CN117933909A - Distributed cloud edge collaborative task management system and method - Google Patents

Abstract

The invention relates to the technical field of power systems, and discloses a distributed cloud edge collaborative task management system, which comprises: and the central processing unit provides a distributed storage service in a cloud platform resource-as-service mode, wherein the distributed storage service is used for key data storage of basic configuration data and integrated data real-time data, historical data, alarm information and auxiliary equipment information. According to the distributed cloud edge collaborative task management system and the distributed cloud edge collaborative task management method, the network transmission delay is reduced, so that emergency and time-sensitive tasks can be successfully completed; and a load prediction mode is adopted to predict the system load condition at the next moment, so that the resource utilization efficiency is improved.

Description

Distributed cloud edge collaborative task management system and method

Technical Field

The invention relates to the technical field of power systems, in particular to a distributed cloud edge collaborative task management system and method.

Background

Firstly, the indexes of the modern industry, especially the high and new technology industry, such as voltage value, frequency, ripple coefficient, power supply reliability and the like of the electric energy are becoming stricter, and the electric power enterprises must meet the requirement; secondly, the device also becomes a technical support which is essential for the safe and efficient operation of the power enterprises; finally, the unattended transformer substation is beneficial to the efficiency improvement of the power enterprises and the alleviation of the difficult problem of the construction land of the transformer substation.

The time requirement and the emergency degree of the transformer substation for cutting off different types of faults are also different, all data are uploaded to the cloud for analysis and processing in the traditional mode, and then the emergency faults can not be cut off in time possibly due to reasons such as communication congestion in the mode of issuing results, so that potential safety hazards are brought to the transformer substation.

Disclosure of Invention

(One) solving the technical problems

Aiming at the defects of the prior art, the invention provides a distributed cloud edge collaborative task management system and a distributed cloud edge collaborative task management method.

(II) technical scheme

In order to achieve the above purpose, the present invention provides the following technical solutions:

first aspect:

A distributed cloud-edge collaborative task management system, comprising:

the central processing unit provides distributed storage service in a cloud platform resource-as-a-service mode, wherein the distributed storage service is used for key data storage of basic configuration data and integrated data real-time data, historical data, alarm information and auxiliary equipment information;

the data acquisition module is used for collecting data generated by data acquisition equipment of the transformer substation;

the data analysis module is used for performing preliminary classification processing on the acquired data;

The load prediction module is used for selecting a training set and a testing set according to the association degree by adopting a load prediction method based on similar days and taking the altitude of a transformer substation, the voltage level of the transformer substation, the average daily air temperature, the humidity and the weather type as similar daily comment judgment bases; a long-period memory network algorithm is adopted, the service time and the fault rate of electrical equipment in the transformer substation are combined, the data scale acquired by the data acquisition module is used as an adjustment reference, and the load condition at the next moment is predicted;

the edge computing module is used for preprocessing the operation data, analyzing and processing the data according to the computing tasks distributed by the collaborative strategy module based on the preprocessed operation data, matching corresponding control instructions according to analysis and processing results, and controlling the operation of the equipment according to the obtained control instructions by the execution module.

Preferably, the method further comprises: the task scheduling module distributes the fault detection task to the cloud or edge side computing nodes according to the current needed resources and the time urgency degree of the task and the matching edge computing module; the resource monitoring module comprises a resource monitor and a monitoring alarm, wherein the resource monitor monitors the resource states of cloud computing nodes and edge computing nodes in the system; when the computing node is abnormal and cannot execute the current task, the monitoring alarm synchronously gives an alarm, and the task scheduling module redistributes the task which cannot be executed currently to other nodes; the fault detection module further analyzes the data by utilizing the computing node resources to obtain specific fault content and fault occurrence points, and analyzes possible fault reasons and fault treatment measures for reference of operation and maintenance personnel; the fault alarm module gives an alarm to operation and maintenance personnel after the fault occurs and sends fault analysis data to the cloud backup to serve as historical data for post fault analysis and pre-prediction at the next moment.

Preferably, the operation data of the device includes operation data obtained by reading in real time according to the set measuring points.

Preferably, the preprocessing the operation data includes:

Cleaning the blank value, format content, logic error and non-required operation data;

performing feature construction, data classification and data quantization on the operation data;

carrying out data statistics on the transformed operation data, and merging the operation data into a unified operation data storage;

and detecting and removing samples which still possibly have abnormality in the operation data samples by adopting a discriminator.

Preferably, the preprocessing the operation data further includes:

denoising operation data:

Establishing two-dimensional distribution data based on time and space of the operation data;

Based on the two-dimensional distribution data, selecting data at a vibration-free moment and operation data of an nth space point, and respectively calculating the amplitude and the phase of the operation data of the nth space point by utilizing Fourier transformation to obtain a spectral subtraction result of the nth space point;

and carrying out inverse Fourier transform on the nth space point to obtain denoised operation data.

Preferably, the method further comprises: the communication module comprises a first wire and a second wire, wherein the first wire is used for data transmission of wired data acquisition equipment and wired transmission between edge computing nodes; the second standby line is used for data transmission of the mobile data acquisition equipment, wireless transmission between the edge side and the cloud computing node and between modules of the system, and is used as a standby communication mode when the first line fails.

Preferably, the method further comprises: the security management module is used for managing each module, including the functions of registering each module, defining a menu, configuring a role of a system user, managing the system user, auditing and recording user operation logs, providing log inquiry and publishing notices to the system user, wherein in the security management module, the configuration of the role of the system user is used for distributing the system to the role, and the user of the management system is used for distributing the role and the operation authority to the user.

Second aspect:

A distributed cloud edge collaborative task management method comprises the following steps:

s1: the method comprises the steps of collecting data of electrical equipment in a transformer substation through a data collecting module, wherein the data comprise digital coding data, image data and voiceprint data of transformers, circuit breakers, isolating switches and other equipment;

s2: the data collected in the step S1 are primarily analyzed by a data analysis module, and classified according to sources;

s3: then, a load prediction module predicts the load condition of the next moment by adopting a load prediction method based on similar days;

s4: distributing the fault detection task to a cloud or an appropriate computing node on the edge side through an edge computing module;

s5: constructing a task rule distribution model based on the deep neural network to carry out cooperative processing;

S6: extracting data information required to be subjected to data analysis and processing from the preprocessed operation data by using a task rule distribution model; determining the connection relation between the extracted data information and each device, so as to rapidly acquire the device type corresponding to the data analysis processing result;

S7: and rapidly extracting equipment fault solutions corresponding to the edge calculation database according to the fault types, and solving the faults based on the fault solutions.

(III) beneficial effects

Compared with the prior art, the invention provides a distributed cloud edge collaborative task management system and a distributed cloud edge collaborative task management method, which have the following beneficial effects:

1. According to the distributed cloud edge collaborative task management system and the distributed cloud edge collaborative task management method, the network transmission delay is reduced, so that emergency and time-sensitive tasks can be successfully completed; and a load prediction mode is adopted to predict the system load condition at the next moment, so that the resource utilization efficiency is improved.

2. The distributed cloud-edge collaborative task management system and the distributed cloud-edge collaborative task management method can generate limit probability which has important influence on research search targets on comprehensiveness of perceived data, task completion efficiency, perceived data quality and the like, and achieve the effect of an edge cloud collaborative mode of task distribution according to a formulated edge task distribution strategy.

Drawings

The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:

FIG. 1 is a schematic diagram of the structure 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.

Example 1

As shown in FIG. 1, the present invention provides a distributed cloud edge collaborative task management system, comprising:

The central processing unit provides distributed storage service in a cloud platform resource-as-a-service mode, wherein the distributed storage service is used for key data storage of basic configuration data, integrated data real-time data, historical data, alarm information and auxiliary equipment information;

the data acquisition module is used for collecting data generated by data acquisition equipment of the transformer substation;

the data analysis module is used for performing preliminary classification processing on the acquired data;

The load prediction module is used for selecting a training set and a testing set according to the association degree by adopting a load prediction method based on similar days and taking the altitude of a transformer substation, the voltage level of the transformer substation, the average daily air temperature, the humidity and the weather type as similar daily comment judgment bases; a long-period memory network algorithm is adopted, the service time and the fault rate of electrical equipment in the transformer substation are combined, the data scale acquired by the data acquisition module is used as an adjustment reference, and the load condition at the next moment is predicted;

the edge computing module is used for preprocessing the operation data, analyzing and processing the data according to the computing tasks distributed by the collaborative strategy module based on the preprocessed operation data, matching corresponding control instructions according to analysis and processing results, and controlling the operation of the equipment according to the obtained control instructions by the execution module.

Further comprises: the task scheduling module distributes the fault detection task to the cloud or edge side computing nodes according to the current needed resources and the time urgency degree of the task and the matching edge computing module; the resource monitoring module comprises a resource monitor and a monitoring alarm, and the resource monitor monitors the resource states of cloud and edge side computing nodes in the system; when the computing node is abnormal and cannot execute the current task, the monitoring alarm synchronously gives an alarm, and the task scheduling module redistributes the task which cannot be executed currently to other nodes; the fault detection module further analyzes the data by utilizing the computing node resources to obtain specific fault content and fault occurrence points, and analyzes possible fault reasons and fault treatment measures for operation and maintenance personnel to refer to; the fault alarm module gives an alarm to operation and maintenance personnel after the fault occurs and sends fault analysis data to the cloud backup to serve as historical data for post fault analysis and pre-prediction at the next moment.

The operation data of the equipment comprises operation data obtained by reading in real time according to the set measuring points.

Preprocessing the operational data includes:

Cleaning the blank value, format content, logic error and non-required operation data;

performing feature construction, data grading and data quantization on the operation data;

carrying out data statistics on the transformed operation data, and merging the operation data into a unified operation data storage;

and detecting and removing samples which still possibly have abnormality in the operation data samples by adopting a discriminator.

Preprocessing the operational data further includes:

denoising operation data:

establishing two-dimensional distribution data based on time and space of the operation data;

Based on the two-dimensional distribution data, selecting data without vibration moment and operation data of an nth space point, and respectively calculating the amplitude and the phase of the operation data of the nth space point by utilizing Fourier transformation to obtain a spectral subtraction result of the nth space point;

and carrying out inverse Fourier transform on the nth space point to obtain denoised operation data.

Wherein the task rule distribution model comprises:

according to the related data, calculating efficiency matrixes of all calculation units in the collaborative strategy module are obtained:

Optimal solution for task allocation:

Wherein m represents m target computing units, n represents n types of data needing computing and analyzing, x _ij represents an allocation scheme of the j-th computing unit to the i-th type data, and Z _ij represents the computing efficiency of the j-th computing unit to the i-th type data.

Further comprises: the communication module comprises a first wire and a second wire, wherein the first wire is used for data transmission of wired data acquisition equipment and wired transmission between edge computing nodes; the second standby line is used for data transmission of the mobile data acquisition equipment, wireless transmission between the edge side and the cloud computing node and between modules of the system, and is used as a standby communication mode when the first line fails.

Further comprises: the security management module is used for managing each module, including the functions of registering each module, defining a menu, configuring a role of a system user, managing the system user, auditing and recording user operation logs, providing log inquiry and publishing notices to the system user, wherein in the security management module, the configuration of the role of the system user is used for distributing the system to the role, and the management system user is used for distributing the role and the operation authority to the user.

Example 2

A distributed cloud edge collaborative task management method comprises the following steps:

s1: the method comprises the steps of collecting data of electrical equipment in a transformer substation through a data collecting module, wherein the data comprise digital coding data, image data and voiceprint data of transformers, circuit breakers, isolating switches and other equipment;

s2: the data collected in the step S1 are primarily analyzed by a data analysis module, and classified according to sources;

s3: then, a load prediction module predicts the load condition of the next moment by adopting a load prediction method based on similar days;

s4: distributing the fault detection task to a cloud or an appropriate computing node on the edge side through an edge computing module;

s5: constructing a task rule distribution model based on the deep neural network to carry out cooperative processing;

S6: extracting data information required to be subjected to data analysis and processing from the preprocessed operation data by using a task rule distribution model; determining the connection relation between the extracted data information and each device, so as to rapidly acquire the device type corresponding to the data analysis processing result;

S7: and rapidly extracting equipment fault solutions corresponding to the edge calculation database according to the fault types, and solving the faults based on the fault solutions.

Further, the method comprises the steps of,

S3.1: inputting historical samples and influence factor data of a day to be predicted, wherein the influence factor data comprise the altitude of a transformer substation, the voltage level of the transformer substation, the service time of electrical equipment in the transformer substation, the comprehensive failure rate of the electrical equipment in the transformer substation, the average daily air temperature, the humidity and the weather type;

S3.2: selecting a data set required by load prediction, wherein the data set comprises a training set and a testing set;

S3.3: and (3) carrying out time sequence prediction by adopting a long-period memory network algorithm, and predicting the load condition of the next moment, wherein the load condition comprises calculation, storage and network resources required by the next moment.

Claims (8)

1. The utility model provides a distributed cloud limit collaborative task management system which characterized in that includes:

the central processing unit provides distributed storage service in a cloud platform resource-as-a-service mode, wherein the distributed storage service is used for key data storage of basic configuration data and integrated data real-time data, historical data, alarm information and auxiliary equipment information;

the data acquisition module is used for collecting data generated by data acquisition equipment of the transformer substation;

the data analysis module is used for performing preliminary classification processing on the acquired data;

The load prediction module is used for selecting a training set and a testing set according to the association degree by adopting a load prediction method based on similar days and taking the altitude of a transformer substation, the voltage level of the transformer substation, the average daily air temperature, the humidity and the weather type as similar daily comment judgment bases; a long-period memory network algorithm is adopted, the service time and the fault rate of electrical equipment in the transformer substation are combined, the data scale acquired by the data acquisition module is used as an adjustment reference, and the load condition at the next moment is predicted;

the edge computing module is used for preprocessing the operation data, analyzing and processing the data according to the computing tasks distributed by the collaborative strategy module based on the preprocessed operation data, matching corresponding control instructions according to analysis and processing results, and controlling the operation of the equipment according to the obtained control instructions by the execution module.

2. The distributed cloud computing collaborative task management system of claim 1, further comprising: the task scheduling module distributes the fault detection task to the cloud or edge side computing nodes according to the current needed resources and the time urgency degree of the task and the matching edge computing module; the resource monitoring module comprises a resource monitor and a monitoring alarm, wherein the resource monitor monitors the resource states of cloud computing nodes and edge computing nodes in the system; when the computing node is abnormal and cannot execute the current task, the monitoring alarm synchronously gives an alarm, and the task scheduling module redistributes the task which cannot be executed currently to other nodes; the fault detection module further analyzes the data by utilizing the computing node resources to obtain specific fault content and fault occurrence points, and analyzes possible fault reasons and fault treatment measures for reference of operation and maintenance personnel; the fault alarm module gives an alarm to operation and maintenance personnel after the fault occurs and sends fault analysis data to the cloud backup to serve as historical data for post fault analysis and pre-prediction at the next moment.

3. The distributed cloud computing collaborative task management system of claim 1, wherein: the operation data of the equipment comprises operation data obtained by reading in real time according to the set measuring points.

4. The distributed cloud computing collaborative task management system of claim 1, wherein: the preprocessing the operation data includes:

Cleaning the blank value, format content, logic error and non-required operation data;

performing feature construction, data classification and data quantization on the operation data;

carrying out data statistics on the transformed operation data, and merging the operation data into a unified operation data storage;

and detecting and removing samples which still possibly have abnormality in the operation data samples by adopting a discriminator.

5. The distributed cloud computing collaborative task management system of claim 1, wherein: the preprocessing the operational data further includes:

denoising operation data:

Establishing two-dimensional distribution data based on time and space of the operation data;

Based on the two-dimensional distribution data, selecting data at a vibration-free moment and operation data of an nth space point, and respectively calculating the amplitude and the phase of the operation data of the nth space point by utilizing Fourier transformation to obtain a spectral subtraction result of the nth space point;

and carrying out inverse Fourier transform on the nth space point to obtain denoised operation data.

6. The distributed cloud computing collaborative task management system of claim 1, further comprising: the communication module comprises a first wire and a second wire, wherein the first wire is used for data transmission of wired data acquisition equipment and wired transmission between edge computing nodes; the second standby line is used for data transmission of the mobile data acquisition equipment, wireless transmission between the edge side and the cloud computing node and between modules of the system, and is used as a standby communication mode when the first line fails.

7. The distributed cloud computing collaborative task management system of claim 1, further comprising: the security management module is used for managing each module, including the functions of registering each module, defining a menu, configuring a role of a system user, managing the system user, auditing and recording user operation logs, providing log inquiry and publishing notices to the system user, wherein in the security management module, the configuration of the role of the system user is used for distributing the system to the role, and the user of the management system is used for distributing the role and the operation authority to the user.

8. The distributed cloud edge collaborative task management method applied to the distributed cloud edge collaborative task management system described in the claims 1-7 is characterized by comprising the following steps:

s1: the method comprises the steps of collecting data of electrical equipment in a transformer substation through a data collecting module, wherein the data comprise digital coding data, image data and voiceprint data of transformers, circuit breakers, isolating switches and other equipment;

s2: the data collected in the step S1 are primarily analyzed by a data analysis module, and classified according to sources;

s3: then, a load prediction module predicts the load condition of the next moment by adopting a load prediction method based on similar days;

s4: distributing the fault detection task to a cloud or an appropriate computing node on the edge side through an edge computing module;

s5: constructing a task rule distribution model based on the deep neural network to carry out cooperative processing;

S6: extracting data information required to be subjected to data analysis and processing from the preprocessed operation data by using a task rule distribution model; determining the connection relation between the extracted data information and each device, so as to rapidly acquire the device type corresponding to the data analysis processing result;

S7: and rapidly extracting equipment fault solutions corresponding to the edge calculation database according to the fault types, and solving the faults based on the fault solutions.