A new energy power station intelligent inspection method and system based on the Internet of Things

Abstract

The present invention discloses a method and system for intelligent inspection of new energy power stations based on the Internet of Things. The method includes: obtaining regional layout parameters and equipment distribution parameters of the new energy power station, and deploying sensors according to the regional layout parameters and equipment distribution parameters; using the deployed sensors to collect working index data and working environment data of the working equipment of the new energy power station, and performing fault prediction and abnormality detection on the working equipment based on the edge computing device and generating a first inspection report; collecting the real-time working data of each working equipment and uploading it to the central server through a set standard communication protocol to perform potential fault assessment and generate a second inspection report; integrating the inspection reports to determine the early warning information and push it visually. The distance between the data source and the analysis end can be shortened, thereby reducing the delay in data transmission, and abnormality detection of the working equipment can be realized at the first time. The inspection quality, reliability and practicality are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment inspection, and in particular to an intelligent inspection method and system for a new energy power station based on the Internet of Things. Background Art

[0002] At present, as an important area of ​​future energy supply, the stability and safety of the operation status of new energy power stations have attracted much attention. However, due to the large number of new energy power station equipment, wide distribution, and complex structure, regular daily inspections are required to ensure the normal operation of equipment and stations. The existing inspection methods are all through manual inspections, which not only increases labor costs but also makes the inspection results seriously subjective, reducing the inspection efficiency. Therefore, scientists have proposed to collect equipment operation data through servers for evaluation. However, due to the different volumes of operation data of each device and the different time to upload to the server, it is impossible to troubleshoot potential faults or equipment abnormalities in the first time, reducing the quality and practicality of the inspection and the reliability of the inspection. Summary of the invention

[0003] In response to the above-mentioned problems, the present invention provides an intelligent inspection method and system for new energy power stations based on the Internet of Things to solve the problem mentioned in the background technology that due to the different volumes of operating data of various devices and the different upload times to the server, potential faults or abnormalities of the equipment cannot be checked in the first time, thereby reducing the inspection quality, practicality and reliability.

[0004] An intelligent inspection method for a new energy power station based on the Internet of Things comprises the following steps:

[0005] Obtain regional layout parameters and equipment distribution parameters of new energy power stations, and deploy sensors based on the regional layout parameters and equipment distribution parameters;

[0006] Use the deployed sensors to collect the working index data and working environment data of the working equipment of the new energy power station, perform fault prediction and abnormality detection on the working equipment based on the edge computing device, and generate the first inspection report;

[0007] Collect the real-time working data of each working device and upload it to the central server through the set standard communication protocol to evaluate potential faults and generate the second inspection report;

[0008] The first inspection report and the second inspection report are integrated to determine the early warning information and push it visually.

[0009] Preferably, the acquiring of regional layout parameters and equipment distribution parameters of the new energy power station and the deployment of sensors according to the regional layout parameters and equipment distribution parameters include:

[0010] Obtain the design documents of the new energy power station, determine the distribution parameters of the working area of ​​the new energy power station according to the design documents, and collect the distribution parameters of the equipment in each working area;

[0011] Determine the environmental extreme index of each working area according to the working area distribution parameters, determine the environmental interference factors of each working area based on the environmental extreme index, and determine the anti-interference capability requirements of the sensor in each working area according to the environmental interference factors;

[0012] Obtain the equipment type of each working area, and determine the sensor accuracy requirements and sensor installation position requirements of each working area according to the equipment type and the spatial parameters of the working area;

[0013] Select appropriate sensors based on the sensor anti-interference capability requirements and sensor accuracy requirements of each working area, and deploy sensors in each working area based on the sensor installation location requirements.

[0014] Preferably, the method further comprises:

[0015] Encrypt the data collected by the sensor;

[0016] Obtain the user's system access request, and perform triple identity authentication on the user based on the system access request to determine the legitimacy of the user's access;

[0017] The encrypted sensor data will be displayed and reviewed based on the user's access legitimacy.

[0018] Among them, triple identity authentication includes: password-based authentication, digital certificate-based authentication, and unified identity authentication;

[0019] Preferably, the method of using the deployed sensors to collect the working index data and working environment data of the working equipment of the new energy power station and performing fault prediction and abnormality detection on the working equipment based on the edge computing device and generating a first inspection report includes:

[0020] Determine the detection data type of each sensor, determine the synchronous detection work index item and the detection work environment data item according to the detection data type, and use the deployed sensors to collect the work index data and work environment data of the working equipment of the new energy power station based on the synchronous detection work index item and the detection work environment data item;

[0021] Use edge computing devices to clean and preprocess work indicator data and work environment data and extract key feature information;

[0022] Based on key feature information, the deep learning model of edge computing devices is used for in-depth analysis, and device abnormality indicators are determined based on the analysis results;

[0023] Determine abnormal functions and potential faults of working equipment based on equipment abnormality indicators, and generate a first inspection report based on the abnormal functions and potential faults.

[0024] Preferably, before collecting the real-time working data of each working device and uploading it to the central server through a set standard communication protocol to perform potential fault assessment and generate the second inspection report, the method further includes:

[0025] Determine the supportable communication modes of each working device, and define the standard communication protocol between the central server and each working device based on the supportable communication modes;

[0026] Test the data delay transmission rate and data packet loss rate under the standard communication protocol, and determine the data transmission standard of each working device based on the data delay transmission rate, data packet loss rate and the working data volume of each working device;

[0027] Determine the time parameters for synchronous data transmission of all working devices according to the data transmission standard, and set the evaluation period according to the time parameters.

[0028] Preferably, before collecting the real-time working data of each working device and uploading it to the central server through a set standard communication protocol to perform potential fault assessment and generate the second inspection report, the method further includes:

[0029] Collect historical operation data of new energy power stations, including operation data under normal conditions and operation data under fault conditions, clean, annotate and divide the historical operation data to form a training data set, a data verification feature set and a test task set; select a suitable deep learning model structure and design the input layer, hidden layer and output layer of the model, and determine the parameters and hyperparameters of the model;

[0030] Train the model using the training set. Update the model's weights and parameters through the back-propagation algorithm and optimizer;

[0031] Use the validation set to evaluate the trained model and calculate the model's accuracy, recall, and F1 value. Fine-tune the model based on the evaluation results and deploy the fine-tuned model.

[0032] Preferably, the real-time working data of each working device is collected and uploaded to the central server through a set standard communication protocol to perform potential fault assessment and generate a second inspection report, including:

[0033] The real-time working data of each working device is collected through the data receiving and buffering mechanism and uploaded to the central server through the set standard communication protocol;

[0034] On the central server, remove noise and outliers from the real-time working data of each working device and perform necessary format conversion and standardization processing;

[0035] Use the trained model to infer and calculate the real-time working data processed by each working device;

[0036] According to the inference and calculation results, the potential failure mode, failure type and failure characteristics of each working equipment are determined and a second inspection report is generated.

[0037] Preferably, the step of integrating the first inspection report and the second inspection report to determine the warning information and push it visually includes:

[0038] Integrate the non-duplicate items in the first inspection report and the second inspection report to determine the statistical parameters of potential failures of the working equipment;

[0039] Determine the failure cost parameter of each potential failure, and determine the warning urgency of each working equipment according to the failure cost parameter;

[0040] Generate emergency warning information and non-emergency warning information according to the warning urgency, and push the emergency warning information to online operation and maintenance personnel through all preset visualization methods;

[0041] The non-emergency warning information is pushed to the terminal where the chief engineer of the operation and maintenance personnel is located through a preset visual method.

[0042] Preferably, the password-based authentication is as follows: when the user device accesses the central server, the system requires the user to set a password and stores it in the database. The system provides an API interface for processing user registration, login and password verification requests. When data is transmitted through the interface, the input user name and password are compared with the stored password in the database through the underlying program setting. If they match, the authentication is passed;

[0043] Digital certificate-based authentication specifically includes: generating key pairs based on public key infrastructure technology, creating certificate signing requests, verifying and signing certificates, distributing certificates, and verifying certificates.

[0044] Unified identity authentication is specifically: Unified identity authentication is a user identity authentication solution that shares authentication information between different applications and platforms. After obtaining the user's identity login information in a certain application, the user can access other applications without having to enter the password or perform identity authentication again.

[0045] An intelligent inspection system for new energy power stations based on the Internet of Things, the system comprising:

[0046] A deployment module is used to obtain regional layout parameters and equipment distribution parameters of new energy power stations, and to deploy sensors based on the regional layout parameters and equipment distribution parameters;

[0047] A first generation module is used to collect working index data and working environment data of working equipment of the new energy power station by using deployed sensors, and to perform fault prediction and abnormality detection on the working equipment based on edge computing devices and generate a first inspection report;

[0048] The second generation module is used to collect the real-time working data of each working device and upload it to the central server through a set standard communication protocol to perform potential fault assessment and generate a second inspection report;

[0049] The push module is used to integrate the first inspection report and the second inspection report to determine the warning information and push it visually.

[0050] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0051] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0053] Figure 1 This is a workflow diagram of an intelligent inspection method for a new energy power station based on the Internet of Things provided by the present invention;

[0054] Figure 2 Another work flow chart of an intelligent inspection method for a new energy power station based on the Internet of Things provided by the present invention;

[0055] Figure 3 Another working flow chart of an intelligent inspection method for a new energy power station based on the Internet of Things provided by the present invention;

[0056] Figure 4 This is a structural schematic diagram of an intelligent inspection system for new energy power stations based on the Internet of Things provided by the present invention. DETAILED DESCRIPTION

[0057] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0058] At present, as an important area of ​​future energy supply, the stability and safety of the operating status of new energy power stations have attracted much attention. However, due to the large number of new energy power station equipment, wide distribution, and complex structure, regular daily inspections are required to ensure the normal operation of equipment and stations. The existing inspection methods are all through manual inspections, which not only increases labor costs but also makes the inspection results seriously subjective, reducing the inspection efficiency. Therefore, scientists have proposed to collect equipment operation data through servers for evaluation. However, due to the different volumes of operating data of each device and the different time it takes to upload to the server, it is impossible to troubleshoot potential faults or abnormalities of the equipment in the first time, reducing the quality and practicality of the inspection as well as the reliability of the inspection. In order to solve the above problems, this embodiment discloses a method for inspecting new energy power station equipment by combining edge computing device detection and server monitoring.

[0059] An intelligent inspection method for new energy power stations based on the Internet of Things, such as Figure 1 As shown, the following steps are included:

[0060] Step S101, obtaining regional layout parameters and equipment distribution parameters of a new energy power station, and deploying sensors according to the regional layout parameters and equipment distribution parameters;

[0061] Step S102: Using the deployed sensors to collect working index data and working environment data of the working equipment of the new energy power station, and performing fault prediction and abnormality detection on the working equipment based on the edge computing device and generating a first inspection report;

[0062] Step S103: collect the real-time working data of each working device and upload it to the central server through a set standard communication protocol to perform potential fault assessment and generate a second inspection report;

[0063] Step S104: Integrate the first inspection report and the second inspection report to determine the warning information and push it visually.

[0064] In this embodiment, the regional layout parameters are represented by the layout parameters of various working systems in the construction area of ​​the new energy power station, including: power generation system, sewage treatment system, etc.;

[0065] In this embodiment, the equipment distribution parameters are represented by the working equipment distribution parameters within each regional working system;

[0066] In this embodiment, the working index data is represented by monitoring index data of the working equipment when it is in working state, such as working temperature, water flow, etc.;

[0067] In this embodiment, the working environment data is represented by monitoring environment index data of the working equipment when it is in working state, such as ambient temperature and ambient humidity.

[0068] The working principle of the above technical solution is: obtain the regional layout parameters and equipment distribution parameters of the new energy power station, and deploy sensors according to the regional layout parameters and equipment distribution parameters; use the deployed sensors to collect the working index data and working environment data of the working equipment of the new energy power station, and perform fault prediction and abnormality detection on the working equipment based on the edge computing device and generate a first inspection report; collect the real-time working data of each working equipment and upload it to the central server through the set standard communication protocol to perform potential fault assessment and generate a second inspection report; integrate the first inspection report and the second inspection report to determine the early warning information and push it visually.

[0069] The beneficial effects of the above technical solution are as follows: by setting up a combination of edge computing device detection and server monitoring to conduct equipment inspection at home, the distance between the data source and the analysis end can be shortened, thereby reducing the delay in data transmission, and abnormalities of working equipment can be checked at the first time. At the same time, the central server can also accurately conduct secondary inspections on the working equipment according to its operating data to ensure the accuracy of the inspection results, thereby improving the inspection quality, reliability and practicality, achieving automation and intelligence, reducing manual intervention, and improving inspection efficiency. It solves the problem mentioned in the prior art that due to the different volumes of operating data of each device and the different upload times to the server, it is impossible to check for potential faults or abnormalities of the equipment at the first time, thereby reducing the inspection quality, practicality and reliability.

[0070] In this embodiment, after the first inspection report and the second inspection report are integrated to determine the warning information and push it visually, the following is also included:

[0071] Generate operation and maintenance instructions based on early warning information, configure operation and maintenance personnel and equipment based on the operation and maintenance instructions, generate an operation and maintenance scheduling plan based on the configuration results, and shut down the target working equipment with potential faults through the operation and maintenance scheduling plan;

[0072] Collect environmental parameters in the area where the target working equipment is located, and determine the gas atomization condition in the area where the target working equipment is located according to the environmental parameters;

[0073] Determine the visibility change trend in the area where the target working equipment is located according to the gas atomization situation, determine the line of sight blockage rate in the area where the target working equipment is located based on the visibility change trend, and determine the image clarity of the operation and maintenance monitoring image based on the line of sight blockage rate;

[0074] Adjust the monitoring image resolution of the monitoring equipment based on the image clarity, and extract the key frame sequence of the operation and maintenance monitoring image based on the spatial self-attention mechanism according to the adjustment result;

[0075] Determine a state feature change sequence of the target working equipment according to the key frame sequence, and determine an operation scale parameter of the operation and maintenance task of the target working equipment according to the state feature change sequence;

[0076] Determine the expected operation and maintenance duration scale parameter and the expected operation and maintenance operation scale parameter based on the operation and maintenance scheduling plan, and compare the expected operation and maintenance operation scale parameter with the expected operation and maintenance operation scale parameter to determine the abnormal operation parameter;

[0077] Determine the abnormal operation item based on the abnormal operation parameters, obtain the target operation point of the abnormal operation item, and perform fixed-point operation and maintenance on the target operation point;

[0078] Obtain the target operation and maintenance time scale parameter of the target operation point according to the expected operation and maintenance time scale parameter, and determine the actual operation and maintenance time scale parameter of the target operation point according to the target operation and maintenance time scale parameter and the time scale parameter required for secondary operation and maintenance of the target operation point;

[0079] The progress delay label of the target operation point is determined according to the actual operation and maintenance time scale parameter, and an operation and maintenance delay report is generated according to the progress delay label and uploaded to the central server.

[0080] The beneficial effects of the above technical solution are: it can not only realize stable and effective monitoring of the target working equipment during the operation and maintenance process, but also quickly and accurately determine the erroneous operation parameters during the inspection process to ensure the success rate and efficiency of operation and maintenance, but also dynamically update the operation and maintenance progress of the target working equipment so that the target working equipment can be started to work as soon as possible, reducing cost losses.

[0081] In one embodiment, Figure 2 As shown, the obtaining of regional layout parameters and equipment distribution parameters of the new energy power station and the deployment of sensors according to the regional layout parameters and equipment distribution parameters include:

[0082] Step S201, obtaining the design file of the new energy power station, determining the distribution parameters of the working area of ​​the new energy power station according to the design file, and collecting the distribution parameters of the equipment in each working area;

[0083] Step S202: determining an environmental extreme index of each working area according to the working area distribution parameter, determining an environmental interference factor of each working area based on the environmental extreme index, and determining an anti-interference capability requirement of a sensor in each working area according to the environmental interference factor;

[0084] Step S203: Acquire the device type of each working area, and determine the sensor accuracy requirement and sensor installation position requirement of each working area according to the device type and the spatial parameters of the working area;

[0085] Step S204: Select an adaptive sensor according to the sensor anti-interference capability requirement and sensor accuracy requirement of each working area, and deploy the sensor in each working area according to the sensor installation position requirement.

[0086] In this embodiment, the environmental extreme index is represented by the index size of the extreme environment defined in each working area due to large temperature fluctuations or excessive chemical content in the area. The larger the index, the more extreme the environment.

[0087] In this embodiment, the environmental interference factors are represented by interference factors that affect the data collection work and data collection accuracy of the sensor in each working area.

[0088] The beneficial effects of the above technical solution are: the equipment distribution parameters in each working area are collected according to the working area distribution parameters of the new energy power station, and the sensor anti-interference capability requirements are determined based on the environmental interference factors in each working area, so as to obtain adapted sensors, which can ensure that data can still be accurately collected in extreme environments, reducing the probability of reduced sensor performance and stability in extreme environments. At the same time, reasonable deployment of sensors can achieve full coverage of new energy power stations.

[0089] In one embodiment, the method further comprises:

[0090] Encrypt the data collected by the sensor;

[0091] Obtain the user's system access request, and perform triple identity authentication on the user based on the system access request to determine the user's access legitimacy;

[0092] The encrypted sensor data is displayed and reviewed according to the user's access legitimacy;

[0093] Among them, triple identity authentication includes: password-based authentication, digital certificate-based authentication and unified identity authentication.

[0094] The beneficial effects of the above technical solution are: encrypting the collected data to ensure the security of data transmission, and at the same time, performing triple identity authentication on users based on their system access requests to ensure that only legitimate users or devices can access the system or data, preventing illegal access and data tampering.

[0095] In one embodiment, the method of using the deployed sensors to collect the working index data and working environment data of the working equipment of the new energy power station and performing fault prediction and abnormality detection on the working equipment based on the edge computing device and generating a first inspection report includes:

[0096] Determine the detection data type of each sensor, determine the synchronous detection work index items and the detection work environment data items according to the detection data type, and use the deployed sensors to collect the work index data and working environment data of the new energy power station working equipment based on the synchronous detection work index items and the detection work environment data items.

[0097] Use edge computing devices to clean and preprocess work indicator data and work environment data and extract key feature information;

[0098] Based on key feature information, the deep learning model of edge computing devices is used for in-depth analysis, and device abnormality indicators are determined based on the analysis results;

[0099] Determine abnormal functions and potential faults of working equipment based on equipment abnormality indicators, and generate a first inspection report based on the abnormal functions and potential faults.

[0100] The beneficial effects of the above technical solution are: through the edge computing device, the work indicator data and work environment data are cleaned and preprocessed and key feature information is extracted to confirm the abnormal indicators of the equipment, thereby obtaining the abnormal functions and potential failures of the working equipment, and generating the first inspection report. It can move the calculation and data storage to the edge of the network, thereby reducing the delay and bandwidth requirements of data transmission. At the same time, the intelligent inspection system can achieve faster response speed and higher processing efficiency, reduce the demand for data transmission to the central server, and reduce the pressure on network bandwidth and storage resources.

[0101] In one embodiment, before collecting the real-time working data of each working device and uploading it to the central server through a set standard communication protocol for potential fault assessment and generating a second inspection report, the method further includes:

[0102] Determine the supportable communication modes of each working device, and define the standard communication protocol between the central server and each working device based on the supportable communication modes;

[0103] Test the data delay transmission rate and data packet loss rate under the standard communication protocol, and determine the data transmission standard of each working device based on the data delay transmission rate, data packet loss rate and the working data volume of each working device;

[0104] Determine the time parameters for synchronous data transmission of all working devices according to the data transmission standard, and set the evaluation period according to the time parameters.

[0105] The beneficial effects of the above technical solution are: obtaining the data delay transmission rate and data packet loss rate under the standard communication protocol, determining the data transmission standard of each working device in combination with the working data volume of each working device, and setting the evaluation cycle, which can ensure the accuracy of the evaluation results and the real-time nature of the evaluation time. At the same time, it overcomes the problem of slow or unstable transmission speed when transmitting large amounts of data, ensuring the real-time and reliability of the data and the compatibility of each device during the data transmission process.

[0106] In one embodiment, before collecting the real-time working data of each working device and uploading it to the central server through a set standard communication protocol for potential fault assessment and generating a second inspection report, the method further includes:

[0107] Collecting historical operation data of the new energy power station, the historical operation data includes operation data in a normal state and operation data in a fault state, cleaning, labeling and dividing the historical operation data to form a training set, a verification set and a test set;

[0108] According to data characteristics and task requirements, select the appropriate deep learning model structure and design the model's input layer, hidden layer, and output layer, and determine the model's parameters and hyperparameters;

[0109] Train the model using the training set. Update the model's weights and parameters through the back-propagation algorithm and optimizer;

[0110] Use the validation set to evaluate the trained model and calculate the model's accuracy, recall, and F1 value. Fine-tune the model based on the evaluation results and deploy the fine-tuned model.

[0111] The beneficial effects of the above technical solution are: by preprocessing historical operation data, selecting a suitable deep learning model structure, setting multiple layers of the model, and training and evaluating the model, the accuracy and generalization ability of the model can be improved. At the same time, a lightweight model structure is adopted to reduce computing resource requirements.

[0112] In one embodiment, Figure 3 As shown, the real-time working data of each working device is collected and uploaded to the central server through the set standard communication protocol for potential fault assessment and generation of the second inspection report, including:

[0113] Step S301, collecting the real-time working data of each working device through the data receiving and buffering mechanism and uploading it to the central server through the set standard communication protocol;

[0114] Step S302: remove noise and abnormal values ​​from the real-time working data of each working device on the central server and perform necessary format conversion and standardization processing;

[0115] Step S303: inferring and calculating the real-time working data processed by each working device through the trained model;

[0116] Step S304: determine the potential failure mode, failure type and failure characteristics of each working device according to the reasoning and calculation results and generate a second inspection report.

[0117] The beneficial effects of the above technical solution are: by adopting an efficient data receiving and buffering mechanism, the continuity and integrity of the data can be ensured. Furthermore, by preprocessing the real-time working data, the quality of the data can be improved. At the same time, the real-time working data processed by each working device is inferred and calculated through the trained model, and the second inspection report is generated in combination with the failure mode and failure type of the working equipment. This can ensure the credibility of the inspection report, timely discover and solve potential fault problems, and improve the safety of the system.

[0118] In one embodiment, the step of integrating the first inspection report and the second inspection report to determine the warning information and push it visually includes:

[0119] Integrate the non-duplicate items in the first inspection report and the second inspection report to determine the statistical parameters of potential failures of the working equipment;

[0120] Determine the failure cost parameter of each potential failure, and determine the warning urgency of each working equipment according to the failure cost parameter;

[0121] Generate emergency warning information and non-emergency warning information according to the warning urgency, and push the emergency warning information to online operation and maintenance personnel through all preset visualization methods;

[0122] The non-emergency warning information is pushed to the terminal where the chief engineer of the operation and maintenance personnel is located through a preset visual method.

[0123] The beneficial effects of the above technical solution are: the warning urgency of each working equipment is determined according to the failure cost parameters of the potential failure of the working equipment, and emergency warning information and non-emergency warning information are generated and pushed using a preset visualization method. Operation and maintenance personnel can view the operating status of the new energy power station in real time, discover and deal with potential problems in a timely manner, reduce the impact of failures on the operation of the new energy power station, and improve the real-time and efficiency of the inspection system.

[0124] In one embodiment, password-based authentication is specifically as follows: when a user device accesses a central server, the system requires the user to set a password and stores it in a database. The system provides an API interface for processing user registration, login, and password verification requests. When data is transmitted through the interface, the input user name and password are compared with the stored password in the database through the underlying program settings. If they match, the authentication is passed;

[0125] Digital certificate-based authentication specifically includes: generating key pairs based on public key infrastructure technology, creating certificate signing requests, verifying and signing certificates, distributing certificates, and verifying certificates.

[0126] Unified identity authentication is specifically: Unified identity authentication is a user identity authentication solution that shares authentication information between different applications and platforms. After obtaining the user's identity login information in a certain application, the user can access other applications without having to enter the password or perform identity authentication again.

[0127] The beneficial effects of the above technical solution are: authenticating the user identity according to different authentication methods can improve the diversity of identity authentication, improve the security and privacy of the system and data, and avoid illegal access and tampering.

[0128] In one embodiment, this embodiment also discloses an intelligent inspection system for new energy power stations based on the Internet of Things, such as Figure 4 As shown, the system includes:

[0129] A deployment module 401 is used to obtain regional layout parameters and equipment distribution parameters of the new energy power station, and to deploy sensors according to the regional layout parameters and equipment distribution parameters;

[0130] The first generation module 402 is used to collect working index data and working environment data of working equipment of the new energy power station by using deployed sensors, and perform fault prediction and abnormality detection on the working equipment based on the edge computing device and generate a first inspection report;

[0131] The second generation module 403 is used to collect the real-time working data of each working device and upload it to the central server through a set standard communication protocol to perform potential fault assessment and generate a second inspection report;

[0132] The push module 404 is used to integrate the first inspection report and the second inspection report to determine the warning information and push it visually.

[0133] The working principle and beneficial effects of the above technical solution have been explained in the method embodiment and will not be repeated here.

[0134] Those skilled in the art should understand that the first and second in the present invention merely refer to different application stages.

[0135] Other embodiments of the present disclosure will be readily apparent to those skilled in the art after considering the specification and practicing the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and examples are to be considered exemplary only, and the true scope and spirit of the present disclosure are indicated by the following claims.

[0136] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. An intelligent inspection method for new energy power stations based on the Internet of Things, characterized in that: The following steps are involved: Obtain regional layout parameters and equipment distribution parameters of new energy power stations, and deploy sensors based on the regional layout parameters and equipment distribution parameters; Use the deployed sensors to collect the working index data and working environment data of the working equipment of the new energy power station, perform fault prediction and abnormality detection on the working equipment based on the edge computing device, and generate the first inspection report; Collect the real-time working data of each working device and upload it to the central server through the set standard communication protocol to evaluate potential faults and generate the second inspection report; The first inspection report and the second inspection report are integrated to determine the early warning information and push it visually.

2. According to the method for intelligent inspection of new energy power stations based on the Internet of Things according to claim 1, it is characterized in that: The obtaining of regional layout parameters and equipment distribution parameters of the new energy power station and the deployment of sensors according to the regional layout parameters and equipment distribution parameters include: Obtain the design documents of the new energy power station, determine the distribution parameters of the working area of ​​the new energy power station according to the design documents, and collect the distribution parameters of the equipment in each working area; Determine the environmental extreme index of each working area according to the working area distribution parameters, determine the environmental interference factors of each working area based on the environmental extreme index, and determine the anti-interference capability requirements of the sensor in each working area according to the environmental interference factors; Obtain the equipment type of each working area, and determine the sensor accuracy requirements and sensor installation position requirements of each working area according to the equipment type and the spatial parameters of the working area; Select appropriate sensors based on the sensor anti-interference capability requirements and sensor accuracy requirements of each working area, and deploy sensors in each working area based on the sensor installation location requirements.

3. According to the method for intelligent inspection of new energy power stations based on the Internet of Things according to claim 1, it is characterized in that: The method further comprises: Encrypt the data collected by the sensor; Obtain the user's system access request, and perform triple identity authentication on the user based on the system access request to determine the legitimacy of the user's access; The encrypted sensor data is displayed and reviewed according to the user's access legitimacy; Among them, triple identity authentication includes: password-based authentication, digital certificate-based authentication and unified identity authentication.

4. According to the method for intelligent inspection of new energy power stations based on the Internet of Things according to claim 1, it is characterized in that: The method of using the deployed sensors to collect the working index data and working environment data of the working equipment of the new energy power station and performing fault prediction and abnormality detection on the working equipment based on the edge computing device and generating a first inspection report includes: Determine the detection data type of each sensor, determine the synchronous detection work index items and the detection work environment data items according to the detection data type, and use the deployed sensors to collect the work index data and working environment data of the new energy power station working equipment based on the synchronous detection work index items and the detection work environment data items. Use edge computing devices to clean and preprocess work indicator data and work environment data and extract key feature information; Based on key feature information, the deep learning model of edge computing devices is used for in-depth analysis, and device abnormality indicators are determined based on the analysis results; Determine abnormal functions and potential faults of working equipment based on equipment abnormality indicators, and generate a first inspection report based on the abnormal functions and potential faults.

5. According to the method for intelligent inspection of new energy power stations based on the Internet of Things according to claim 1, it is characterized in that: Before collecting the real-time working data of each working device and uploading it to the central server through the set standard communication protocol for potential fault assessment and generating the second inspection report, it also includes: Determine the supportable communication modes of each working device, and define the standard communication protocol between the central server and each working device based on the supportable communication modes; Test the data delay transmission rate and data packet loss rate under the standard communication protocol, and determine the data transmission standard of each working device based on the data delay transmission rate, data packet loss rate and the working data volume of each working device; Determine the time parameters for synchronous data transmission of all working devices according to the data transmission standard, and set the evaluation period according to the time parameters.

6. According to the method for intelligent inspection of new energy power stations based on the Internet of Things in claim 1, it is characterized in that: Before collecting the real-time working data of each working device and uploading it to the central server through the set standard communication protocol for potential fault assessment and generating the second inspection report, it also includes: Collecting historical operation data of the new energy power station, the historical operation data includes operation data in a normal state and operation data in a fault state, cleaning, labeling and dividing the historical operation data to form a training set, a verification set and a test set; According to data characteristics and task requirements, select the appropriate deep learning model structure and design the model's input layer, hidden layer, and output layer, and determine the model's parameters and hyperparameters; Train the model using the training set. Update the model's weights and parameters through the back-propagation algorithm and optimizer; Use the validation set to evaluate the trained model and calculate the model's accuracy, recall, and F1 value. Fine-tune the model based on the evaluation results and deploy the fine-tuned model.

7. The method for intelligent inspection of new energy power stations based on the Internet of Things according to claim 6 is characterized in that: Collect the real-time working data of each working device and upload it to the central server through the set standard communication protocol to evaluate potential faults and generate a second inspection report, including: The real-time working data of each working device is collected through the data receiving and buffering mechanism and uploaded to the central server through the set standard communication protocol; On the central server, remove noise and outliers from the real-time working data of each working device and perform necessary format conversion and standardization processing; Use the trained model to infer and calculate the real-time working data processed by each working device; According to the inference and calculation results, the potential failure mode, failure type and failure characteristics of each working equipment are determined and a second inspection report is generated.

8. The method for intelligent inspection of new energy power stations based on the Internet of Things according to claim 1 is characterized in that: The step of integrating the first inspection report and the second inspection report to determine the warning information and push it visually includes: Integrate the non-duplicate items in the first inspection report and the second inspection report to determine the statistical parameters of potential failures of the working equipment; Determine the failure cost parameter of each potential failure, and determine the warning urgency of each working equipment according to the failure cost parameter; Generate emergency warning information and non-emergency warning information according to the warning urgency, and push the emergency warning information to online operation and maintenance personnel through all preset visualization methods; The non-emergency warning information is pushed to the terminal where the chief engineer of the operation and maintenance personnel is located through a preset visual method.

9. The method for intelligent inspection of new energy power stations based on the Internet of Things according to claim 3 is characterized in that: Password-based authentication is as follows: when a user device accesses the central server, the system requires the user to set a password and store it in the database. The system provides an API interface for processing user registration, login, and password verification requests. When data is transmitted through the interface, the input user name and password are compared with the stored password in the database through the underlying program settings. If they match, the authentication is passed; Authentication based on digital certificates specifically includes: generating key pairs based on public key infrastructure technology, creating certificate signing requests, verifying and signing certificates, distributing certificates, and verifying certificates; Unified identity authentication is specifically: Unified identity authentication is a user identity authentication solution that shares authentication information between different applications and platforms. After obtaining the user's identity login information in a certain application, the user can access other applications without having to enter the password or perform identity authentication again.

10. An intelligent inspection system for new energy power stations based on the Internet of Things, characterized in that: The system includes: A deployment module is used to obtain regional layout parameters and equipment distribution parameters of the new energy power station, and to deploy sensors according to the regional layout parameters and equipment distribution parameters; A first generation module is used to collect working index data and working environment data of working equipment of the new energy power station by using deployed sensors, and to perform fault prediction and abnormality detection on the working equipment based on edge computing devices and generate a first inspection report; The second generation module is used to collect the real-time working data of each working device and upload it to the central server through a set standard communication protocol to perform potential fault assessment and generate a second inspection report; The push module is used to integrate the first inspection report and the second inspection report to determine the warning information and push it visually.

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