Power distribution network operation situation multilayer data system compatible with front-end early warning and rear-end tracking

Abstract

The invention discloses a power distribution network operation situation multi-layer data system compatible with front-end early warning and rear-end tracking, and the data system is integrated with a power utilization information collection system of a power grid, a centralized service application system of the power grid and other necessary existing power grid data systems in advance. And constructing by taking power grid appeal risk datamation optimization, power application end attribute optimization and power grid bottom layer distributed port support as data orientation. Meanwhile, according to the structured data processing method deeply developed and constructed in the data system, matching construction of the data process is carried out according to the dynamism and the time-space characteristics of the power demand data and the data development situation of the power demand data in the later period of risk early warning and risk events, and a brand-new data processing flow and a data structure are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid data processing, and in particular to the development of a multi-layer data system for distribution network operation status compatible with front-end early warning and back-end tracking and a structured data processing algorithm thereof. Background Art

[0002] With the continuous improvement of power supply service process control methods, State Grid Hebei Electric Power has established service quality control methods for online channels such as 95598, and provided a three-level control mechanism for early warning for cities, districts, counties, and power supply stations. However, with the continuous improvement of service quality management requirements, the accuracy of data processing and analysis dimensions of traditional technical conditions is increasingly unable to meet current technical needs.

[0003] In this technical context, a power grid service situation monitoring and risk warning application project was launched. On the one hand, the overall architecture, application architecture, business and data architecture, and security deployment architecture of the application project were systematically developed and constructed. On the other hand, it is particularly important to build a comprehensive adaptive data algorithm on the constructed system architecture.

[0004] According to the preliminary investigation of the application project development, the data algorithms for the existing power grid service status data processing are still mainly dependent on traditional manual data processing and analysis methods. However, the reliance on manual methods makes it limited to processing small-scale and simple-structured power demand data sets, generally single-event power demand data. When faced with global, large-scale, complex and dynamically changing power demand data, the existing data processing methods are not only problems in terms of efficiency and accuracy, but also almost unfeasible.

[0005] Therefore, considering the serious inapplicability of existing manual data processing in the global processing of power demand data, on the basis of the overall architecture of the above-mentioned application project, further matching and constructing data processes according to the dynamic and spatiotemporal characteristics of power demand data, as well as the data development trends of power demand data in risk warning and post-risk events is the core issue of current application system development. Summary of the invention

[0006] The technical problem to be solved by the present invention is to build a corresponding data system architecture for power grid service status application development projects, and further match and construct data processes according to the dynamic and spatiotemporal characteristics of power demand data and the data development trends of power demand data in risk warning and post-risk event periods.

[0007] In order to solve the above technical problems, the technical solutions adopted by the present invention are as follows.

[0008] A multi-layer data system for distribution network operation status that is compatible with front-end early warning and back-end tracking. The data system is pre-integrated with the power grid electricity consumption information collection system, the power grid centralized business application system and other necessary existing power grid data systems, and is constructed with data-oriented optimization of power grid demand risk data, optimization of power application end attributes, and support for underlying distributed ports of the power grid.

[0009] As a preferred technical solution of the present invention, in terms of the overall architecture, the data layer contained in the data system may optionally include: optimization of power demand indicator monitoring, optimization of power risk warning applications, construction of a power user attribute library, construction of a power grid service strategy library, construction of risk management application scenarios, and construction and optimization of data-based and automatic analysis report applications.

[0010] As a preferred technical solution of the present invention, the data flow architecture of the data system is set as follows: the data flow architecture mainly includes two parts: basic support data flow and application data flow; Basic supporting data flow: Through the multi-dimensional data import port, 95598 call real-time data, marketing business application system data, electricity consumption collection system data, online State Grid data, 95598 business support system data, 12398 power supervision hotline and other data are aggregated to realize the storage, integration and pre-processing of various types of data.

[0011] Application data flow: mainly for the following data application ports: power demand indicator monitoring optimization, service risk warning application optimization, construction of power user attribute library, construction of service strategy library, risk management application scenario construction, data-based automated analysis report application optimization, and application expansion and application port expansion as needed.

[0012] As a preferred technical solution of the present invention, the application architecture of the data system is configured to include a platform layer, a data layer, an application layer, and an application object layer; The platform layer is constructed with the following data modules: platform capability management, platform application theme management, platform system management, and platform data management; achieving unified management of various applications, capabilities, and data of the system; The data layer is constructed with the following data modules: data acquisition, data cleaning, data conversion and processing, and the data objects it applies mainly include 95598 voice translation text data, work order data, power user basic file data and other data.

[0013] The application layer is constructed with the following data modules: power demand indicator monitoring optimization, service risk warning application optimization, construction of power user attribute library, construction of service strategy library, risk management application scenario construction, and data-based automated analysis report application optimization.

[0014] As a preferred technical solution of the present invention, the application object layer is constructed with the following data modules: front-line business personnel, management personnel, provincial company personnel, and municipal company personnel; and provides front-line employee views, management analysis views, provincial company views, external service views and other view capabilities for the above-mentioned data modules of different levels.

[0015] As a preferred technical solution of the present invention, the data architecture of the data system is set as: A. Use interfaces to obtain structured data and service risk warning mechanism data from multiple channels such as 12398, marketing service public opinion or other offline channels, as well as electricity consumption information collection systems. At the same time, build output channels that support the above data aggregation and analysis scenario applications and analysis statements / reports; B. In response to the shared data needs of third-party manufacturers and systems related to the power grid, the output data is reprocessed in accordance with the data permissions of the data requester to form result data provided to the external application system corresponding to the permissions of the data requester. These result data can be directly provided to the application display page or provided to the data demander through interface services.

[0016] As a preferred technical solution of the present invention, the technical architecture of the data system is set as: The system data is mainly based on 95598 voice translation text data, work orders of the power grid business application system, 95598 business support work orders, and power failure data of the power consumption information collection system. It is obtained and stored in the power grid situation monitoring and risk warning application system in the form of RESTAPI, and provided to data demanders and data analysis applications at different ports for use. The results of the analysis are simultaneously output as reports / reports through the data communication protocol; among them: Data analysis list generation: query work order data and call text data on an hourly basis to generate a data analysis list; Channel data analysis and text data analysis perform data analysis based on the acquired data and summarize and generate data analysis results; Applications such as optimization of power user demand indicator monitoring, optimization of power grid service risk warning applications, construction of power user attribute library, construction of service strategy library, construction of risk management application scenarios, and optimization of analysis report applications each call on data analysis results to display analysis graphs and reports.

[0017] As a preferred technical solution of the present invention, the security architecture of the data system is set as follows: The boundary of the power grid information area is taken as the dominant boundary involved in application deployment. On this basis, the existing logical strong isolation, hardware firewall, IDS / IPS and other boundary security protection equipment of the power grid system are utilized for the boundary of the information intranet; for the outside of the boundary, security policies such as identity authentication, access control, intrusion detection, logging and security audit are configured to achieve boundary isolation and security protection.

[0018] As a preferred technical solution of the present invention, the deployment architecture of the data system is configured to perform application and data deployment based on provincial information regions.

[0019] The structured data processing method within the distribution network operation status multi-layer data system matches and constructs the data process according to the dynamic and spatiotemporal characteristics of the power demand data, as well as the data development trend of the power demand data in risk warning and post-risk event, and has a brand-new data processing flow and data structure.

[0020] The beneficial effects of adopting the above technical solution are: The present invention has developed and constructed a multi-layer data system for the operation status of the distribution network, which can analyze and process the index data of power users' demands in a digital way, and can control the checkpoints in advance for the potential risks that still exist in the demands of power users. Relying on the 95598 demand big data application and frequency curve, it can realize the monitoring and disposal of various demand changes, locate the key areas, personnel, and problems of service hazards, and carry out layered supervision and tiered control. While doing a good job in demand analysis and monitoring, it can also expand the breadth and depth of business type analysis, realize the terminalization of indicator analysis business, the terminalization of problem distribution, and the terminalization of demand occurrence time, and analyze and monitor various risk issues. At the same time, the present invention has developed and constructed a multi-layer data system for the operation status of the distribution network, which can improve the omni-channel power demand control from the perspective of power users, optimize the means of problem tracing control, realize the clustering and tracing analysis of various demands of power users, and support the directional closed loop at all levels.

[0021] A core development item of the present invention is to construct a structured processing method for power grid demands and situation data corresponding to the overall architecture of the application project. This development has deep data connotations and has been deeply developed through the joint efforts of multiple scientific research teams. The final technical route is to achieve global characterization and multi-application-oriented processing of power demand data by constructing a data link structure and a data situation model. In the construction of the front-end data link structure, the power demand data is associated with the risk factors of the power grid operation situation to identify and predict the associated power demand data and power grid risk factors; the back-end data situation model construction constructs a data situation model framework through global and dynamic structural characterization and algorithm processing of the power demand data group. This framework can not only analyze the processing and evolution of power demands, but also construct multiple different power demand data application orientations based on the spatial expansion and temporal dynamic characteristics of the demand data. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 Schematic diagram of the overall architecture of the data system of the present invention.

[0023] Figure 2 Schematic diagram of the data flow architecture of the data system of the present invention.

[0024] Figure 3 Schematic diagram of the application architecture of the data system of the present invention.

[0025] Figure 4 Schematic diagram of the data architecture of the data system of the present invention.

[0026] Figure 5 Schematic diagram of the technical architecture of the data system of the present invention.

[0027] Figure 6 Schematic diagram of the security architecture of the data system of the present invention.

[0028] Figure 7 Schematic diagram of the deployment architecture of the data system of the present invention. DETAILED DESCRIPTION

[0029] The following examples describe the present invention in detail. In the description of the following examples, specific details such as specific system structures, technologies, etc. are proposed for illustration rather than limitation, so as to provide a thorough understanding of the present application embodiments. However, it should be clear to those skilled in the art that the present application can also be implemented in other embodiments without these specific details. In other cases, the detailed description of well-known systems, devices, circuits and methods is omitted to prevent unnecessary details from hindering the description of the present application.

[0030] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0031] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0032] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0033] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0034] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0035] Example 1: Power Grid Situation Monitoring and Risk Warning Application Project At present, the power grid system has preliminarily completed the construction of the information management and control platform for power users, and based on this platform, has preliminarily realized the real-time acquisition of call data of provincial 95598 power users, classification and automatic identification of power user demands, assessment of power user service risk index, monitoring of service management indicators, analysis of marketing event demands, application of service risk warning, monitoring of service management quality indicators and other dimensions of power user service quality management. Relying on this platform, through the access to the marketing business application system, business expansion registration system and 95598 call real-time data (less than 30 minutes), the real-time analysis of the calls and demands of power users of each local company has been preliminarily realized, and the analysis results have been promoted and applied to companies in cities, districts and counties across the province. At the same time, by constructing a risk theme model, the risk problems contained in the dispatched opinion work order have been preliminarily realized, and the three levels of red, orange and yellow have been set by constructing a three-level risk warning method.

[0036] The State Grid Hebei service situation monitoring and risk warning application project was carried out under this background.

[0037] The benefit analysis of this project construction is as follows: From the perspective of management benefits, as the company continues to strengthen the control of power supply service quality, the number of complaints has decreased year by year, but from the perspective of power users' demands, potential service risks still exist, and typical problems and red line problems in various units still occur from time to time; by conducting power user demand analysis, service early warning and process quality control, we can timely grasp the current main demands of power users and possible service risks; through the construction of the power user attribute library and service strategy library, combined with historical problem solutions, we can push accurate service handling methods to front-line business personnel, provide decision-making support for operators, effectively tilt resources, optimize work scheduling, improve work accuracy, realize early detection, early warning and fast processing of service risks, and effectively improve the quality of power supply services. From the perspective of economic benefits, by monitoring the service status of each city company and building risk warning scenarios, operation managers can timely discover the current main demands of power users, main service risk points, and differences in quality and efficiency of personnel and things, and through corresponding guidance strategies, concentrate on efficient solutions, improve work efficiency, and achieve optimal resource allocation. At the same time, through the automatic screening of repeated demands and the modular automatic generation of daily analysis reports, the input of business analysts and the secondary risks caused by subjective judgment of personnel can be reduced, and the operating costs of enterprises can be reduced. From the perspective of social benefits, service risk prevention and control and quality management work should be carried out, and massive unstructured data resources should be fully utilized. Combined with risk warning and disposal models, possible service risk escalation events can be predicted, and effective public relations measures can be taken in advance to notify or appease power users, so as to achieve pre-emptive prevention, reduce the outbreak of large-scale group events, improve service levels, establish a good social image of the company, and continuously improve the perception of power users and the experience of power users. By analyzing power supply quality issues, the company can understand the operation of the distribution network from the perspective of the headquarters, and provide data reference for the provincial company to do a good job in power grid construction and transformation, strengthen operation and maintenance management, and improve power supply controllability.

[0038] Example 2: Architecture of power grid situation monitoring and risk warning application In general, this development project constructs a multi-layer data system for the operation status of the distribution network, which is pre-integrated with the power grid electricity consumption information collection system, the power grid centralized business application system and other necessary existing power grid data systems, and is constructed with the data-oriented optimization of power grid demand risk data, optimization of power application end attributes, and support for the underlying distributed ports of the power grid.

[0039] See attached Figure 1 In terms of the overall architecture, the data layer of the power grid situation monitoring and risk warning application data system can optionally include: power demand indicator monitoring optimization, power risk warning application optimization, construction of power user attribute library, construction of power grid service strategy library, risk management application scenario construction, data and automatic analysis report application construction and optimization.

[0040] See attached Figure 2 , the data flow architecture of the power grid situation monitoring and risk warning application data system is given, which mainly includes two parts: basic support data flow and application data flow.

[0041] See attached Figure 3 , the application architecture of the data system is set up to include platform layer, data layer, application layer, and application object layer; the platform layer is constructed with the following data modules: platform capability management, platform application theme management, platform system management, and platform data management; to achieve unified management of various applications, capabilities, and data of the system; the data layer is constructed with the following data modules: data acquisition, data cleaning, data conversion and processing, and its applied data objects mainly include 95598 voice translation text data, work order data, power user basic archive data and other data. The application layer is constructed with the following data modules: power demand indicator monitoring optimization, service risk warning application optimization, construction of power user attribute library, construction of service strategy library, risk management application scenario construction, and data-based automated analysis report application optimization. The application object layer is constructed with the following data modules: front-line business personnel, management personnel, provincial company personnel, and municipal company personnel; for the above-mentioned data modules at different levels, it provides front-line employee views, management analysis views, provincial company views, external service views and other view capabilities.

[0042] See attached Figure 4 , and gives the data architecture of the power grid situation monitoring and risk warning application data system. It uses interfaces to obtain structured data and service risk warning mechanism data from multiple channels such as 12398, marketing service public opinion or other offline channels, and electricity consumption information collection systems. At the same time, it builds output channels that support the above data aggregation and analysis scenario applications and analysis statements / reports; at the same time, in response to the shared data needs of third-party manufacturers and systems related to the power grid, the output data is processed secondary according to the data permissions of the data requester to form result data provided to the external application system corresponding to the permissions of the data requester. These result data can be directly provided to the application display page or provided to the data demander through interface services.

[0043] See attached Figure 5, the technical architecture of the data system is set as follows: the system data is mainly based on 95598 voice translation text data, grid business application system work orders, 95598 business support work orders, and power consumption information collection system power failure data. It is obtained and stored in the grid situation monitoring and risk warning application system through RESTAPI, and provided to data demanders and data analysis applications at different ports for use. The results of the analysis are simultaneously output as reports / reports through the data communication protocol. The data analysis list is generated by querying the work order data and call text data at an hourly frequency to generate a data analysis list; channel data analysis and text data analysis are performed based on the acquired data, and the data analysis results are summarized and generated; the power user demand indicator monitoring optimization, grid service risk warning application optimization, construction of power user attribute library, construction of service strategy library, risk management application scenario construction, analysis report application optimization and other applications call the data analysis results to display analysis graphics and reports.

[0044] See attached Figure 6-7 The security and deployment architecture is set up to deploy applications and data based on provincial information zones.

[0045] Embodiment 3: Structured data processing method for distribution network operation status data system The development of the power grid service situation monitoring and risk warning application project, on the one hand, systematically develops and builds the overall architecture, application architecture, business and data architecture, and security deployment architecture of the application project. On the other hand, it is particularly important to build a comprehensive adaptive data algorithm on the constructed system architecture. According to the preliminary investigation of the application project development, the data algorithms for the existing power grid service situation data processing currently mainly rely on traditional manual data processing and analysis methods, and the reliance on manual methods makes it limited to processing small-scale and simple-structured power demand data sets, generally single-event power demand data. When faced with global, large-scale, complex and dynamically changing power demand data, the data processing methods of existing technologies are not only problems in efficiency and accuracy, but also almost unfeasible.

[0046] Therefore, considering the serious inapplicability of existing manual data processing in the global processing of power demand data, on the basis of the overall architecture of the above-mentioned application project, further matching and constructing data processes according to the dynamic and spatiotemporal characteristics of power demand data, as well as the data development trends of power demand data in risk warning and post-risk events is the core issue of current application system development.

[0047] Specifically, a team of experts from inside and outside the joint power grid has deeply developed and constructed a structured data processing method within the multi-layer data system of the distribution network operation status. This structured data processing method matches and constructs the data process according to the dynamic and spatiotemporal characteristics of power demand data, as well as the data development trends of power demand data in risk warning and post-risk event periods. It has a brand-new data processing flow and data structure.

[0048] The technical development results in this area are introduced in detail as follows.

[0049] (1) Front-end structured processing of power demand data The front-end structured processing of power demand data is guided by the prior data processing of the distribution network operation status. Based on the construction of the data link structure, the power demand data is associated with the risk factors of the power grid operation status. Its application orientation is mainly based on the prior data risk warning. Its construction path is: through the equal position exchange processing of power demand data and 95598 audio data power demand type on the data model, the power grid operation risk factor data link structure extracted and marked based on the 95598 audio data power demand type is transferred to the data association structure of power demand data and power grid operation status risk factors. The specific data structuring process is detailed as follows.

[0050] At the bottom layer of the data structure, a data entity set is constructed in the form of a data set, each of which represents a data entity; the data set E is initialized to include several independent data entities: power demand data, power grid operation risk factor data; and other subsequently extensible data entities, such as power grid operation status data; each data entity is directly and consistently characterized by its attribute set, that is, in the data structure, the data entity itself and the attribute of the data entity are directly treated as equivalent; for each attribute in the attribute set, the underlying data set E based on the data model is initialized to include power demand data, power grid operation status data, power grid operation risk factor data and allows subsequent expansion of other data entity types, then the attribute is initialized to be compatible with the widest range of data object data types, including basic data types such as integers, strings, etc. or text data and other unstructured data. On the basis of data entity accommodation, the data set further establishes its multi-directional data relationship link based on the data relationship between the attribute sets of each data entity in principle; in particular, with respect to the data structure of the data relationship, the data relationship link within the data set is constructed as a core-guided type of multi-directional link, that is, the data link initially takes a selected data entity as the core, based on the data relationship between the attribute sets of each data entity, and combined with the data standard for measuring the data relationship between the attribute sets set in advance or manually specified, as the data guide of the data relationship link, the selected core data entity is guided to be linked with one or more other data entities. Among them, the guidance of the data link relationship takes the subset in the attribute set as the initialization data processing object, that is, the data processing relationship and its data processing process are constructed based on the selected subset; among them, the simplest and most practical data selection is: taking a single subset of the attribute set as the initialization data processing object guided by the data link relationship. Here, different types of power demands are directly adopted as a single subset, and used as the initialization data object guided by the data link relationship in the data process.

[0051] The specific data steps are as follows: first, the attribute set of the data entity "power demand data" is set to at least include: different power demand types (unstructured text demand data or structured vector data after text vectorization); the attribute set of the power grid operation risk factor data is constructed as a two-dimensional array structure, firstly, the data is expanded along one dimension according to the type of power grid risk event, and at the same time, each data column of the first data dimension is expanded in the second dimension according to the risk level of power grid operation, thereby obtaining the two-dimensional data attribute set of the power grid operation risk factor data entity; when the risk event type and the risk level are numerically equivalent, this two-dimensional array structure is correspondingly represented as a second-order tensor; as for the attribute set or other attributes of the power grid operation status data, it can include power grid electrical and signal data, as well as power grid operation and maintenance related data. At this time, a single subset of the attribute set, namely "power demand type", is used as the initialization data processing object guided by the data link relationship, = {power demand type}; for example, the link relationship between the power demand type and the power grid operation risk factor data is correspondingly represented as: which power demand types mean which type of power grid risk factors will appear or are likely to appear.

[0052] At this time, the data link relationship is designed as follows: Step 1, all possible power demand types are constructed into a non-repetitive unified data group, which is mathematically reflected as a data set. Step 2, there are two specific implementation possibilities: A, for any selected or subsequently added target power grid risk monitoring element (whether for a specific power user individual or a combined power user group), a power demand type subset group containing all possible power demand types is first generated for it by means of combinatorial traversal, and then based on the fitting and proofreading of artificial standards or historical data, one or several groups of the most effective power demand type combinations are selected from all power demand type subsets generated in the combinatorial traversal mode and all possible power demand type combinations therein, as the optimal power demand type combination of the selected target power grid risk monitoring element; at the same time, the optimal power demand type combination obtained in this way allows dynamic adjustment according to subsequent application effectiveness or other factors; B, compared with the data path in A above, an equivalent data process is adopted, but all power demand type subsets obtained in A are further ordered, that is, each power demand type subset in the power demand type subset group generates n! according to the order of the power demand type within the set! a new subset (where n is the number of power demand types within the specified power demand type subset); in this way, a parameter path for the order in which power demand types appear is constructed on the data model, that is, a parameter associated with the order in which power demand types appear in a specific power grid risk event or in a power demand data stream; wherein, the fitting and proofreading based on artificial standards or historical data selects one or several groups of the most effective power demand type combinations from all power demand type subsets generated under the combination traversal mode and all possible power demand type combinations therein as the optimal power demand type combination for the selected target power grid risk monitoring element; generally, this data step is implemented by constructing a set of digitized and programmed data fitting and verification systems, which are compared and proofread based on manual analysis and historical data or expert knowledge bases; this model also has an outstanding additional advantage, that is, it can very conveniently introduce existing artificial intelligence algorithms for iterative fitting optimization, which can greatly improve the efficiency and accuracy of fitting and proofreading compared to the original data optimization based on application feedback.Step 3: The specific implementation path of this step is to convert the data processes in steps 1 and 2 into data relations and data storage methods in the data system. Specifically, in the final data model, various optimized power demand type combinations are not actually stored for various power grid operation risk factor data, etc., but only a full set of power demand type combinations, that is, the data set in step 1, and the combination parameters corresponding to the best power demand type combination finally selected, recorded as A parameters, or combination parameters + ordered parameters, recorded as A + B parameters; at the same time, such A parameters or A + B parameters are constructed and stored in the form of instructions to optimize and simplify the data capacity of the data system; in subsequent risk warnings or other data analysis applications, the back-end server directly calls the instructions corresponding to the A parameters or A + B parameters, which may appear in the form of text of just a few bytes and have good calling and transmission efficiency, while the restoration and display of the real power demand type combination relies on the back-end server for calculation and generation, thereby avoiding the consumption of computing resources of the current data model and optimizing the energy efficiency of the constructed data system in terms of computing power model consumption. Among them, each power demand type subset in the power demand type subset group generates n! new subsets according to the order of power demand types within the set. On the one hand, when these ordered subsets are linked to the power grid risk events or 95598 audio data streams for data point links, their orderliness is associated with the time sequence of the internal elements of these ordered subsets in the power grid risk events or 95598 audio data streams, and the orderliness association is constructed on the time dimension; therefore, implementation path b is an obviously higher-order and more refined data model than implementation path a. Based on the accumulation of big data and the improvement of cloud server analysis computing power, the data correspondence structure between the order of appearance of power demand types in the power demand data stream and the correspondence of risk events has strong certainty and distinction. Therefore, the data model concretized under implementation path B has higher data sophistication and accuracy; on the other hand, implementation path B can further introduce more variable parameters in the time correlation of the internal elements of its ordered subsets, As described in the first aspect above, the default ordered association of the time dimension is based on the chronological order of the appearance of the power demand types. However, this chronological order may not be adopted in the data structure. Instead, it is allowed to set any random order based on the effectiveness of data analysis or the use of data models, as long as the appearance order of the power demand types finally selected and entered in the time dimension has a definite order.

[0053] (2) Back-end structured processing of power demand data The back-end structured processing of power demand data is oriented to the construction of dynamic data spatiotemporal situation model, which is guided by the post-data processing of the distribution network operation situation. For the known power demand data group, including the linked and associated power grid operation risk events that have been recorded or concerned, a data situation model framework is constructed to perform global and dynamic structural representation and algorithm processing on the power demand data group. The construction of this data situation model framework takes the processing and evolution of power demand as its application-oriented anchor. The specific data process is as follows.

[0054] The first aspect is to establish a dynamic data space for integrating and representing power demands. Considering that the final data space is associated with the division and dynamic changes of power demand data in different geographical areas and at different time points in the power grid, the constructed data space is recorded as the data space-time model m (of power demand data). Then, the data set k representing power demands is constructed as a vector in the data space-time model m, which is recorded as the power demand data vector s. By analyzing and processing the vector s, the spatial correlation variation and the temporal dynamic evolution characteristics of the power demand data are analyzed and processed. Overall, it can be recorded as an mks data architecture. In the mks data architecture, the source of electricity user data is generally from electricity user service records, social media, user surveys, etc. In the initial framework, the user demands recorded on the 95598 electronic work order are the main ones; the core, regarding the construction path of the mks data architecture, we can first merge the electricity demands from the same region into a data set k. Then, in the data space-time model m, the data set k can be constructed as a vector s in the data space-time model m in the following way: ① First, the dimension of the data space-time model M is defined by the data set k. At this time, the types of electricity demands in the data set k are directly mapped to the number of dimensions of the data space-time model m. Here, more dimensions can be set as redundant backup as needed, so that the data space-time model m is compatible with the overall global vector representation of the data set k in terms of data capacity; ② Next, it is necessary to specify the position of the electricity demand data vector s in the data space. ; ②-①, here it is specifically defined through the following data relationship: the projection distance of vector s in each dimension, that is, the distance between the end point of vector s and the center zero point of the data space-time model M, is characterized by the number of repetitions of different types of power demands in the data set k; ②-②, in addition to this, other types of projection distance characterization algorithms can also be constructed, for example: based on the intensity data of power demands, time urgency data, time distance data to the deadline for demand processing, or other data types that characterize different aspects of power demands for projection distance characterization, thereby forming a variety of different variant data space models; under the above-given distance algorithm based on the number of repetitions of the same demand data type, the projection distance of the power demand data vector s in any dimension of the data space-time model m is proportional to the number of user demands of this type; thus, the basic data space model is obtained, that is, the data space-time model m used to characterize power demands.

[0055] The second aspect is that for the data spatiotemporal model, the first aspect is in space. Since the data set and vector are derived from the power demand data in a certain area, different areas of the distribution network will generate different power demand data sets and corresponding different vectors as a whole. In this way, under the framework of the data spatiotemporal model, a vector space of power demand data with a certain density is constructed corresponding to the geographical distribution of the entire power grid. This vector space constructs a global data structure and represents the data essence for the power demand data of the entire power grid system. It should also be noted that in the construction of the power demand data vector space or in its subsequent application, regarding the division of power demand data in different areas of the distribution network, the areas can be adjacent surrounding areas, or selected discrete distribution areas based on certain standards such as the similarity of power consumption types, the high and low attributes of power load, the difference in power peaks and valleys, or other standards.

[0056] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0057] In various embodiments, the hardware implementation of the technology can directly adopt existing intelligent devices, including but not limited to industrial computers, PCs, smart phones, handheld stand-alone computers, floor-standing stand-alone computers, etc. Its input device preferably adopts a screen keyboard, its data storage and calculation module adopts existing memory, calculator, controller, its internal communication module adopts existing communication ports and protocols, and its remote communication adopts existing GPRS network, World Wide Internet, etc.

[0058] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0059] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms. The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0060] Each functional unit in each embodiment of the present invention may be integrated into a processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of a software functional unit. If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it may be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and may also instruct the relevant hardware to complete it through a computer program. The computer program may be stored in a computer-readable storage medium, and the computer program may be executed by the processor to implement the steps of the above-mentioned method embodiments. Among them, the computer program includes computer program code, and the computer program code may be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice within the jurisdiction.

[0061] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A multi-layer data system for distribution network operation status compatible with front-end early warning and back-end tracking, characterized by: The data system is pre-integrated with the power grid electricity consumption information collection system, the power grid centralized business application system and other necessary existing power grid data systems, and is constructed with data-oriented optimization of power grid demand risks, optimization of power application end attributes, and support for underlying distributed ports of the power grid.

2. A multi-layer data system for distribution network operation status compatible with front-end early warning and back-end tracking according to claim 1, characterized in that: In terms of the overall architecture, the data layer contained in this data system may optionally include: optimization of power demand indicator monitoring, optimization of power risk warning applications, construction of customer attribute library, construction of power grid service strategy library, construction of risk management application scenarios, and construction and optimization of data-based and automatic analysis report applications.

3. According to claim 1, a multi-layer data system for distribution network operation status compatible with front-end early warning and back-end tracking, characterized in that: The data flow architecture of the data system is set as follows: the data flow architecture mainly includes two parts: basic support data flow and application data flow; Basic support data flow: Through the multi-dimensional data import port, it aggregates 95598 call real-time data, marketing business application system data, electricity consumption collection system data, online state grid data, 95598 business support system data, 12398 power supervision hotline and other data to achieve storage, integration and pre-processing of various data; Application data flow: mainly for the following data application ports: power demand indicator monitoring optimization, service risk warning application optimization, construction of customer attribute library, construction of service strategy library, risk management application scenario construction, data-based automated analysis report application optimization, and application expansion and application port expansion as needed.

4. According to claim 1, a multi-layer data system for distribution network operation status compatible with front-end early warning and back-end tracking, characterized in that: The application architecture of the data system is configured to include a platform layer, a data layer, an application layer, and an application object layer; The platform layer is constructed with the following data modules: platform capability management, platform application theme management, platform system management, and platform data management; achieving unified management of various applications, capabilities, and data of the system; The data layer is constructed with the following data modules: data acquisition, data cleaning, data conversion and processing, and the data objects applied mainly include 95598 voice translation text data, work order data, customer basic file data and other data; The application layer is constructed with the following data modules: power demand indicator monitoring optimization, service risk warning application optimization, construction of power user attribute library, construction of service strategy library, risk management application scenario construction, and data-based automated analysis report application optimization.

5. According to claim 1, a multi-layer data system for distribution network operation status compatible with front-end early warning and back-end tracking, characterized in that: The application object layer is constructed with the following data modules: front-line business personnel, management personnel, provincial company personnel, and municipal company personnel; and provides view capabilities such as front-line employee views, management analysis views, provincial company views, and external service views for the above-mentioned data modules at different levels.

6. According to claim 1, a multi-layer data system for distribution network operation status compatible with front-end early warning and back-end tracking, characterized in that: The data architecture of this data system is set as: A. Use interfaces to obtain structured data and service risk warning mechanism data from multiple channels such as 12398, marketing service public opinion or other offline channels, as well as electricity consumption information collection systems. At the same time, build output channels that support the above data aggregation and analysis scenario applications and analysis statements / reports; B. In response to the shared data needs of third-party manufacturers and systems related to the power grid, the output data is reprocessed in accordance with the data permissions of the data requester to form result data provided to the external application system corresponding to the permissions of the data requester. These result data can be directly provided to the application display page or provided to the data demander through interface services.

7. A multi-layer data system for distribution network operation status compatible with front-end early warning and back-end tracking according to claim 1, characterized in that: The technical architecture of the data system is set up as follows: The system data is mainly based on 95598 voice translation text data, work orders of the power grid business application system, 95598 business support work orders, and power failure data of the power consumption information collection system. It is obtained and stored in the power grid situation monitoring and risk warning application system in the form of RESTAPI, and provided to data demanders and data analysis applications at different ports for use. The results of the analysis are simultaneously output as reports / reports through the data communication protocol; among them: Data analysis list generation: query work order data and call text data on an hourly basis to generate a data analysis list; Channel data analysis and text data analysis perform data analysis based on the acquired data and summarize and generate data analysis results; Applications such as optimization of power user demand indicator monitoring, optimization of power grid service risk warning applications, construction of customer attribute library, construction of service strategy library, construction of risk management application scenarios, and optimization of analysis report applications each call on data analysis results to display analysis graphs and reports.

8. According to claim 1, a multi-layer data system for distribution network operation status compatible with front-end early warning and back-end tracking, characterized in that: The security architecture of this data system is set up as follows: The boundary of the power grid information area is taken as the dominant boundary involved in application deployment. On this basis, the existing logical strong isolation, hardware firewall, IDS / IPS and other boundary security protection equipment of the power grid system are utilized for the boundary of the information intranet; for the outside of the boundary, security policies such as identity authentication, access control, intrusion detection, logging and security audit are configured to achieve boundary isolation and security protection.

9. A multi-layer data system for distribution network operation status compatible with front-end early warning and back-end tracking according to claim 1, characterized in that: The deployment architecture of the data system is set up to deploy applications and data based on provincial information regions.

10. The structured data processing method within the distribution network operation status multi-layer data system according to any one of claims 1 to 9, characterized in that: This structured data processing method matches and constructs the data process according to the dynamic and spatiotemporal characteristics of power demand data, as well as the data development trend of power demand data in risk warning and post-risk event, and has a brand-new data processing flow and data structure.

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