-th original data stream, Qi represents the historical missing field proportion of the i-th original data stream, denotes the historical receiving delay of the i-th original data stream, which is obtained according to the difference between the time stamp when the data is generated and the time stamp when the data is received; denotes the set allowable maximum receiving delay, denotes the historical data error rate of the i-th original data stream, which is obtained according to the proportion of the data amount exceeding the corresponding standard value range in the statistical historical original data stream, denotes the set allowable maximum data error rate; , , denote the set weight factors, respectively; According to the quality weight of the original data stream, when the quality weight exceeds the preset weight standard, only the current original data stream is subjected to format conversion processing, the original data stream is converted to a preset standard format, and the standardized processed service data is obtained; otherwise, when the quality weight does not exceed the preset weight standard, the original data stream is further subjected to data cleaning processing such as data difference and abnormal value exclusion, and then the cleaned data is subjected to format conversion, and the standardized processed service data is obtained.

[0028] In a scene, according to the historical data quality of the data source to which the original data stream belongs, the historical data of the same data source as the basis is 1 day, 3 days, 7 days, 15 days, 20 days or 30 days, and the corresponding indicators are counted according to the historical data, wherein the proportion of the historical missing fields can obtain the related missing field data according to the audit log of the historical data; the historical receiving delay can be obtained according to the time difference between the generation time and the receiving time of the data; the historical data error rate is obtained according to the proportion of the data amount exceeding the standard range (such as the numerical standard range or the data type standard range) in the data.

[0029] In the above embodiment of the present application, when the obtained massive service data is standardized and preprocessed, first, the quality of the historical data obtained from different data sources is taken as a benchmark to evaluate the quality of the data obtained from the same data source at present. For the data source with high and relatively stable quality, the quality of the original data stream obtained from the data source is expressed as trust, thereby reducing the degree of standardization processing of the original data stream (only necessary standardization processing is performed), and for the original data stream with unstable data quality or low data quality, a higher degree of standardization processing is adopted (for example, including data cleaning) to perform the first standardization processing on the data, thereby improving the data quality. The quality weight calculation method is used to evaluate the data quality of the current data, which can be based on the posteriori quality of the original data stream (for example, realized according to the log or idle time sampling method) as a basis to accurately determine the degree of standardization of the real-time data obtained from different data sources, thereby reducing the pressure of the system on the preprocessing of the massive original data stream obtained from different data sources under the condition of ensuring the data quality, thereby improving the system performance.

[0030] Since the real-time obtained original data stream cannot be judged in real time for data quality or error data (a large amount of computing resources is required), but considering that when the original data stream transmitted in the receiving service scene is received, the data transmission factor has a key influence on the quality of the data stream (the transmission delay condition is usually accompanied by packet loss, error data, etc.), therefore, in the quality weight calculation method proposed in the above embodiment, the historical data transmission quality and delay of the data source corresponding to the original data stream are taken as the evaluation standard, thereby evaluating the quality of the data source transmitting the original data stream, reflecting the data transmission quality of the data source.

[0031] Preferably, the data management module comprises a database unit and an association unit. The database unit is used to build a shared database according to the standardized service data, and to classify and store the standardized service data and the corresponding metadata. The association unit is used to extract association features according to the service data in the shared database, and to associate and identify the service data associated with each other according to the extracted management features.

[0032] After the standardized service data is stored in the shared database, the stored service data is further associated and processed, so that the service data associated with each other can be stored in association, which facilitates data retrieval according to the features and association features of the service data when the data is called, and improves the pertinence and effect of data retrieval.

[0033] In the association unit, the association features extracted from the business data include metadata features such as data source, belonging region, security level, business data type, and business data category, and an association data tag is established according to one or more metadata features, and business data that meets the one or more metadata features is uniformly marked with the same association data tag. Meanwhile, on the basis of the data association according to the metadata, advanced features such as semantic features (for example, semantic vectors) of the business data can be further added as a basis to further associate data that is the same or similar (for example, based on semantic cosine similarity).

[0034] Preferably, the resource allocation module includes a request acquisition unit, an allocation response unit, and a dynamic allocation unit; wherein, The request acquisition unit is configured to acquire a data call request initiated by a requestor based on a business scenario, wherein the data call request includes requestor information, business scenario description, and request data range. The allocation response unit is configured to analyze the requestor information, business scenario description, and request data range carried in the data call request to obtain a data call priority strength. The dynamic allocation unit is configured to allocate data processing nodes of a corresponding level according to the obtained priority strength to complete corresponding data retrieval tasks and further data processing tasks, and the data processing nodes obtain corresponding business data memory images from a shared database and return them to the requestor.

[0035] In a scenario, data requestors include a fault management center, a power grid general control console, a dispatching analysis server, a general resident user, a payment business center, and the like. Different requestors need different business data according to business scenarios. For example, the fault management center needs business data including power grid operation data in a region, and the payment business center needs business data including node monthly cumulative power consumption data in a region, and the like.

[0036] The business scenario description is a description content of a business data request initiated for a business scenario, which includes a business scenario content description, such as “fault positioning”, “load prediction”, “monthly report summary”, “electricity fee query”, and the like.

[0037] The request data range is a range of required business data, including time features, regional features, object features, security level features, and the like, which limit the range of business data; and the business scenario information includes a content description of an application scenario or role of the required business data.

[0038] When the data requester initiates a data call request according to a business scenario, the data call request is analyzed by the allocation response unit to determine the corresponding data call priority, and the dynamic allocation unit allocates the corresponding data processing node to complete the data distribution task corresponding to the data call request according to different data call priorities, so that the allocation of the data processing node is more reasonable, and can adapt to the requirements of data call requests under different priorities and different business scenarios. The overall performance of the shared data system is improved, and the response effect of the data sharing system to the data call task is optimized.

[0039] Preferably, the allocation response unit analyzes the requestor information, business scenario description and request data range carried in the data call request, specifically including: extracting the data transmission path delay between the current server and the requestor according to the requestor information; extracting the corresponding business scenario semantic features according to the business scenario description, and calculating the similarity between the semantic scenario business features and the standard features of each standard business scenario to obtain the standard business type corresponding to the data extraction request; locking the required business data range according to the obtained standard business type and request data range, and obtaining the corresponding data call chain information according to the required business data range, and calculating the complexity of the data call chain; obtaining the data call priority strength of the current data call request: In the formula, represents the call priority strength of the current data call request k; represents the business level of the standard business type corresponding to the data call request; represents the data transmission path delay of the requestor; represents the delay sensitivity coefficient, represents the complexity of the data call chain.

[0040] The data transmission path delay of the requestor can be obtained according to the real-time delay information of the requestor monitored by the requestor accessing the API gateway; the semantic features are extracted from the business scenario description according to the data call request content initiated by the requestor, and the similarity analysis is performed according to the semantic features and the preset standard business scenarios (such as summarizing, positioning, querying, repairing, predicting, analyzing, etc.), so that the standard business type and the corresponding business level corresponding to the data extraction request are obtained according to the similarity analysis result (usually the maximum similarity), wherein the higher the business level is, the higher the corresponding data call request priority is. The complexity of the data call chain represents the dispersion degree of the data required by the data call request, which is calculated by the response speed of the server to the historical same or similar data call request.

[0041] The priority intensity calculation manner can evaluate the semantics, timeliness and complexity of the service data calling request in a unified dimension, and comprehensively analyze the priority intensity of the data calling request. When the priority intensity is higher, it indicates that the data calling request needs the server to allocate more resources for priority processing, so as to meet the demand of shared data calling in the key service scenario service, and therefore a data node with better performance is used to process the corresponding data calling request. When the priority intensity is lower, it indicates that the real-time requirement of the current data calling task is reduced, so as to ensure the balanced allocation of the performance of the shared data server, and therefore a general or edge data node is used to process the corresponding data calling request. Through the above manner, the priority intensity of the data calling request is analyzed, the comprehensive priority of the data calling task is evaluated according to the actual situation, and a basis is provided for further allocating a corresponding data node to process the corresponding data calling task.

[0042] In a scene, the traditional priority allocation manner considering only the service scenario lacks consideration of the actual data transmission situation and data calling complexity, and the priority allocation manner based on a single dimension is likely to lock the resources of the high-level data node when a single high-priority task has data transmission or data calling exception, thereby affecting the performance of processing subsequent data calling tasks. The priority intensity evaluation manner provided in the above embodiment based on semantics, timeliness and complexity can further comprehensively analyze the data transmission situation of the requester and the complexity of the calling task on the basis of the service scenario, so as to degrade the processing of the “abnormal” high-priority task, thereby better allocating the resources of the high-level data node and improving the comprehensive performance in the shared data allocation process.

[0043] Preferably, the dynamic allocation unit specifically comprises: According to the data calling priority intensity of the data calling request, when the data calling priority intensity is within a set first priority range, a first-level data processing node is allocated to respond to the data calling request, so that the first-level data processing node can directly access the shared database according to the data calling request, directly obtain the mirror of the required business data from the shared database and transmit the mirror to the requester; When the data calling priority intensity is within a set second priority range, a second-level data processing node is allocated to complete the response to the data calling request, so that the second data processing node uses the load balancer to allocate an idle second-level data processing node to respond to the data calling request and complete the forwarding of the required data; When the data call priority intensity is in the set third priority range, a third data processing node is allocated to complete the data call request response, so that the third data processing node asynchronously acquires the required data through edge processing, and then transmits the acquired data mirror to the requester.

[0044] According to the priority intensity of the data call request, the dynamic allocation unit allocates three different data nodes to complete the corresponding data call task, and through the three data processing nodes set, different characteristic data call tasks can be completed respectively, so as to improve the comprehensive performance of the server in responding to shared data calls.

[0045] In one scenario, after the data call priority intensity is range-limited and normalized, the data call priority intensity The value range of is [0, 1], wherein when , a first data processing node is allocated to complete the data scheduling task k, when , a second data processing node is allocated to complete the data scheduling task k, and when , a third data processing node is allocated to complete the data scheduling task k.

[0046] In one scenario, the first data processing node is a dedicated data node, the second data node is a general data node, and the third data node is an edge data node.

[0047] The dedicated data node and the shared database establish a physical channel (such as through an RDMA network connection), which enables the dedicated data node to directly access the data mirror in the shared database and assist in completing the corresponding data scheduling task through the "exclusive" computing node, so as to obtain the shared data resources required by the requester in the highest priority and lowest delay.

[0048] The general data node first establishes a connection with the load balancing module, and the load balancing module controls the access performance of the general data node to the shared data in the shared database. The load balancing module coordinates the access priority and performance (data transmission rate and data transmission resources) of each general data node according to the current access and data acquisition load of the shared database, so as to maximize the shared data scheduling request of each general data node.

[0049] The edge data node obtains the corresponding business data from the shared database in a low-priority (for example, asynchronous scheduling) manner, obtains data mirror from the shared database according to the load condition of the shared server or the set idle period and saves the data mirror in the local edge node, and completes the corresponding data scheduling task according to the asynchronous data, that is, the edge data node can process real-time and low-priority data scheduling tasks, and the additional edge node setting can share the computing power and data transmission pressure of the shared database.

[0050] In one scenario, for a data call task of measurement data in a current fault section requested by a requestor, the data call and transmission are completed in the following manner: The system obtains a data call request issued by a fault locating system, analyzes the corresponding data call priority intensity according to the data call request, and determines that the data call priority intensity is The system allocates a dedicated data node to complete the data call task, the dedicated data node obtains a required data set from the shared database according to the data call request, and a direct interface between the fault locating system and the shared database is established (IP: 192.168.10.3:9001). The fault locating system directly obtains required data (millisecond-level updated power grid data in a region) from the shared database according to the positioning of the data set, so that the fault locating system can complete a fault locating task according to the obtained data. The dedicated data node is released after the task is completed.

[0051] In another scenario, for historical data of a power transmission line requested by a prediction system, the data call and transmission are completed in the following manner: the system obtains a data call request issued by the prediction system, analyzes the corresponding data call priority intensity according to the data call request, and determines that the data call priority intensity is The system allocates a general data node to complete the data call task, the general data node is uniformly controlled by a load balancer, the load balancer calculates the current load conditions of each general data node (node A load 0.42, node B load 0.58) according to the obtained value and business scenario label, and allocates a corresponding general data node (node A (IP: 10.0.0.12)) to establish a connection with the prediction system according to the balancing principle. Node A accesses a corresponding data pool to the shared database and pulls required data according to the data request, and returns the data to the prediction system.

[0052] In another scenario, for regional user monthly electricity data requested by an electricity settlement system, the data call and transmission are completed in the following manner: the system obtains a data call request issued by the electricity settlement system, analyzes the corresponding data call priority intensity according to the data call request, and determines that the data call priority intensity is The system allocates an edge data node to complete the data calling task, and the edge node obtains a copy of the required data (including a user ID, power consumption data in a specified time period, etc.) from the shared database during an idle time (for example, a preset time period or according to a condition judgment) according to a current load of the shared database, performs statistics on the data copy through the edge data node, obtains a user reading power consumption statistical result, and returns the statistical result directly to the electricity bill settlement system.

[0053] It should be noted that the various functional units / modules in the various embodiments of the present application can be integrated in one processing unit / module, or can be physically present separately, or two or more units / modules can be integrated in one unit / module. The integrated unit / module can be realized in the form of hardware or in the form of a software functional unit / module.

[0054] From the above description of the embodiments, those skilled in the art can clearly understand that it should be understood that the embodiments described herein can be realized in hardware, software, firmware, middleware, code or any appropriate combination thereof. For hardware implementation, the processor can be implemented in one or more of the following units: application specific integrated circuit (ASIC), digital signal processor (DSP), digital signal processing device (DSPD), programmable logic device (PLD), field programmable gate array (FPGA), processor, controller, microcontroller, microprocessor, other electronic units designed to implement the functions described herein, or a combination thereof. For software implementation, part or all of the processes of the embodiments can be instructed by a computer program to relevant hardware. When implemented, the above program can be stored in a computer readable medium or transmitted as one or more instructions or codes on a computer readable medium. The computer readable medium includes computer storage medium and communication medium, wherein the communication medium includes any medium that facilitates the transmission of a computer program from one place to another. The storage medium can be any available medium that can be accessed by a computer. The computer readable medium can include but is not limited to RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer.

[0055] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, and are not limited to the scope of protection of the present application. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should analyze that the technical solutions of the present application can be modified or replaced by equivalents without departing from the essence and scope of the technical solutions of the present application.

Claims

1. A digital operation management and control platform based on shared service operation management, characterized in that, The system comprises a data acquisition module, a data management module and a resource allocation module, wherein The data acquisition module is configured to access heterogeneous data sources of different service scenarios, collect service data in real time under different service scenarios, and perform standardized processing on the collected service data to obtain standardized service data. The data management module is configured to build a shared database according to the standardized service data, and perform associated identification management on the service data in the shared database. The resource allocation module is configured to obtain a data call request initiated by a requestor based on a service scenario, respond to the data call request according to a dynamic resource allocation rule, obtain a priority level of the data call request, and allocate a corresponding processing node according to the priority level to call corresponding data and complete data processing, and return the data processing result to the requestor by the processing node. 2.The digital operation management and control platform based on shared service operation management of claim 1, wherein, The system further comprises a permission management module, wherein The permission management module is configured to verify the identity information of the requestor initiating the data call request, and obtain the data calling permission of the requestor based on the verified identity information. 3.The digital operation management and control platform based on shared service operation management of claim 1, wherein, The data acquisition module comprises a data access unit, a metadata extraction unit and a standardized processing unit, wherein The data access unit is configured to connect with the data interface of each service scenario to obtain the original data stream of each service scenario. The metadata extraction unit is configured to extract metadata according to the obtained original data stream to form a feature vector corresponding to the original data stream, wherein the extracted metadata includes data type, update frequency, single data volume and transmission delay. The standardized processing unit is configured to call the corresponding standardized processing standard to perform standardized processing on the original data stream according to the obtained feature vector to obtain the standardized service data.

4. The digital operation management and control platform based on shared service operation management according to claim 3, characterized in that, In the standardized preprocessing unit, the corresponding standardized processing standard is called to perform standardized processing on the original data stream, specifically including: According to the historical data quality of the data source to which the original data stream belongs, the quality weight of the current original data stream is determined, wherein the quality weight acquisition function used is: wherein, represents the quality weight of the i-th original data stream, represents the historical vacancy field proportion of the i-th original data stream, represents the historical receiving delay of the i-th original data stream, which is obtained according to the difference between the time stamp when the data is generated and the time stamp when the data is received; represents the set allowed maximum receiving delay, represents the historical data error rate of the i-th original data stream, which is obtained according to the proportion of the data amount exceeding the corresponding standard value range in the statistical historical original data stream, represents the set allowed maximum data error rate; , , respectively represent the set weight factors; According to the quality weight of the original data stream, when the quality weight exceeds the preset weight standard, only the current original data stream is subjected to format conversion processing to convert the original data stream to a preset standard format to obtain the standardized service data; otherwise, when the quality weight does not exceed the preset weight standard, the original data stream is further subjected to data cleaning processing of data difference and abnormal value exclusion, and then the cleaned data is subjected to format conversion to obtain the standardized service data.

5. The digital operation management and control platform based on shared service operation management according to claim 3, characterized in that, The data management module comprises a database unit and an association unit. The database unit is configured to build a shared database according to the standardized service data, and perform classified storage management on the standardized service data and the corresponding metadata. The association unit is configured to extract associated features according to the service data in the shared database, and perform associated identification on the service data associated with each other according to the extracted features.

6. The digital operation management and control platform based on shared service operation management according to claim 5, characterized in that, The resource allocation module comprises a request acquisition unit, an allocation response unit and a dynamic allocation unit, wherein The request acquisition unit is configured to obtain a data call request initiated by a requestor based on a service scenario, The request acquisition unit is configured to acquire a data call request initiated by a requester based on a service scenario, wherein the data call request comprises requester information, service scenario description, and request data range; The allocation response unit is configured to analyze the requester information, service scenario description, and request data range carried in the data call request to obtain a data call priority strength; The dynamic allocation unit is configured to allocate a corresponding level of data processing node to complete a corresponding data call task and further data processing task according to the obtained priority strength, and the data processing node acquires corresponding business data memory image from a shared database and returns it to the requester.

7. The digital operation management and control platform based on shared service operation management according to claim 6, characterized in that, The allocation response unit analyzes the requester information, service scenario description, and request data range carried in the data call request, specifically including: extracting the data transmission path delay between the current server and the requester according to the requester information; extracting the corresponding business scenario semantic features according to the business scenario description, and calculating the similarity between the semantic scenario business features and the standard features of each standard business scenario to obtain the standard business type corresponding to the data extraction request; locking the required business data range according to the obtained standard business type and request data range, and obtaining the corresponding data call chain information according to the required business data range to calculate the complexity of the data call chain; obtaining the data call priority strength of the current data call request: In the formula, represents the calling priority strength of the current data calling request k; represents the service level of the standard service type corresponding to the data calling request; represents the data transmission path delay of the request party; represents the delay sensitivity coefficient, represents the complexity of the data calling chain. 8.The digital operation management and control platform based on shared service operation management of claim 7, wherein, The dynamic allocation unit specifically includes: analyzing the data call priority strength of the data call request, when the data call priority strength is within a set first priority range, then allocating a first-level data processing node to respond to the data call request, so that the first-level data processing node can directly locate the shared database according to the data call request, directly acquire the mirror image of the required business data from the shared database, and transmit it to the requester; when the data call priority strength is within a set second priority range, then allocate a second-level data processing node to complete the response to the data call request, so that the second data processing node uses the load balancer to allocate an idle second-level data processing node to respond to the data call request, and completes the forwarding of the required data; when the data call priority strength is within a set third priority range, then allocate a third-level data processing node to complete the response to the data call request, so that the third data processing node asynchronously acquires the required data through edge processing, and then transmits the acquired data mirror to the requester.

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