CN117909693A - Electric digital data analysis processing device - Google Patents
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- CN117909693A CN117909693A CN202410305311.2A CN202410305311A CN117909693A CN 117909693 A CN117909693 A CN 117909693A CN 202410305311 A CN202410305311 A CN 202410305311A CN 117909693 A CN117909693 A CN 117909693A
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Abstract
The invention discloses an electric digital data analysis processing device, which particularly relates to the technical field of data analysis processing, and comprises an electric digital acquisition module, a data processing module, an intelligent early warning module, a virtualization server module and an operation and maintenance mobile terminal, wherein the electric digital acquisition module acquires various business data through various data acquisition channels and transmits the business data to the data processing module, the data processing module acquires target data through data filtering, the data filtering is processed through a moving average filtering algorithm and then transmits the processed target data to the intelligent early warning module, the intelligent early warning module analyzes the data through a principal component analysis method, detects abnormal data and performs fault early warning, the data filtering unit adopts the moving average filtering algorithm to ensure that the data is processed in real time to acquire effective sampling values, so that the latest data can be acquired in real time, and the acquired data is processed through a data conversion unit to acquire an early warning setting threshold value so as to lay an early warning foundation for the intelligent early warning module.
Description
Technical Field
The invention relates to the technical field of data analysis and processing, in particular to an electric digital data analysis and processing device.
Background
Along with the construction and development of smart power grids and the Internet of things, the power industry continuously accumulates massive data, and the characteristics of large data volume, multiple data types, low value density and the like are achieved, so that electric digital data acquire various business data sets through various data acquisition channels such as sensors, intelligent equipment and mobile terminals.
The electric digital data is characterized in that various data acquisition channels in the electric power system acquire data and transmit the data to a computer, the electric digital data analysis processing system is divided into a data acquisition layer, a calculation analysis layer and a service application layer after the computer data is calculated, the data acquisition layer is used for acquiring various data of the electric power industry by various data sources of various data, texts and external interfaces, the calculation analysis layer is used for carrying out data analysis based on an electric power algorithm and an analysis module, the service application layer is used for displaying the electric power running condition through multiple graphics, and mass data are continuously gushed out due to the rapid development of times and the continuous expansion of an electric network, so that the capability of computer calculation analysis is continuously reduced.
In the prior art, the electrical digital data analysis relates to data acquisition, information transmission and analysis, equipment fault investigation and diagnosis and the like, and the traditional data analysis software and hardware are difficult to rapidly operate and process due to large data acquisition information quantity, so that the visualization effect is poor, and the fault early warning reaction speed is low.
Disclosure of Invention
In order to overcome the above-mentioned drawbacks of the prior art, an embodiment of the present invention provides an electric digital data analysis processing device, which uses a moving average filtering algorithm to ensure that data is processed in real time to obtain an effective sampling value through a data filtering unit, is favorable to obtaining the latest data in real time, and uses a data conversion unit to obtain an early warning setting threshold value after processing the collected data so as to lay an early warning foundation for an intelligent early warning module, and uses a virtualized server module to store the data in a plurality of independent devices in a scattered manner, thereby eliminating repeated allocation of resources and improving the data processing speed and early warning efficiency, so as to solve the problems presented in the above-mentioned background art.
In order to achieve the above purpose, the present invention provides the following technical solutions: the utility model provides an electricity digital data analysis processing apparatus, includes electricity digital acquisition module, data processing module, intelligent early warning module, virtualization server module and fortune dimension mobile terminal, electricity digital acquisition module acquires various business data transmission to data processing module through various data acquisition channels, data processing module obtains target data through data filtering, data filtering adopts moving average filtering algorithm to handle the back and transmits to intelligent early warning module, intelligent early warning module carries out analysis, abnormal data detection and the trouble early warning to data through principal component analysis method, virtualization server module is used for dynamic feedback to terminate the realization and distributes multilayer virtualization central control service resource, fortune dimension mobile terminal shows data and accept the early warning.
In a preferred embodiment, the data processing module includes a data filtering unit and a data transforming unit, where the data filtering unit refers to a process of weakening or filtering random errors caused by interference data on multiple data in a digital filtering manner to obtain real data, and the data filtering is to use a moving average filtering algorithm to ensure that the data is processed in real time to obtain effective sampling values, that is, a plurality of sampling values are processed to obtain moving average, a plurality of data temporary storage areas are opened in the data processing, one data is collected newly each time and stored in the temporary storage areas, and meanwhile, one data at the head of the queue is flushed, and the latest data is always kept in the memory queue, so that the data is collected in real time, and the response speed of the system is improved.
In a preferred embodiment, the data transformation unit refers to the fact that a nonlinear relationship exists between the data obtained in each data acquisition module and the measured parameter, and the scale transformation is performed by an interpolation polynomial, wherein the interpolation polynomial refers to an n-degree polynomial instead of the nonlinear functional relationship, i.e. the measured parameter y has a functional relationship with the sensor output value xThe functional relationship at each different point is/>，/>，...，/>Constructing an n-degree polynomial as/>De-approximating function/>Will/>Values at n+1 outliers are substituted as interpolation into the polynomial of degree n/>Obtaining an equation set, then solving the equation set to obtain a numerical value, wherein x is an output value on the sensor, y is a measured parameter, and n+1 undetermined coefficients are obtained.
In a preferred embodiment, the intelligent early-warning module comprises a data analysis unit, a detection abnormality unit and an editable early-warning unit, the data analysis unit performs data mining analysis on data through a principal component analysis algorithm, judges the number of main components of faults, replaces a large number of related variables with a group of uncorrelated variables in electric digital mass data so as to simplify the complex analysis process, thereby realizing the purpose of data analysis, the detection abnormality unit detects abnormal data in a PCA mode, namely, maps the data in a low-dimensional feature space, checks the deviation of each data point and other data in the feature space through different dimensions, and obtains the variance of the data in the corresponding direction, so as to judge whether the fault problem exists, and the editable early-warning unit refers to adding early-warning functions in the intelligent early-warning module of the system, setting early-warning time, setting early-warning mobile phone numbers, setting early-warning rules, and reflecting more humanized setting early-warning rules.
In a preferred embodiment, the virtualization server module includes a smart detection knowledge base and a cloud computing unit, where the smart detection knowledge base refers to a distributed file storage database in a virtual environment, that is, data packets are stored in data sets, each data set has a unique identifier name, each identifier name includes a number of documents, a data storage location is quickly found by the identifier name, the distributed file storage database supports automatic failover, where when failover occurs, the data sets do not perform a write operation any more, and the automatic failover operation is as follows:
adapting Driver of mongodb4.2 will retry the write operation by default;
Driver adapted to mongdb 4.0-3.6 needs to be displayed when the connection string contains RETRYWRITES =true, so as to ensure that the write operation can be retried when the master node is out of connection;
A client configuration string connecting the duplicate sets, wherein rs0 is the duplicate set name REPLSETNAME set in the configuration file;
according to the method, data are stored in a plurality of independent devices in a scattered mode on the basis of a virtualized server, repeated configuration of resources is eliminated, the storage capacity of a database is enhanced, and meanwhile the database is easier to upgrade.
In a preferred embodiment, the cloud computing unit virtualizes the data resources based on the virtualization technology, and exchanges corresponding programs and data for different levels, so that the dynamic scheduling maximizes the utilization of the existing resources, and uses the KVM as a memory virtualization mode, and the Linux kernel uses the KVM as a loadable module, runs the Linux or Windows operating system on the KVM, and can perform data analysis calculation and resource management even in heterogeneous environments.
In a preferred embodiment, the operation and maintenance mobile terminal is an editable early warning unit based on an intelligent early warning module, and when the early warning rule is exceeded, the operation and maintenance mobile terminal receives an early warning signal through a mobile phone short message or an early warning applet, so that the electric number is specifically early warned, the operation and maintenance mobile terminal is reminded at any time, the time consumption of continuous attention of operation and maintenance personnel on data is reduced, and a manager can conveniently and rapidly make an adjustment scheme on the data.
In a preferred embodiment, the specific steps are as follows:
s1, firstly, an electric digital acquisition module acquires various business data through various data acquisition channels and transmits the business data to a data processing module;
S2, the data processing module obtains target data through data filtering, and the data filtering is processed through a moving average filtering algorithm and then is transmitted to the intelligent early warning module;
S3, the intelligent early warning module analyzes the data, detects abnormal data and sends out fault early warning through a principal component analysis method;
And S4, the final virtualization server module is used for dynamically feeding back and displaying the multi-layer virtualization service resources, and the operation and maintenance mobile terminal displays data and receives early warning.
The invention has the technical effects and advantages that:
The method ensures that the data is processed in real time to obtain the effective sampling value through the data filtering unit by adopting a moving average filtering algorithm, is favorable for obtaining the latest data in real time, and obtains the early warning set threshold value after the collected data is processed through the data conversion unit so as to lay an early warning foundation for the intelligent early warning module, and the data is stored in a plurality of independent devices in a scattered manner through the virtualized server module, so that the repeated allocation of resources is eliminated, and the data processing speed and the early warning efficiency are improved.
Drawings
Fig. 1 is a block diagram of a system architecture of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Examples
The embodiment provides a system of an electric digital data analysis processing device shown in fig. 1, which comprises an electric digital acquisition module, a data processing module, an intelligent early warning module, a virtualization server module and an operation and maintenance mobile terminal, wherein the electric digital acquisition module acquires various business data through various data acquisition channels and transmits the business data to the data processing module, the data processing module acquires target data through data filtering, the data filtering is processed by adopting a moving average filtering algorithm and then transmits the processed data to the intelligent early warning module, the intelligent early warning module analyzes the data, detects abnormal data and performs fault early warning through a principal component analysis method, and the virtualization server module is used for realizing the distribution of multi-layer virtualization central control service resources through dynamic feedback termination, and the operation and maintenance mobile terminal displays the data and receives early warning.
S1, the electric digital acquisition module acquires various business data through various data acquisition channels and transmits the business data to the data processing module;
the embodiment needs to specifically explain that the electric digital acquisition module comprises data acquired by vibration play detection, ultrasonic detection, ground electric wave detection and infrared temperature measurement detection, and is installed at each measuring position of the electric power tested equipment to acquire electric digital data in real time.
S2, the data processing module obtains target data through data filtering, and the data filtering is processed through a moving average filtering algorithm and then transmitted to the intelligent early warning module;
The embodiment needs to specifically explain that the data processing module comprises a data filtering unit and a data conversion unit, the data filtering unit is a process of weakening or filtering random errors caused by interference data on various data in a digital filtering mode to obtain real data, the data filtering is a process of adopting a moving average filtering algorithm to ensure that the data are processed in real time to obtain effective sampling values, namely a plurality of sampling values are processed to obtain moving average, a plurality of data temporary storage areas are opened in the data processing, one data is newly collected each time and is stored in the temporary storage areas, meanwhile, one data at the head of a user is flushed, the latest data is always kept in a memory queue, and then the data are collected in real time, so that the response speed of the system is improved.
The data transformation unit is used for transforming the scale of the measured parameter by interpolation polynomial, wherein the interpolation polynomial is used for replacing nonlinear function relation, namely, the measured parameter y and the sensor output value x have function relation ofThe functional relationship at each different point is/>，/>，...，/>Constructing an n-degree polynomial as/>De-approximating function/>Will/>Values at n+1 outliers are substituted as interpolation into the polynomial of degree n/>Obtaining an equation set, then solving the equation set to obtain a numerical value, wherein x is an output value on the sensor, y is a measured parameter, and n+1 undetermined coefficients are obtained.
S3, the intelligent early warning module analyzes the data, detects abnormal data and sends out fault early warning through a principal component analysis method;
The embodiment needs to specifically explain that the intelligent early warning module comprises a data analysis unit, a detection abnormality unit and an editable early warning unit, wherein the data analysis unit performs data mining analysis on data through a principal component analysis algorithm, judges the number of main components of faults, replaces a large number of related variables with a group of uncorrelated variables in electric digital massive data so as to simplify the complex analysis process, thereby realizing the purpose of data analysis.
And S4, the virtualization server module is used for dynamically feeding back and displaying the multi-layer virtualization service resources, and the operation and maintenance mobile terminal displays data and receives early warning.
The embodiment needs to specifically explain that the virtualized server module comprises an intelligent detection knowledge base and a cloud computing unit, wherein the intelligent detection knowledge base is used for storing data packets in a data set in a virtual environment by adopting a distributed file storage database, each data set is provided with a unique identification name, each identification name comprises a plurality of documents, a data storage position is quickly found through the identification names, the distributed file storage database supports automatic failover, and when the automatic failover occurs, the data set does not execute a writing operation any more, and the automatic failover operation is as follows:
adapting Driver of mongodb4.2 will retry the write operation by default;
Driver adapted to mongdb 4.0-3.6 needs to be displayed when the connection string contains RETRYWRITES =true, so as to ensure that the write operation can be retried when the master node is out of connection;
A client configuration string connecting the duplicate sets, wherein rs0 is the duplicate set name REPLSETNAME set in the configuration file;
The method disperses and stores data in a plurality of independent devices on the basis of a virtualized server, eliminates repeated configuration of resources, enhances the storage capacity of a database, simultaneously makes the database upgrade easier, and the embodiment is not particularly limited.
The cloud computing unit performs virtualization on data resources based on a virtualization technology, and exchanges corresponding programs and data in different levels, so that dynamic scheduling can maximally utilize existing resources, a KVM (kernel-based virtual machine) is used as a memory virtualization mode, a Linux kernel takes the KVM as a loadable module, and a Linux or Windows operating system runs on the KVM, so that data analysis, calculation and resource management can be performed even in a heterogeneous environment.
The embodiment needs to specifically explain that the operation and maintenance mobile terminal is an editable early warning unit based on the intelligent early warning module, when the early warning rule is exceeded, the operation and maintenance mobile terminal receives an early warning signal through a mobile phone short message or an early warning applet, so that the electricity number is specifically early warned, the operation and maintenance mobile terminal reminds all the time, the time consumption of continuous attention of operation and maintenance personnel on data is reduced, and a manager can conveniently and rapidly make an adjustment scheme on the data.
Finally: the foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (8)
1. An electric digital data analysis processing device is characterized in that: the intelligent early warning system comprises an electric digital acquisition module, a data processing module, an intelligent early warning module, a virtualization server module and an operation and maintenance mobile terminal, wherein the electric digital acquisition module acquires various business data through various data acquisition channels and transmits the business data to the data processing module, the data processing module acquires target data through data filtering, the data filtering is processed through a moving average filtering algorithm and then transmitted to the intelligent early warning module, the intelligent early warning module analyzes data, detects abnormal data and performs fault early warning through a principal component analysis method, the virtualization server module is used for realizing the distribution of multi-layer virtualization central control service resources through dynamic feedback termination, and the operation and maintenance mobile terminal displays the data and receives early warning.
2. An electrical digital data analysis processing apparatus according to claim 1, wherein: the data processing module comprises a data filtering unit and a data conversion unit, wherein the data filtering unit weakens or filters random errors caused by interference data in a digital filtering mode on various data to obtain real data, the data filtering is to adopt a moving average filtering algorithm to ensure that the data are processed in real time to obtain effective sampling values, namely a plurality of sampling values are subjected to moving average, a plurality of data temporary storage areas are opened in the data processing, each time one data is newly collected, the data are stored in the temporary storage areas, meanwhile, the first data are flushed, and the latest data are always kept in a memory queue for collecting the data in real time.
3. An electrical digital data analysis processing apparatus according to claim 1, wherein: the data transformation unit is used for carrying out scale transformation by interpolation polynomial which is used for replacing nonlinear functional relationship, namely, the non-linear functional relationship between the measured parameter and the measured parameter is that the measured parameter y has a functional relationship ofThe functional relationship at each different point is/>，，...，/>Constructing an n-degree polynomial as/>De-approximating function/>Will/>Values at n+1 outliers are substituted as interpolation into the polynomial of degree n/>Obtaining an equation set, then solving the equation set to obtain a numerical value, wherein x is an output value on the sensor, y is a measured parameter, and n+1 undetermined coefficients are obtained.
4. An electrical digital data analysis processing apparatus according to claim 1, wherein: the intelligent early warning module comprises a data analysis unit, a detection abnormality unit and an editable early warning unit, wherein the data analysis unit performs data mining analysis on data through a principal component analysis algorithm, judges the number of main components of faults, replaces a large number of related variables with a group of uncorrelated variables in electric digital mass data, the detection abnormality unit detects abnormal data in a PCA mode, namely, the data is mapped in a low-dimensional feature space, each data point is checked with other data deviations through different dimensions in the feature space, the variance of the data in the corresponding direction is obtained, so that whether the fault problem exists is judged, and the editable early warning unit is used for adding an early warning function in the intelligent early warning module of the system, setting early warning time, setting early warning mobile phone numbers and setting early warning rules, and reflecting more humanized setting early warning.
5. An electrical digital data analysis processing apparatus according to claim 1, wherein: the virtualization server module comprises an intelligent detection knowledge base and a cloud computing unit, wherein the intelligent detection knowledge base is a distributed file storage database in a virtual environment, namely data groups are stored in data sets, each data set is provided with a unique identification name, each identification name comprises a plurality of documents, a data storage position is quickly found through the identification names, the distributed file storage database supports automatic failover, when the failover occurs, the data sets do not execute write operations any more, and the automatic failover operation is as follows:
adapting Driver of mongodb4.2 will retry the write operation by default;
Driver adapted to mongdb 4.0-3.6 needs to be displayed when the connection string contains RETRYWRITES =true, so as to ensure that the write operation can be retried when the master node is out of connection;
A client configuration string connecting the duplicate sets, wherein rs0 is the duplicate set name REPLSETNAME set in the configuration file;
The method disperses and stores data in a plurality of independent devices on the basis of a virtualized server and eliminates repeated configuration of resources, wherein mongadb is/account is passward@mongodb0.example.com:27017, mongadb1. Example. Com:27017 and mongadb2. E.
6. An electrical digital data analysis processing apparatus according to claim 1, wherein: the cloud computing unit virtualizes data resources based on a virtualization technology, exchanges corresponding programs and data for different levels, enables dynamic scheduling to maximally utilize existing resources, utilizes KVM as a memory virtualization mode, and enables a Linux kernel to operate a Linux or Windows operating system on the KVM by taking the KVM as a loadable module, so that data analysis calculation and resource management can be performed even in a heterogeneous environment.
7. A method of an electrical digital data analysis processing apparatus as claimed in claim 1, wherein: the operation and maintenance mobile terminal is an editable early warning unit based on the intelligent early warning module, and receives early warning signals through mobile phone short messages or early warning small programs when the early warning rules are exceeded.
8. A method of operating an electrical digital data analysis processing apparatus according to any one of claims 1 to 7, wherein: the method comprises the following specific steps:
s1, firstly, an electric digital acquisition module acquires various business data through various data acquisition channels and transmits the business data to a data processing module;
S2, the data processing module obtains target data through data filtering, and the data filtering is processed through a moving average filtering algorithm and then is transmitted to the intelligent early warning module;
S3, the intelligent early warning module analyzes the data, detects abnormal data and sends out fault early warning through a principal component analysis method;
And S4, the final virtualization server module is used for dynamically feeding back and displaying the multi-layer virtualization service resources, and the operation and maintenance mobile terminal displays data and receives early warning.
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