CN117761444B

CN117761444B - Method and system for monitoring service life of surge protector

Info

Publication number: CN117761444B
Application number: CN202410197410.3A
Authority: CN
Inventors: 石兵雨; 李凯; 刘学锋
Original assignee: Shenzhen Putai Electrical Co ltd
Current assignee: Shenzhen Putai Electrical Co ltd
Priority date: 2024-02-22
Filing date: 2024-02-22
Publication date: 2024-04-30
Anticipated expiration: 2044-02-22
Also published as: CN117761444A

Abstract

The invention provides a service life monitoring method and system of a surge protector. The method comprises the following steps: installing a sensor in a power system, monitoring power data in real time through the sensor, and monitoring key parameters of a surge protector through the sensor; the collected power data and the key parameters are sent to edge equipment; preprocessing the received power data and key parameters through the edge equipment to obtain preprocessed monitoring data; transmitting the preprocessed monitoring data to the cloud platform; and after receiving the monitoring data, the cloud platform stores the monitoring data in a segmented mode, and processes the monitoring data stored in the segmented mode through a deep learning algorithm. By installing the sensor in the power system, key parameters and power data of the surge protector can be monitored in real time, and the running state of the surge protector can be known in time.

Description

Method and system for monitoring service life of surge protector

Technical Field

The invention provides a life monitoring method and a life monitoring system of a surge protector, and belongs to the technical field of life monitoring.

Background

The surge protector is an important protection device in the power system, and can effectively prevent damage to the power system caused by lightning, power surge and the like. However, the lifetime of the surge protector directly affects its protection effect. Therefore, it is of great importance to monitor and predict the life of the surge protector.

The existing surge protector life monitoring method is mainly based on a traditional physical detection method, and the life of the surge protector is estimated by detecting parameters such as temperature, current and the like of the surge protector. However, these methods not only require manual operations, but also have low accuracy, and cannot meet the requirements of real-time and remote monitoring.

Disclosure of Invention

The invention provides a life monitoring method and a life monitoring system of a surge protector, which are used for solving the problems that detection in the prior art depends on manual work, and has low detection efficiency, low accuracy and insufficient instantaneity:

The invention provides a life monitoring method of a surge protector, which comprises the following steps:

S1: installing a sensor in a power system, monitoring power data in real time through the sensor, and monitoring key parameters of a surge protector through the sensor; the collected power data and the key parameters are sent to edge equipment;

s2: preprocessing the received power data and key parameters through the edge equipment to obtain preprocessed monitoring data; transmitting the preprocessed monitoring data to the cloud platform;

S3: after the cloud platform receives the monitoring data, the monitoring data are stored in segments, the monitoring data stored in segments are processed through a deep learning algorithm, and the health condition and the service life state of the surge protector are judged through a statistical model based on the processing result;

s4: based on the health condition and the service life state of the surge protector, a warning mechanism reminds a user to maintain or replace the surge protector.

Further, a sensor is arranged in the power system, the power data is monitored in real time through the sensor, and key parameters of the surge protector are monitored through the sensor; the collected power data and the key parameters are sent to edge equipment; comprising the following steps:

s11: selecting a sensor according to the power data to be monitored and key parameters of the surge protector;

s12: installing the sensor, and connecting the sensor with the edge equipment through the Internet of things;

S13: and transmitting the electric power data acquired by the sensor and key parameters to edge equipment in a wired or wireless mode.

Further, the edge equipment preprocesses the received power data and key parameters to obtain preprocessed monitoring data; transmitting the preprocessed monitoring data to the cloud platform; comprising the following steps:

S21: the electric power data and the key parameters are subjected to data filtering through the edge equipment, and invalid and abnormal data are removed;

S22: the data is filtered, the power data and key parameters of invalid and abnormal data are removed, and data correction and standardization processing are carried out;

s23: carrying out noise reduction treatment on the standardized power data and key parameters, and compressing the power data and key parameters subjected to the noise reduction treatment through a compression algorithm to obtain monitoring data;

S24: and aggregating the monitoring data, and transmitting the aggregated monitoring data to a cloud platform in a wired or wireless mode.

Further, after receiving the monitoring data, the cloud platform stores the monitoring data in a segmented mode, processes the monitoring data stored in the segmented mode through a deep learning algorithm, and judges the health condition and the service life state of the surge protector through a statistical model based on a processing result; comprising the following steps:

S31: after the cloud platform receives the monitoring data, the monitoring data are stored in different storage spaces in a segmented mode according to a time window, and then the monitoring data are stored in different subspaces in the storage spaces through the monitoring data types;

s32: dividing the monitoring data in different subspaces into a plurality of data blocks, wherein each data block represents a processing task;

s33: the data blocks are processed in parallel through a processing unit or a process, the resource consumption of the processing unit or the process is monitored in real time, and the resource is scheduled through a scheduling algorithm;

S34: after the parallel processing is finished, merging the calculation results into an overall result through a merging algorithm;

S35: classifying or regressing the overall result through a machine learning algorithm, and judging the health state of the surge protector through comparison with a preset threshold value;

S36: based on a statistical model, the service time and the working state of the surge protector are analyzed and modeled, and the service life state of the surge protector is predicted.

Further, the method reminds a user to maintain or replace the surge protector through an early warning mechanism based on the health condition and the service life state of the surge protector; comprising the following steps:

S41: generating a state evaluation report and a maintenance suggestion of the surge protector according to the judging result of the health condition and the life state;

S42: and feeding back the evaluation report and the maintenance proposal to a user through a prediction mechanism, and taking corresponding actions after the user receives the evaluation report and the maintenance proposal.

The invention provides a life monitoring system of a surge protector, which comprises:

and a data acquisition module: installing a sensor in a power system, monitoring power data in real time through the sensor, and monitoring key parameters of a surge protector through the sensor; the collected power data and the key parameters are sent to edge equipment;

And a data preprocessing module: preprocessing the received power data and key parameters through the edge equipment to obtain preprocessed monitoring data; transmitting the preprocessed monitoring data to the cloud platform;

And a data processing module: after the cloud platform receives the monitoring data, the monitoring data are stored in segments, the monitoring data stored in segments are processed through a deep learning algorithm, and the health condition and the service life state of the surge protector are judged through a statistical model based on the processing result;

And the early warning module is used for: based on the health condition and the service life state of the surge protector, a warning mechanism reminds a user to maintain or replace the surge protector.

Further, the data acquisition module includes:

A selection module; selecting a sensor according to the power data to be monitored and key parameters of the surge protector;

And (3) installing a module: installing the sensor, and connecting the sensor with the edge equipment through the Internet of things;

And a transmission module: and transmitting the electric power data acquired by the sensor and key parameters to edge equipment in a wired or wireless mode.

Further, the data preprocessing module includes:

And a data filtering module: the electric power data and the key parameters are subjected to data filtering through the edge equipment, and invalid and abnormal data are removed;

Labeling processing module: the data is filtered, the power data and key parameters of invalid and abnormal data are removed, and data correction and standardization processing are carried out;

and a data compression module: carrying out noise reduction treatment on the standardized power data and key parameters, and compressing the power data and key parameters subjected to the noise reduction treatment through a compression algorithm to obtain monitoring data;

And a data aggregation module: and aggregating the monitoring data, and transmitting the aggregated monitoring data to a cloud platform in a wired or wireless mode.

Further, the data processing module includes:

And a data segmentation module: after the cloud platform receives the monitoring data, the monitoring data are stored in different storage spaces in a segmented mode according to a time window, and then the monitoring data are stored in different subspaces in the storage spaces through the monitoring data types;

The task dividing module: dividing the monitoring data in different subspaces into a plurality of data blocks, wherein each data block represents a processing task;

And the parallel processing module is used for: the data blocks are processed in parallel through a processing unit or a process, the resource consumption of the processing unit or the process is monitored in real time, and the resource is scheduled through a scheduling algorithm;

and a data merging module: after the parallel processing is finished, merging the calculation results into an overall result through a merging algorithm;

the state judging module is used for: classifying or regressing the overall result through a machine learning algorithm, and judging the health state of the surge protector through comparison with a preset threshold value;

A state prediction module: based on a statistical model, the service time, the working state and the like of the surge protector are analyzed and modeled, and the service life state of the surge protector is predicted.

Further, the early warning module includes:

The suggestion generation module: generating a state evaluation report and a maintenance suggestion of the surge protector according to the judging result of the health condition and the life state;

The measure processing module is used for: and feeding back the evaluation report and the maintenance advice to a user through a prediction mechanism, and taking corresponding actions after the user receives the evaluation report and the maintenance advice.

The invention has the beneficial effects that: by installing the sensor in the power system, key parameters and power data of the surge protector can be monitored in real time, and the running state of the surge protector can be known in time; the edge equipment and the cloud platform perform preprocessing, standardization processing, noise reduction processing and compression on the acquired data, so that the data processing efficiency is improved, and the burden of data transmission and storage is reduced; the cloud platform processes the monitoring data stored in the segments through a deep learning algorithm, so that the health condition and the service life state of the surge protector can be more accurately judged, and the prediction accuracy is improved; according to the health condition and the service life state of the surge protector, a warning mechanism is used for reminding a user to maintain or replace, so that potential problems are timely treated, and the stable operation of the power system is ensured; the resources are scheduled through a scheduling algorithm, so that each resource is guaranteed to be reasonably utilized, the use of the resources is maximized, and the data processing efficiency is improved; the whole result is classified or regressed through a machine learning algorithm, so that the health state of the surge protector can be judged more accurately, and a more accurate basis is provided for maintenance; the service time, the working state and the like of the surge protector are analyzed and modeled based on the statistical model, so that the service life state of the surge protector can be predicted, and more comprehensive information is provided for users; and generating a state evaluation report and a maintenance suggestion according to the judging result of the health condition and the life state, and feeding back the state evaluation report and the maintenance suggestion to a user through a prediction mechanism, so that the user can take corresponding actions in time.

Drawings

Fig. 1 is a step diagram of a method for monitoring the life of a surge protector according to the present invention;

fig. 2 is a block diagram of a life monitoring system of a surge protector according to the present invention.

Detailed Description

In order that the above-recited objects, features and advantages of the present application will be more clearly understood, a more particular description of the application will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings. It should be noted that, without conflict, the embodiments of the present application and features in the embodiments may be combined with each other.

In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, and the described embodiments are merely some, rather than all, embodiments of the present invention. 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.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

In one embodiment of the present invention, as shown in fig. 1, a method for monitoring the lifetime of a surge protector, the method comprising:

S1: installing a sensor in a power system, monitoring power data in real time through the sensor, and monitoring key parameters of a surge protector through the sensor; the power data includes voltage, current, power and energy consumption; the key parameters include operating voltage and surge current; the collected power data and the key parameters are sent to edge equipment;

The working principle of the technical scheme is as follows: sensors are installed in the power system, and power data including voltage, current, power and energy consumption are monitored in real time through the sensors. Meanwhile, the sensor also monitors key parameters of the surge protector, including working voltage and surge current; the power data and key parameters acquired by the sensor are preprocessed through the edge equipment, and preprocessed monitoring data are obtained. The preprocessed monitoring data are transmitted to a cloud platform for further processing and analysis; and after the cloud platform receives the monitoring data, the data are stored in a segmented mode, and the data are processed by using a deep learning algorithm. And judging the health condition and the service life state of the surge protector through a statistical model. These determinations may be made based on historical data and known failure modes; according to the health condition and the service life state of the surge protector, the system reminds a user to maintain or replace through an early warning mechanism. The user can timely take action according to the early warning information, and normal operation of the surge protector and stable operation of the system are ensured.

The technical scheme has the effects that: by installing the sensor and monitoring the power data and key parameters in real time, the working state and performance index of the surge protector can be timely obtained, and potential faults or damage risks can be effectively avoided; the collected data is preprocessed through the edge equipment, useful monitoring information can be extracted, and the preprocessed data is transmitted to the cloud platform to prepare for subsequent deep learning algorithm processing and analysis; the cloud platform processes the monitoring data stored in the segments by using a deep learning algorithm, and judges the health condition and the service life state of the surge protector by using a statistical model. Thus, potential problems can be found in advance, corresponding maintenance measures are taken, and faults are avoided; based on the health condition and the service life state of the surge protector, the system reminds a user to maintain or replace through an early warning mechanism. The protector is helpful for timely handling the protector with fault risk, and ensures the normal operation and safety of the power system; by means of real-time monitoring and health state judgment of the surge protector, stability and reliability of the power system can be improved. The protector can be maintained and replaced in time, so that the fault risk can be effectively reduced, and the downtime and the maintenance cost are reduced.

According to one embodiment of the invention, a sensor is installed in a power system, power data is monitored in real time through the sensor, and key parameters of a surge protector are monitored through the sensor; the collected power data and the key parameters are sent to edge equipment; comprising the following steps:

S11: selecting a sensor according to the power data to be monitored and key parameters of the surge protector; the sensor comprises a voltage sensor, a current sensor, a power sensor and an energy consumption sensor;

The working principle of the technical scheme is as follows: and selecting a corresponding sensor according to the power data required to be monitored and key parameters of the surge protector. For example, a voltage sensor, a current sensor, a power sensor, and an energy consumption sensor may be selected; installing the selected sensor, connecting the sensor with a surge protector, and connecting the sensor with edge equipment through the technology of the Internet of things; the collected power data and key parameters are transmitted to the edge equipment in a wired or wireless mode. In the process, the sensor monitors the electric power data and key parameters of the surge protector in real time and sends the acquired data to the edge equipment; the edge device performs preprocessing, such as cleaning, denoising, filtering and the like, on the received data so as to process and analyze the received data by a subsequent deep learning algorithm; the preprocessed data is transmitted to a cloud platform to prepare for the processing and analysis of a subsequent deep learning algorithm; the cloud platform processes the monitoring data stored in the segments by using a deep learning algorithm, and judges the health condition and the service life state of the surge protector by using a statistical model; based on the health condition and the service life state of the surge protector, the system reminds a user to maintain or replace through an early warning mechanism.

The technical scheme has the effects that: by installing the sensor in the power system, the power data and key parameters of the surge protector can be monitored in real time, and the running state of the power equipment can be mastered in time; the sensor selects various aspects such as voltage, current, power and energy consumption, and the running condition of the power equipment can be comprehensively monitored; the data acquired by the sensor is sent to the edge equipment for preprocessing, so that the data quality is higher, and the occurrence of error judgment and false alarm conditions can be reduced; the real-time monitoring and data acquisition of the sensor can help enterprises to quickly find equipment faults, and the maintenance efficiency and the equipment utilization rate are improved; and processing the monitoring data stored in a segmented mode according to a deep learning algorithm, judging the health condition and the service life state of the surge protector, and reminding a user to maintain or replace through an early warning mechanism, so that maintenance cost and risk are reduced.

According to one embodiment of the invention, the edge equipment is used for preprocessing the received power data and key parameters to obtain preprocessed monitoring data; transmitting the preprocessed monitoring data to the cloud platform; comprising the following steps:

S22: the data is filtered, the power data and key parameters of invalid and abnormal data are removed, and data correction and standardization processing are carried out; the method for the standardized treatment comprises the following steps:

by the formula Calculating the standard deviation of each attribute in the power data and the key parameters, wherein the standard deviation is calculated by the methodThe formula performs normalization processing on the data. Wherein/>Representing the ith attribute value, n representing the number of attributes;

The working principle of the technical scheme is as follows: the edge equipment performs data filtering on the received power data and key parameters to remove invalid and abnormal data, so that the high quality of the data transmitted to the cloud platform is ensured, and the consumption of cloud computing resources is reduced; the data is subjected to data correction and standardization processing by the power data and key parameters after data filtration, so that the data has consistency and comparability, and the subsequent data analysis and processing are convenient; and carrying out noise reduction treatment on the standardized power data and key parameters, removing noise interference and improving the accuracy of the data. Then, compressing the data after noise reduction treatment through a compression algorithm, so that the data volume is reduced, and the transmission cost and delay are reduced; the processed monitoring data are aggregated, a plurality of data points are combined into one, and the data quantity and the calculation complexity are reduced, so that the demands on network bandwidth and cloud platform calculation resources are reduced; the aggregated monitoring data is transmitted to the cloud platform in a wired or wireless mode for subsequent data storage, analysis and decision making. Aggregation may reduce the amount of data and computational complexity.

The technical scheme has the effects that: the electric power data is preprocessed through the edge equipment, operations such as data filtering, correction and standardization can be completed at the edge end, the quantity of the data transmitted to the cloud platform is reduced, the transmission delay and the cost are reduced, and the transmission efficiency is improved; invalid and abnormal data can be removed through operations such as data filtering, correction and standardization, and the quality and usability of the data are improved, so that the subsequent data processing is more accurate and reliable; through noise reduction processing and a compression algorithm, the data volume can be reduced, the requirements on network bandwidth and cloud platform computing resources are reduced, the computing resources are saved, and the system performance is improved; by aggregating the monitoring data, the data volume and the calculation complexity can be reduced, the demands on network bandwidth and cloud platform calculation resources are reduced, and the data processing efficiency and the system performance are improved; the data is preprocessed and transmitted to the edge equipment to respond in real time, so that the change of the power system can be responded quickly, measures can be taken in time, and the stability and the safety of the power system are improved. Meanwhile, the first compression operation can reduce the volume of power data and key parameters by adopting a compression algorithm, thereby reducing the bandwidth and storage cost required by transmission. This helps to improve the efficiency of data transmission and saves network resources; since the first compression operation reduces the volume of data, the time required for transmission is correspondingly reduced. The delay of the data in the transmission process can be reduced, the monitoring data can reach the cloud platform faster, and real-time data analysis and processing are realized; the second aggregation operation merges multiple data points into one, reducing the amount of data and the computational complexity. Therefore, the computing load of the cloud platform can be reduced, the computing efficiency is improved, and the use of computing resources is saved; the first compression operation reduces the volume of data, thereby reducing the space required for storage. The storage cost on the cloud platform can be reduced, so that large-scale power data and key parameters can be saved and managed more economically; the second aggregation operation combines multiple data points into one, so that the complexity and the dimension of the data can be reduced, and the subsequent data analysis and decision making are simplified. This helps to improve the efficiency and accuracy of data analysis while reducing the complexity of data processing. The data filtering, correcting, standardizing, noise reducing, compressing, aggregating and transmitting steps are carried out through the formula, so that the quality and accuracy of the power data and key parameters can be improved, and the subsequent data analysis and application are facilitated. Meanwhile, the processing methods can save storage and transmission resources and improve the efficiency and performance of data processing. And the invalid and abnormal data can be removed by carrying out data filtering on the power data and the key parameters through the edge equipment. Thus, the accuracy and the reliability of the data can be improved, and the influence of error data on subsequent processing and analysis is avoided; by correcting and normalizing the data, the deviation and difference between the data can be eliminated. The normalization process can enable the data to have the same scale and range, and subsequent analysis and comparison are convenient. Meanwhile, calculating the standard deviation of each attribute can provide information of data distribution, so that a user is helped to know the change condition of data; the standardized power data and key parameters are subjected to noise reduction treatment, so that unnecessary noise and interference can be removed, and the quality and the credibility of the data are improved. The data after noise reduction treatment is compressed through a compression algorithm, so that the storage and transmission cost of the data can be reduced, and the efficiency is improved; the processed monitoring data are aggregated, so that the data of a plurality of data sources can be combined into a whole, and subsequent analysis and processing are facilitated. The aggregated monitoring data is transmitted to the cloud platform in a wired or wireless mode, so that remote monitoring and management of the data can be realized, and the accessibility and the utilization value of the data are improved.

According to one embodiment of the invention, after the cloud platform receives the monitoring data, the monitoring data are stored in segments, the monitoring data stored in segments are processed through a deep learning algorithm, and the health condition and the service life state of the surge protector are judged through a statistical model based on a processing result; comprising the following steps:

s33: the data blocks are processed in parallel through a processing unit or a process, the resource consumption of the processing unit or the process is monitored in real time, and the resource is scheduled through a scheduling algorithm; each resource is guaranteed to be reasonably utilized, and the resource utilization is maximized; the calculation formula of the resource usage amount is as follows:

；

wherein g represents a data block number; g represents the total data block number; representing the hardware resource capacity of the jth processing unit; /(I) Representing the current resource utilization of the jth processing unit; n represents the total number of processing units; l represents a data block size;

s36: based on a statistical model, the service time, the working state and the like of the surge protector are analyzed and modeled, and the service life state of the surge protector is predicted.

The working principle of the technical scheme is as follows: and after the cloud platform receives the monitoring data, the data are stored in segments according to the time window. This means that the data is divided into different segments by time and stored in subspaces of different storage spaces. Thus, the subsequent processing and management can be facilitated; the monitored data within each subspace is divided into a plurality of data chunks, each data chunk representing a processing task. Thus, the data can be processed in parallel, and the processing efficiency and speed are improved; and processing the data blocks in parallel by a processing unit or a process, and monitoring the use condition of the resources in real time. The scheduling algorithm is used for scheduling the resources and ensuring the balance and priority of the processing tasks; and after the parallel processing is completed, the calculation results of all the processing units or processes are combined into an overall result through a combination algorithm. Thus, the integrity and consistency of the data can be ensured; and classifying or carrying out regression analysis on the whole result by using a machine learning algorithm. The health status of the surge protector can be determined by comparing with a preset threshold. For example, the results may be categorized into normal, abnormal, etc. categories, or the remaining life of the surge protector may be assessed; based on the statistical model, the service time, the working state and the like of the surge protector are analyzed and modeled. This allows for predicting the life-state of the surge protector and providing predictive maintenance and management recommendations.

The technical scheme has the effects that: the monitoring data is stored and processed in a segmented mode, so that the state change of the surge protector can be more accurately captured, and information loss or blurring caused by overlarge data volume is avoided; the cloud platform can receive and process the monitoring data in real time, and can complete the processing task in a short time through parallel processing and optimal scheduling, and timely feed back the health condition of the surge protector so as to take corresponding measures; by using a deep learning algorithm and a statistical model, the cloud platform can automatically evaluate and predict the health condition and the life state of the surge protector without manual intervention. Thus, the labor cost can be reduced and the management efficiency can be improved; through life prediction based on a statistical model, the cloud platform can discover potential faults and ageing problems of the surge protector in advance and provide maintenance and replacement suggestions in time. Thus, the shutdown loss and the safety risk caused by the sudden failure of the equipment can be avoided; the cloud platform can share the processing results and the monitoring data to related management personnel and technical personnel, so that the management personnel and the technical personnel can monitor the state of the surge protector remotely at any time and any place and make decisions and adjustments. Meanwhile, the large-scale monitoring data can be divided into smaller data blocks through multiple segmentation, so that the processing task of each data block is lighter and more efficient. The segmented data are processed in parallel, so that a plurality of computing resources can be utilized for processing at the same time, and the overall data processing efficiency is greatly improved; by parallel processing and optimized scheduling, computing resources can be fully utilized. Whether a plurality of processing units are used on a single computer or a plurality of nodes are utilized for processing in a distributed computing environment, the maximum utilization of resources can be realized, and the overall performance of the system is improved; by parallel processing of the segmented data, processing tasks may be assigned to different processing units or nodes. The balance of the tasks can be realized, overload of certain processing units is avoided, and meanwhile, the tasks can be scheduled according to the priority of the tasks, so that the important tasks can be completed in time; multiple segmentation and parallel processing may reduce the delay of data processing so that results can be produced more quickly. Particularly, for application scenes needing real-time monitoring and decision making, the processing result can be timely obtained so as to make corresponding actions; the system has better expandability and flexibility through multiple segmentation and parallel processing. The processing units can be increased or decreased according to the requirements, and the segmentation strategy can be dynamically adjusted according to the data size so as to adapt to monitoring data processing tasks with different scales and complexity. Through the formula, the resource consumption of the processing unit or the process can be monitored in real time, and resource scheduling is performed according to the calculated consumption, so that reasonable utilization of each resource is ensured, and the resource use is maximized. Therefore, the efficiency and the speed of parallel processing can be effectively improved, and the waste of resources is reduced.

According to one embodiment of the invention, the surge protector is reminded to be maintained or replaced by a user through an early warning mechanism based on the health condition and the service life state of the surge protector; comprising the following steps:

S42: and feeding back the evaluation report and the evaluation suggestion to a user through a prediction mechanism, and taking corresponding actions after the user receives the evaluation report and the maintenance suggestion. If the report suggests maintenance measures, cleaning, checking connection and other operations are performed; if replacement measures are recommended, replacement of the element or the entire surge protector can be considered.

The working principle of the technical scheme is as follows: monitoring the surge protector through equipment such as a sensor and the like to acquire the running state and related parameter data of the surge protector; based on the collected monitoring data, the health condition and the life state of the surge protector are judged and evaluated by using a deep learning algorithm and a statistical model. These models can be trained to identify abnormal, faulty and aged features and compare them to preset thresholds; and generating a state evaluation report of the surge protector according to the judging result of the health condition and the life state. The report will provide a detailed description of health and state of life, as well as possible problems and risks; in conjunction with a status assessment report of the surge protector, the system will provide corresponding maintenance recommendations. Repair advice may include measures to clean, inspect connections, replace components or the entire surge protector; and according to the evaluation report and the maintenance suggestion, the system can send early warning information to the user through an early warning mechanism. After the user receives the assessment report and the maintenance recommendation, a corresponding action may be taken. If the report suggests maintenance measures, the user can perform operations such as cleaning, checking connection and the like; if replacement measures are recommended, the user may consider replacing the element or the whole surge protector.

The technical scheme has the effects that: by monitoring the health and state of life of the surge protector, the system can discover potential problems and risks in advance. Thus, the influence of equipment faults on normal operation can be avoided, and the occurrence of emergency situations of maintenance and replacement is reduced; and generating a state evaluation report and maintenance advice of the surge protector according to the judging result of the health condition and the life state. These reports and suggestions provide detailed information that enables a user to fully understand the condition of the surge protector and to take corresponding maintenance or replacement measures; the system may provide corresponding operational guidelines for different assessment recommendations. If the report suggests maintenance measures, the user can perform operations such as cleaning, checking connection and the like; if replacement measures are recommended, the user may consider replacing the element or the whole surge protector. Thus, the user can adopt correct operation according to specific conditions, and the maintenance effect is improved; through regular health condition and life state evaluation, a user can timely take maintenance measures, and the service life of the surge protector is prolonged. Thus, equipment faults caused by aging or damage can be avoided, and the cost for replacing equipment is saved; surge protectors are important components that protect electronic devices from surge interference and damage. Through timely maintenance and replacement, the normal operation and effective protection of the surge protector can be ensured, and the reliability and stability of the system are improved.

In one embodiment of the present invention, a life monitoring system for a surge protector device, the system comprising:

And a data acquisition module: installing a sensor in a power system, monitoring power data in real time through the sensor, and monitoring key parameters of a surge protector through the sensor; the power data includes voltage, current, power and energy consumption; the key parameters include operating voltage and surge current; the collected power data and the key parameters are sent to edge equipment;

In one embodiment of the present invention, the data acquisition module includes:

a selection module; selecting a sensor according to the power data to be monitored and key parameters of the surge protector; the sensor comprises a voltage sensor, a current sensor, a power sensor and an energy consumption sensor;

In one embodiment of the present invention, the data preprocessing module includes:

Labeling processing module: the data is filtered, the power data and key parameters of invalid and abnormal data are removed, and data correction and standardization processing are carried out; the method for the standardized treatment comprises the following steps:

In one embodiment of the present invention, the data processing module includes:

and the parallel processing module is used for: the data blocks are processed in parallel through a processing unit or a process, the resource consumption of the processing unit or the process is monitored in real time, and the resource is scheduled through a scheduling algorithm; each resource is guaranteed to be reasonably utilized, and the resource utilization is maximized; the calculation formula of the resource usage amount is as follows:

；

In one embodiment of the present invention, the early warning module includes:

The measure processing module is used for: and feeding back the evaluation report and the evaluation suggestion to a user through a prediction mechanism, and taking corresponding actions after the user receives the evaluation report and the maintenance suggestion. If the report suggests maintenance measures, cleaning, checking connection and other operations are performed; if replacement measures are recommended, replacement of the element or the entire surge protector can be considered.

It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.

Claims

1. A method of life monitoring of a surge protector device, the method comprising:

Installing a sensor in a power system, monitoring power data in real time through the sensor, and monitoring key parameters of a surge protector through the sensor; the collected power data and the key parameters are sent to edge equipment;

Preprocessing the received power data and key parameters through the edge equipment to obtain preprocessed monitoring data; transmitting the preprocessed monitoring data to the cloud platform;

after the cloud platform receives the monitoring data, the monitoring data are stored in segments, the monitoring data stored in segments are processed through a deep learning algorithm, and the health condition and the service life state of the surge protector are judged through a statistical model based on the processing result;

based on the health condition and the service life state of the surge protector, reminding a user to maintain or replace the surge protector through an early warning mechanism;

after the cloud platform receives the monitoring data, the monitoring data are stored in segments, the monitoring data stored in segments are processed through a deep learning algorithm, and the health condition and the service life state of the surge protector are judged through a statistical model based on the processing result; comprising the following steps:

After the cloud platform receives the monitoring data, the monitoring data are stored in different storage spaces in a segmented mode according to a time window, and then the monitoring data are stored in different subspaces in the storage spaces through monitoring data types;

Dividing the monitoring data in different subspaces into a plurality of data blocks, wherein each data block represents a processing task;

The data blocks are processed in parallel through a processing unit or a process, the resource consumption of the processing unit or the process is monitored in real time, and the resource is scheduled through a scheduling algorithm;

after the parallel processing is finished, merging the calculation results into an overall result through a merging algorithm;

Classifying or regressing the overall result through a machine learning algorithm, and judging the health state of the surge protector through comparison with a preset threshold value;

Based on a statistical model, the service time and the working state of the surge protector are analyzed and modeled, and the service life state of the surge protector is predicted.

2. The method for monitoring the service life of a surge protector according to claim 1, wherein a sensor is installed in a power system, power data is monitored in real time through the sensor, and key parameters of the surge protector are monitored through the sensor; the collected power data and the key parameters are sent to edge equipment; comprising the following steps:

Selecting a sensor according to the power data to be monitored and key parameters of the surge protector;

installing the sensor, and connecting the sensor with the edge equipment through the Internet of things;

And transmitting the electric power data acquired by the sensor and key parameters to edge equipment in a wired or wireless mode.

3. The life monitoring method of a surge protector according to claim 1, wherein the edge device performs preprocessing on the received power data and key parameters to obtain preprocessed monitoring data; transmitting the preprocessed monitoring data to the cloud platform; comprising the following steps:

the electric power data and the key parameters are subjected to data filtering through the edge equipment, and invalid and abnormal data are removed;

the data is filtered, the power data and key parameters of invalid and abnormal data are removed, and data correction and standardization processing are carried out;

carrying out noise reduction treatment on the standardized power data and key parameters, and compressing the power data and key parameters subjected to the noise reduction treatment through a compression algorithm to obtain monitoring data;

and aggregating the monitoring data, and transmitting the aggregated monitoring data to a cloud platform in a wired or wireless mode.

4. The life monitoring method of the surge protector according to claim 1, wherein the life monitoring method is characterized in that a warning mechanism is used for reminding a user of maintaining or replacing the surge protector based on the health condition and the life state of the surge protector; comprising the following steps:

generating a state evaluation report and a maintenance suggestion of the surge protector according to the judging result of the health condition and the life state;

And feeding back the evaluation report and the maintenance proposal to a user through a prediction mechanism, and taking corresponding actions after the user receives the evaluation report and the maintenance proposal.

5. A life monitoring system for a surge protector, the system comprising:

And the early warning module is used for: based on the health condition and the service life state of the surge protector, reminding a user to maintain or replace the surge protector through an early warning mechanism;

The data processing module comprises:

and a data segmentation module: after the cloud platform receives the monitoring data, the monitoring data are stored in different storage spaces in a segmented mode according to a time window, and then the monitoring data are stored in different subspaces in the storage spaces through monitoring data types;

a state prediction module: based on a statistical model, the service time and the working state of the surge protector are analyzed and modeled, and the service life state of the surge protector is predicted.

6. The life monitoring system of the surge protector of claim 5, wherein the data acquisition module comprises:

7. The life monitoring system of the surge protector of claim 5, wherein the data preprocessing module comprises:

8. The life monitoring system of the surge protector of claim 5, wherein the pre-warning module comprises:

the measure processing module is used for: and feeding back the evaluation report and the maintenance proposal to a user through a prediction mechanism, and taking corresponding actions after the user receives the evaluation report and the maintenance proposal.