Photovoltaic power station operation and maintenance management system and method based on big data

Abstract

The invention discloses a photovoltaic power station operation and maintenance management system and method based on big data, and relates to the field of photovoltaic power generation, and the method comprises the steps: collecting the operation data of a photovoltaic module, carrying out the preprocessing of the data through an edge computing device, and storing the data to a cloud big data platform; based on the stored photovoltaic module operation data, establishing a photovoltaic power station power generation behavior basic model by using a self-supervised learning method; performing traceability analysis on abnormal power generation behaviors in the distributed account book in combination with historical data, and triggering an operation and maintenance task by using an intelligent contract; after the operation and maintenance task is executed, the processed abnormal power generation behavior is fed back to the self-supervised learning model sample library, and the model is dynamically adjusted; based on the operation and maintenance task execution content, an operation and maintenance report is generated and uploaded to the block chain; according to the method, the operation and maintenance task is automatically triggered through the intelligent contract, full-process automatic management from anomaly detection to task issuing is realized, and the operation and maintenance efficiency and the operation stability of the photovoltaic power station are improved.

Description

Technical Field

[0001] The present invention relates to the field of photovoltaic power generation, and in particular to a photovoltaic power station operation and maintenance management system and method based on big data. Background Art

[0002] With the transformation of the global energy structure, photovoltaic power generation, as a clean and renewable energy source, has developed rapidly in recent years and has gradually become an important part of energy production. However, the efficient operation of photovoltaic power stations not only depends on the performance of photovoltaic modules, but also needs to be guaranteed by proper operation and maintenance management to ensure its long-term stable operation. Traditional operation and maintenance management mainly relies on regular manual inspections and alarm systems based on simple rules. However, with the continuous expansion of the scale of photovoltaic power stations and the complexity of the operating environment, this method has been difficult to meet the requirements of modern photovoltaic power stations for efficiency, intelligence and automation. In recent years, with the rise of technologies such as big data, blockchain and machine learning, more and more research and applications have begun to explore how to introduce these cutting-edge technologies into the operation and maintenance management of photovoltaic power stations to improve data processing efficiency, anomaly detection accuracy and the intelligence level of operation and maintenance decision-making.

[0003] Therefore, the present invention provides a photovoltaic power station operation and maintenance management system and method based on big data to solve the problem of insufficient accuracy of operation and maintenance anomaly detection. Although the existing photovoltaic power station operation and maintenance technology has achieved status monitoring and fault alarm of photovoltaic components to a certain extent, it still has many shortcomings. Most of the existing fault detection systems are based on preset simple rules, which are difficult to cope with the complex and changeable environmental factors in actual operation, resulting in insufficient accuracy and real-time performance of anomaly detection. In contrast, self-supervised learning can autonomously learn the potential structure of data by generating pseudo-labels from unlabeled data, dynamically adapt to different operating environments and improve the accuracy of anomaly detection. In addition, the existing operation and maintenance data management system usually relies on centralized databases for storage, and the security, transparency and traceability of operation and maintenance data are relatively limited. Especially when multiple stakeholders are involved or operation and maintenance data needs to be displayed to the outside, the traditional centralized data storage method is prone to tampering problems and lacks a reliable third-party audit mechanism. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a photovoltaic power station operation and maintenance management system and method based on big data to solve the problems of insufficient accuracy of anomaly detection and limited security and transparency of operation and maintenance data.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a photovoltaic power station operation and maintenance management system and method based on big data, which includes collecting photovoltaic component operation data, pre-processing the data using edge computing equipment, and storing it on a cloud big data platform;

[0008] Based on the stored PV module operation data, a basic model of PV power station power generation behavior is established using self-supervised learning methods;

[0009] Compare the PV module operation data with the basic model of power generation behavior, identify abnormal power generation behavior, upload the abnormal power generation behavior to the blockchain, and record it in the distributed ledger;

[0010] Combined with historical data, the abnormal power generation behavior in the distributed ledger is traced and analyzed, and smart contracts are used to trigger operation and maintenance tasks;

[0011] After the operation and maintenance task is executed, the abnormal power generation behavior is fed back to the self-supervised learning model sample library to dynamically adjust the model;

[0012] Based on the execution content of the operation and maintenance tasks, an operation and maintenance report is generated and uploaded to the blockchain.

[0013] In the second aspect, the present invention provides a photovoltaic power station operation and maintenance management system and method based on big data, including a data acquisition module, a model construction module, an anomaly detection module, an anomaly tracing module, an operation and maintenance feedback module, and a report generation module; the data acquisition module is used to collect photovoltaic component operation data, use edge computing equipment to pre-process the data, and store it in a cloud big data platform; the model construction module is used to establish a basic model of photovoltaic power station power generation behavior based on the stored photovoltaic component operation data and a self-supervised learning method; the anomaly detection module is used to compare the photovoltaic component operation data and the basic model of power generation behavior, identify abnormal power generation behavior, upload the abnormal power generation behavior to the blockchain, and record it in a distributed ledger; the anomaly tracing module is used to combine historical data to trace the abnormal power generation behavior in the distributed ledger and trigger the operation and maintenance task using a smart contract; the operation and maintenance feedback module is used to feed back the processed abnormal power generation behavior to the self-supervised learning model sample library after the operation and maintenance task is executed, and dynamically adjust the model; the report generation module is used to generate an operation and maintenance report based on the execution content of the operation and maintenance task and upload it to the blockchain.

[0014] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, it implements any step of the photovoltaic power station operation and maintenance management system and method based on big data as described in the first aspect of the present invention.

[0015] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the photovoltaic power station operation and maintenance management system and method based on big data as described in the first aspect of the present invention.

[0016] The beneficial effects of the present invention are as follows: the present invention introduces self-supervised learning and blockchain technology into the photovoltaic power station operation and maintenance management system, forming an efficient and intelligent operation and maintenance management system. Through the self-supervised learning model, the system can automatically extract features from the photovoltaic component operation data and establish a basic model of power generation behavior, reducing the dependence on manually labeled data, and improving the generalization ability of the model and the accuracy of anomaly detection. The introduction of blockchain technology solves the data tampering and trust problems that may exist in traditional centralized data management systems, enhances the system's traceability capabilities, and realizes data transparency and credibility assurance. The operation and maintenance tasks are automatically triggered by smart contracts, realizing the full process automated management from anomaly detection to task issuance, improving the operation and maintenance efficiency and the operational stability of photovoltaic power stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0018] Figure 1 This is a flow chart of the photovoltaic power station operation and maintenance management system and method based on big data in Example 1.

[0019] Figure 2 This is a flow chart of the report generation module in Example 1. DETAILED DESCRIPTION

[0020] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0021] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0022] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0023] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, and provides a photovoltaic power station operation and maintenance management system and method based on big data, including:

[0024] S1: Collect PV module operation data, pre-process the data using edge computing devices, and store it on the cloud big data platform.

[0025] Specifically, the following steps are included:

[0026] S1.1: Deploy a sensor network in the PV power station to collect PV module operation data in real time.

[0027] Specifically, it is necessary to select sensors that support the RS485 communication protocol to collect the current, voltage, irradiance, temperature and humidity data of the photovoltaic modules.

[0028] One set of sensors is installed for each set of photovoltaic modules. The irradiance sensors should be placed in an unobstructed area and consistent with the inclination of the photovoltaic modules. At the same time, each sensor is given an independent number to facilitate subsequent data identification.

[0029] The communication connection is carried out through the RS485 protocol, each sensor is connected in series to the edge computing device, and a 120Ω terminal resistor is connected at the terminal.

[0030] Finally, calibrate the sensors and connect the debugging software to verify the communication stability of each sensor.

[0031] S1.2: Through edge computing devices, use noise filtering algorithms to remove outliers and noise interference, use wavelet transform to compress data, and output standardized data.

[0032] It should be noted that the data acquisition method is to install the Modbus reading software in the edge computing device (such as Raspberry Pi or industrial PC), set the collection interval to 10 seconds, poll all sensor data regularly, and record current, voltage, irradiance, temperature and humidity.

[0033] The noise filtering method is to use the moving average method to smooth short-term fluctuations for the five consecutive data points. Detect abnormal values ​​that exceed the physical reasonable range and remove them (for example, the current value is less than 0 or greater than the rated value of the equipment, or the irradiance detected at night is greater than 5W / m 2Wavelet transform can be called through the pywt library, and only the low-frequency signal part needs to be retained.

[0034] It should be understood that outputting standardized data means storing the processed data in the log backup in JSON format.

[0035] S1.3: Use MQTT protocol for data transmission and upload the processed standardized data to the cloud.

[0036] Specifically, configure the MQTT client (edge ​​device) and server (cloud). The client needs to set the Topic and QoS level, and publish the processed JSON data to the server at a regular interval. The server needs to set up Subscribe to receive the data stream and store the data in the database.

[0037] Preferably, through efficient and stable data collection, accurate data preprocessing, and reasonable data compression and transmission, not only the real-time, accuracy, and reliability of the overall solution are improved, but also the transmission cost and storage pressure are reduced. These technical means provide solid data support for the subsequent photovoltaic power station operation and maintenance management, performance optimization, predictive maintenance, etc., which helps to improve the operational efficiency and economic benefits of photovoltaic power stations.

[0038] S2: Based on the stored PV module operation data, a basic model of PV power station power generation behavior is established using self-supervised learning methods.

[0039] Specifically, the following steps are included:

[0040] S2.1: Perform multi-channel mapping and data augmentation on the standardized data.

[0041] It should be understood that multi-channel mapping is to ensure that each data channel can be independently used as an input feature.

[0042] Among them, data enhancement includes the use of interpolation, noise addition, random scaling and other methods.

[0043] S2.2: Use deep convolutional neural network to extract features from enhanced data.

[0044] Specifically, a convolution operation is applied to the enhanced data to extract the local features of each sensor. Then a pooling operation is applied to reduce the feature dimension and extract the feature map h.

[0045] S2.3: Use a two-layer fully connected neural network to map the features to the contrastive learning space and obtain the feature representation z, which is expressed as:

[0046] z=W2·ReLU(W1·h+b1)+b2;

[0047] Among them, h is the output of the feature extraction network, W1 is the weight matrix of the first layer, W2 is the weight matrix of the second layer, b1 is the bias term of the first layer fully connected network, and b2 is the bias term of the second layer fully connected network.

[0048] S2.4: Through the SimCLR contrast learning architecture, define the contrast loss function, which is expressed as:

[0049]

[0050] Among them, z i To enhance the feature representation of the view, z j For z i Corresponding positive sample pairs, τ is the temperature scaling factor, N is the number of samples, 1 [k≠i] is the indicator function, i is the sample size index coefficient, j is z i The corresponding positive sample pair index coefficient, k is the index coefficient of the negative sample pair, sim(z i ,z j ) is the cosine similarity.

[0051] The periodic enhancement factor C(t) is introduced to correct the cosine similarity, and the expression is:

[0052]

[0053] The periodic enhancement factor C(t) is expressed as:

[0054]

[0055] Among them, t is the current time, T is the daily cycle, α is the periodic amplitude correction parameter, and β is the balance factor.

[0056] S2.5: Use stochastic gradient descent optimization and dynamically adjust the learning rate.

[0057] Specifically, the learning rate expression is:

[0058]

[0059] Among them, η t is the learning rate of the tth iteration, η0 is the initial learning rate, and T is the total number of training rounds.

[0060] S2.6: Use the KNN clustering method to visualize the feature representation z and verify the separability of the embedded representation by projecting it into a two-dimensional space.

[0061] Among them, the feature representation z is projected into a two-dimensional space through the t-SNE dimensionality reduction technology to visualize the similarity and separability between samples.

[0062] The basic model of photovoltaic power station power generation behavior established by self-supervised learning method has comprehensively improved the intelligence level and accuracy of photovoltaic operation and maintenance management. Multi-channel mapping and data enhancement technology ensure the multi-dimensional information integrity of input data, and combine convolutional network and fully connected network to extract deep features and effectively describe the operating status of photovoltaic components. The introduction of SimCLR comparative learning architecture and periodic enhancement factor enhances the adaptability of the model to periodic characteristics and complex scenarios. Dynamic learning rate optimization improves training efficiency, while KNN clustering and t-SNE dimensionality reduction technology make feature embedding highly interpretable and visualized. The model provides a high-quality baseline for subsequent anomaly detection and improves the reliability of photovoltaic power station operation.

[0063] S3: Compare the PV module operation data with the basic model of power generation behavior, identify abnormal power generation behavior, upload the abnormal power generation behavior to the blockchain, and record it in the distributed ledger.

[0064] Specifically, the following steps are included:

[0065] S3.1: Collect the real-time photovoltaic module operation data x t , through the feature extraction network, obtain the feature representation z t ;

[0066] S3.2: Calculate the anomaly index A(z t ,z), the expression is:

[0067]

[0068] Among them, A(z t ,z) is the distance between the model prediction and the actual observation, z t is the feature vector set at the current time t (extracted from the real-time collected PV module data), z t,i is the feature vector of the i-th sample generated at the current time point t, z is the basic feature vector set learned in the model, and z i is the feature vector of the ith basic feature sample, ∥·∥2 is the Euclidean distance, which is used to measure the spatial difference between the two. is a similarity-based metric function used to supplement the distribution information that cannot be captured by the Euclidean distance, λ is a balance parameter, and N is the number of feature vector samples.

[0069] It should be noted that the similarity measurement function The expression is:

[0070]

[0071] This expression is used to measure the directional consistency between the current feature and the basic feature. The more the direction deviates (that is, the smaller the similarity), the more likely anomaly is to occur.

[0072] The balance parameter λ is a hyperparameter that adjusts the weights of the Euclidean distance and similarity metrics. If λ>1, the model relies more on similarity to judge anomalies, and if λ<1, the model prefers to use distance to evaluate anomalies.

[0073] S3.3: Comparison of abnormal index A(z t ,z) and set the abnormal judgment threshold θ to identify abnormal power generation behavior. Specifically, the expression of the abnormal judgment threshold θ is:

[0074] θ=μ+k·σ;

[0075] Among them, μ is the mean of the anomaly index A in the historical data, σ is the standard deviation of the anomaly index A in the historical data, k is a constant with a value range of 2 to 3. k=2 is used in scenarios with higher fault tolerance to reduce false alarms, and k=3 is used in strict scenarios to enhance the ability to identify abnormal behaviors.

[0076] Contrast abnormal index A(z t ,z) and set the abnormal judgment threshold θ, when A(z t ,z)>θ, the current operating state is considered abnormal. t ,z)<θ, the current operating status is considered normal.

[0077] S3.4: Build a record of abnormal power generation behavior, create and call a smart contract, upload the abnormal power generation behavior to the blockchain, and generate a transaction hash value.

[0078] Among them, the created smart contract needs to include functions such as uploading abnormal records, storing the hash value of the record to the distributed ledger, and returning the transaction ID and block location of the successfully stored transaction.

[0079] It should be noted that the generated transaction hash value can be used to trace the uploaded abnormal records to ensure that the data cannot be tampered with.

[0080] Preferably, an efficient, intelligent and secure photovoltaic power station operation and maintenance system is built by integrating anomaly detection algorithm, dynamic threshold adjustment, SHA-256 hash encryption and blockchain storage technology. This method not only improves the accuracy of abnormal behavior identification, but also realizes data immutability, convenient tracking and transparent management through blockchain, significantly reduces operation and maintenance costs, enhances system reliability, and provides a strong guarantee for the large-scale development of the photovoltaic industry in the future.

[0081] S4: Combine historical data to trace the abnormal power generation behavior in the distributed ledger and use smart contracts to trigger operation and maintenance tasks.

[0082] Specifically, the following steps are included:

[0083] S4.1: Extract the abnormal power generation behavior record R from the distributed ledger and collect the operating parameters of the PV power station during the period when the abnormal behavior occurred.

[0084] S4.2: Define the spatiotemporal association graph G = (V, E), where V is the set of abnormal event nodes, corresponding to each abnormal power generation behavior record R i , the edge set E is the spatiotemporal correlation between abnormal events, and the expression is:

[0085]

[0086] Among them, w ij is the association weight of the node, S ij is the similarity of feature vectors between nodes, θ ij is the angle difference of the node in the spatiotemporal distribution, γ is the similarity weight factor, κ is the similarity adjustment factor, δ is the similarity threshold, and φ is the geographical distribution correction factor.

[0087] S4.3: Construct a causal inference network N = (V, E, W) and calculate the causal weight matrix W.

[0088] Specifically, the causal weight matrix W is calculated based on historical abnormal records and operating data in combination with causal relationship modeling tools (such as Granger causal analysis).

[0089] S4.4: Using the causal weight matrix W and the traceability target node R t , determine the tracing path through the depth-first search algorithm.

[0090] S4.5: Design smart contract task allocation rules, expressed as:

[0091]

[0092] in, To assign priorities, w ij is the association weight of the corresponding node, c ij is the causal weight of the corresponding node, β(T e ) is the time priority function, and its expression is:

[0093]

[0094] Among them, is the time impact factor, T c is the current time, T e is the event time.

[0095] Furthermore, smart contract rules are written while ensuring that task priorities are dynamically matched with association weights, causal weights, and time factors.

[0096] S4.6: After the contract is triggered, operation and maintenance instructions are issued through the IoT device and cloud platform interface.

[0097] Specifically, the operation and maintenance instructions issued include component inspection, cleaning, maintenance, sensor calibration, etc., and are targeted at operation and maintenance personnel or designated PV components.

[0098] Preferably, by combining historical data with distributed ledgers, using smart contracts to trigger operation and maintenance tasks, the abnormal power generation behavior of photovoltaic power plants can be accurately traced and analyzed. The constructed spatiotemporal correlation graph and causal reasoning network help identify the correlation and causal relationship between abnormal events. Smart contracts dynamically allocate operation and maintenance tasks according to correlation weights, causal weights and time priorities to ensure that tasks are completed efficiently and accurately. Combining big data analysis with blockchain technology not only improves the accuracy of tracing abnormal events, but also enhances data security and system transparency. Ultimately, the system can achieve automated abnormal monitoring and operation and maintenance response, greatly improving the operation and maintenance efficiency of photovoltaic power plants.

[0099] S5: After the operation and maintenance task is executed, the processed abnormal power generation behavior is fed back to the self-supervised learning model sample library to dynamically adjust the model.

[0100] Specifically, the following steps are included:

[0101] S5.1: Extract the feedback data after the operation and maintenance task is processed and the abnormal data before processing, and format the data.

[0102] Specifically, the processed feedback data is recorded as x after , the abnormal data before processing is recorded as x before .

[0103] S5.2: Extract sample features through the feature extraction network, introduce the correction vector, map the data to the contrastive learning space, and obtain the corrected sample.

[0104] Specifically, the trained feature extraction network F(·) is used to extract features and obtain the feature vector z before , z after . Introduce the correction vector z fix , the expression is:

[0105] z fix =z after -z before ;

[0106] Perform data enhancement (such as noise perturbation, amplitude scaling, etc.) on the correction vector to generate multi-view features, denoted as z fix,aug .

[0107] The enhanced data is mapped to the contrastive learning space and mapped to the modified feature representation z through a two-layer fully connected network. proj, the expression is:

[0108] z proj =W2·ReLU(W1·z fix,aug +b1)+b2;

[0109] S5.3: Store the corrected samples into the self-supervised learning model library.

[0110] S5.4: Based on the updated sample library, re-perform self-supervised learning on the basic model.

[0111] Among them, the method of re-performing self-supervised learning is to minimize the distance between the corrected feature representation and the original abnormal feature, and the expression is:

[0112]

[0113] S5.5: Add the corrected sample weight factor to the original contrastive learning loss function and adjust the loss function.

[0114] Specifically, the modified sample weight factor is recorded as γ fix , the loss function expression is:

[0115]

[0116] S5.6: Dynamically adjust the learning rate based on the number of correction samples.

[0117] Specifically, the learning rate expression is:

[0118]

[0119] Among them, |D fix | is the corrected sample size, |D total | is the total sample size.

[0120] Preferably, through the closed-loop mechanism of operation and maintenance feedback, the model can continuously adapt to new abnormal behaviors and improve its performance in different operation and maintenance environments. The introduction of correction vectors and data enhancement methods enables the model to better learn and understand the abnormal repair process, thereby improving the repair accuracy of abnormal behaviors. Through multi-view learning and dynamic adjustment of learning rate, the model's ability to handle new types of anomalies is improved and the risk of overfitting is reduced. By dynamically adjusting the loss function weights and learning rates, effective learning progress and stability can be maintained during training. These effects jointly promote the continuous improvement of model performance and ensure that the model can always maintain high accuracy and stability during actual operation and maintenance.

[0121] S6: Generate an operation and maintenance report based on the execution content of the operation and maintenance task and upload it to the blockchain.

[0122] Specifically, the following steps are included:

[0123] S6.1: Collect operation and maintenance task execution data and generate operation and maintenance reports.

[0124] Among them, the operation and maintenance task execution data includes: power generation power and efficiency before and after processing, abnormality type, repair operation, repair result, task execution timestamp, location, equipment information and other related log data and diagnostic results.

[0125] The contents of the operation and maintenance report include: description and execution status of the operation and maintenance tasks; specific operation steps and results of abnormal repair; equipment and tools used in the operation and maintenance process; results and verification results after repair; metadata such as task execution time, personnel, and location.

[0126] S6.2: Digitally sign the operation and maintenance report using a private key.

[0127] S6.3: Call the blockchain interface, upload the operation and maintenance report to the distributed ledger, and obtain the transaction hash value.

[0128] S6.4: Store operation and maintenance reports and record the mapping relationship between report files and blockchain transaction hash values ​​in the database.

[0129] Preferably, through the above process, the operation and maintenance report not only provides high security, high transparency and non-tamperability in storage and verification, but also greatly improves data management efficiency, system automation and compliance. With the help of blockchain technology, the operation and maintenance report can be stored and managed in a transparent, verifiable and non-tamperable manner, which plays a significant role in improving operation and maintenance efficiency, ensuring data security, enhancing audit capabilities and improving compliance.

[0130] This embodiment also provides a photovoltaic power station operation and maintenance management system and method based on big data, including:

[0131] The data acquisition module is used to collect the operating data of photovoltaic modules, pre-process the data using edge computing devices, and store it on the cloud big data platform;

[0132] A model building module is used to build a basic model of the power generation behavior of a photovoltaic power station based on the stored photovoltaic module operation data using a self-supervised learning method;

[0133] The anomaly detection module is used to compare the PV module operation data with the basic model of power generation behavior, identify abnormal power generation behavior, upload the abnormal power generation behavior to the blockchain, and record it in the distributed ledger;

[0134] The abnormal tracing module is used to combine historical data to trace the abnormal power generation behavior in the distributed ledger and use smart contracts to trigger operation and maintenance tasks;

[0135] The operation and maintenance feedback module is used to feed back the abnormal power generation behavior to the self-supervised learning model sample library after the operation and maintenance task is executed, and dynamically adjust the model;

[0136] The report generation module is used to generate operation and maintenance reports based on the execution content of operation and maintenance tasks and upload them to the blockchain.

[0137] This embodiment also provides a computer device, which is suitable for the photovoltaic power station operation and maintenance management system and method based on big data, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the photovoltaic power station operation and maintenance management system and method based on big data as proposed in the above embodiments.

[0138] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0139] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the photovoltaic power station operation and maintenance management system and method based on big data as proposed in the above embodiment are implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0140] In summary, the present invention has the following advantages: The present invention introduces self-supervised learning and blockchain technology into the photovoltaic power station operation and maintenance management system to form an efficient and intelligent operation and maintenance management system. Through the self-supervised learning model, the system can automatically extract features from the photovoltaic component operation data and establish a basic model of power generation behavior, reducing the dependence on manually labeled data and improving the generalization ability of the model and the accuracy of anomaly detection. The introduction of blockchain technology solves the data tampering and trust problems that may exist in traditional centralized data management systems, enhances the system's traceability capabilities, and realizes data transparency and credibility assurance. The operation and maintenance tasks are automatically triggered by smart contracts, realizing the full process automated management from anomaly detection to task issuance, improving the operation and maintenance efficiency and the operational stability of photovoltaic power stations.

[0141] Example 2, referring to Table 1, is the second example of the present invention. To further verify the technical solution of the present invention, experimental simulation data of a photovoltaic power station operation and maintenance management system and method based on big data are provided.

[0142] The experiment selected six photovoltaic modules (numbered from module 1 to module 6) and collected their operating data in real time through the deployed sensor network. The raw data recorded by these sensors include voltage (V), current (A), temperature (℃), humidity (%), irradiance (W / m 2). The data is first preprocessed by the edge computing device, and the noise filtering algorithm is used to remove outliers and noise interference to ensure data quality; then the data is standardized to conform to a unified dimension; finally, the data is compressed using wavelet transform to reduce storage and transmission requirements while retaining key feature information. The standardized data is uploaded to the cloud big data platform and input into the self-supervised learning model for feature extraction. For each component, the anomaly index is calculated using a formula based on the current time point characteristics and the characteristics in the basic model of power generation behavior.

[0143] The sensor collected data is shown in Table 1 below:

[0144] Table 1 Sensor data collection table

[0145] Component Number Current (A) Voltage (V) <![CDATA[Irradiance (W / m 2 )]]> Temperature(℃) humidity(%) Component 1 9.5 230 950 28 60 Component 2 4.2 210.5 850 32.5 70 Component 3 10.1 235 1050 27.5 58 Component 4 12.3 240 1000 35 72 Component 5 8.9 225.5 920 29 65 Component 6 7.5 215 800 30 75

[0146] The data is fed into the self-supervised learning model to calculate the feature vector z of each component.

[0147] The characteristic vectors of the components are shown in Table 2 below:

[0148] Table 2 Component feature vector table

[0149]

[0150]

[0151] Calculate the abnormal index A(z t ,z), the expression is:

[0152]

[0153] The specific process is:

[0154] Calculate the Euclidean distance, the expression is,

[0155]

[0156] Calculate the similarity measure, the expression is,

[0157]

[0158]

[0159] z t ·z i =z t,1 ·z i,1 +z t,2 ·z i,2 +z t,3 ·z i,3 ;

[0160]

[0161] Calculate the anomaly index, the expression is,

[0162]

[0163] Get λ=1,z t =[0.50,0.60,0.55], taking component 1 and component 2 as examples, calculate the abnormal index A(z t ,z).

[0164] Component 1:

[0165] From Table 2, we can see that z1=[0.45, 0.60, 0.52].

[0166] Euclidean distance:

[0167]

[0168] Similarity measure:

[0169] z t ·z1=0.50·0.45+0.60·0.60+0.55·0.52=0.225+0.36+0.286=0.871;

[0171]

[0172] Abnormal index:

[0173]

[0174] Component 2:

[0175] From Table 2, we can see that z2=[1.30,1.60,1.50].

[0176] Euclidean distance:

[0177]

[0178] Similarity measure:

[0179] z t ·z2=0.50·1.30+0.60·1.60+0.55·1.50=0.65+0.96+0.825=2.435;

[0181]

[0182]

[0183] Abnormal index:

[0184]

[0185] The other components are calculated according to the above example.

[0186] The abnormal index of the components is shown in Table 3 below:

[0187] Table 3 Component abnormality index table

[0188] Component Number Abnormal Index Component 1 0.00147 Component 2 0.817 Component 3 0.00103 Component 4 0.00117 Component 5 0.0021 Component 6 0.6745

[0189] The expression of abnormal judgment threshold θ is:

[0190] θ=μ+k·σ;

[0191] Take the historical anomaly index A data, calculate the mean μ and standard deviation σ, and in the strict scenario of k=3, the anomaly judgment threshold θ is 0.439.

[0192] Compare the abnormal judgment threshold θ with the abnormal index of the component,

[0193] Component 1:

[0194] 0.00147<0.439;

[0195] Component 2:

[0196] 0.817>0.439;

[0197] Component 3:

[0198] 0.00103<0.439;

[0199] Component 4:

[0200] 0.00117<0.439;

[0201] Component 5:

[0202] 0.0021<0.439;

[0203] Component 6:

[0204] 0.06745>0.439;

[0205] The abnormality index of components 2 and 6 is greater than the threshold θ, indicating that their feature vectors are significantly different from the model prediction, reflecting that these components are faulty or have degraded performance. The abnormality index of other components is less than the threshold θ, indicating that these components are working normally.

[0206] Through the above steps and data tables, the experiment can be used to evaluate the operating status of PV panels and discover potential faults or performance degradation. After that, the specific fault content can be determined through source tracing analysis and operation and maintenance tasks can be assigned.

[0207] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A photovoltaic power station operation and maintenance management system and method based on big data, characterized by: include, Collect PV module operation data, pre-process the data using edge computing devices, and store it on the cloud big data platform; Based on the stored PV module operation data, a basic model of PV power station power generation behavior is established using self-supervised learning methods; Compare the PV module operation data with the basic model of power generation behavior, identify abnormal power generation behavior, upload the abnormal power generation behavior to the blockchain, and record it in the distributed ledger; Combined with historical data, the abnormal power generation behavior in the distributed ledger is traced and analyzed, and smart contracts are used to trigger operation and maintenance tasks; After the operation and maintenance task is executed, the abnormal power generation behavior is fed back to the self-supervised learning model sample library to dynamically adjust the model; Based on the execution content of the operation and maintenance tasks, an operation and maintenance report is generated and uploaded to the blockchain.

2. The photovoltaic power station operation and maintenance management system and method based on big data according to claim 1, characterized in that: The specific steps of collecting photovoltaic module operation data, pre-processing the data using edge computing equipment, and storing it on the cloud big data platform are as follows: Deploy sensor networks in photovoltaic power plants to collect photovoltaic module operation data in real time; Through edge computing devices, noise filtering algorithms are used to remove outliers and noise interference, and wavelet transforms are used for compression to output standardized operating data; The MQTT protocol is used for data transmission, and the processed standardized data is uploaded to the cloud.

3. The photovoltaic power station operation and maintenance management system and method based on big data according to claim 2, characterized in that: The basic model of photovoltaic power station power generation behavior is established based on the stored photovoltaic module operation data using a self-supervised learning method. The specific steps are: Perform multi-channel mapping and data augmentation on standardized data; Use deep convolutional neural network to extract features from enhanced data; Use a two-layer fully connected neural network to map the features to the contrastive learning space and obtain the feature representation z; Through SimCLR contrast learning architecture, define contrast loss function; Introduce the periodic enhancement factor C(t) to modify the cosine similarity; Periodic enhancement factor C(t); Use stochastic gradient descent optimization to dynamically adjust the learning rate; The feature representation z is visualized using the KNN clustering method, and the separability of the embedded representation is verified by projecting it into a two-dimensional space.

4. The photovoltaic power station operation and maintenance management system and method based on big data according to claim 3, characterized in that: The specific steps of comparing the photovoltaic module operation data and the basic model of power generation behavior, identifying abnormal power generation behavior, uploading the abnormal power generation behavior to the blockchain, and recording it in the distributed ledger are as follows: The real-time collected photovoltaic module operation data x t , through the feature extraction network, obtain the feature representation z t ; Calculate the abnormal index A(z t ,z); Contrast abnormality index A(z t ,z) and set the abnormal judgment threshold θ to identify abnormal power generation behavior; Build a record of abnormal power generation behavior, create and call a smart contract, upload abnormal power generation behavior to the blockchain, and generate a transaction hash value.

5. The photovoltaic power station operation and maintenance management system and method based on big data according to claim 4, characterized in that: The above method combines historical data to trace the abnormal power generation behavior in the distributed ledger and uses smart contracts to trigger operation and maintenance tasks. The specific steps are as follows: Extract abnormal power generation behavior records R from the distributed ledger and collect the operating parameters of the photovoltaic power station during the period when the abnormal behavior occurred; Define the spatiotemporal association graph G = (V, E), where V is the set of abnormal event nodes, corresponding to each abnormal power generation behavior record R i , the edge set E is the spatiotemporal correlation between abnormal events; Construct a causal inference network N = (V, E, W) and calculate the causal weight matrix W; Using the causal weight matrix W and the traceability target node R t ,Determine the traceability path through the depth-first search algorithm; Design smart contract task allocation rules; After the contract is triggered, operation and maintenance instructions are issued through the IoT device and cloud platform interface.

6. The photovoltaic power station operation and maintenance management system and method based on big data according to claim 5, characterized in that: After the operation and maintenance task is executed, the abnormal power generation behavior is fed back to the self-supervised learning model sample library, and the model is dynamically adjusted. The specific steps are as follows: Extract feedback data after operation and maintenance task processing and abnormal data before processing, and format the data; Extract sample features through feature extraction network, introduce correction vector, map data to contrast learning space, and obtain corrected samples; Store the corrected samples into the self-supervised learning model library; Based on the updated sample library, the basic model is re-trained through self-supervision; Add a modified sample weight factor to the original contrastive learning loss function and adjust the loss function; The learning rate is dynamically adjusted based on the number of correction samples.

7. The photovoltaic power station operation and maintenance management system and method based on big data according to claim 6, characterized in that: The operation and maintenance report is generated based on the execution content of the operation and maintenance task and uploaded to the blockchain. The specific steps are: Collect operation and maintenance task execution data and generate operation and maintenance reports; Digitally sign the operation and maintenance report using a private key; Call the blockchain interface, upload the operation and maintenance report to the distributed ledger, and obtain the transaction hash value; Store operation and maintenance reports and record the mapping relationship between report files and blockchain transaction hash values ​​in the database.

8. A photovoltaic power station operation and maintenance management system based on big data, based on the photovoltaic power station operation and maintenance management system and method based on big data according to any one of claims 1 to 7, characterized in that: Including data collection module, model building module, anomaly detection module, anomaly tracing module, operation and maintenance feedback module, and report generation module; The data acquisition module is used to collect photovoltaic module operation data, pre-process the data using edge computing equipment, and store it on a cloud big data platform; The model building module is used to establish a basic model of power generation behavior of a photovoltaic power station based on the stored photovoltaic component operation data using a self-supervised learning method; The anomaly detection module is used to compare the photovoltaic module operation data with the basic model of power generation behavior, identify abnormal power generation behavior, upload the abnormal power generation behavior to the blockchain, and record it in the distributed ledger; The abnormal tracing module is used to combine historical data to trace the abnormal power generation behavior in the distributed ledger and trigger the operation and maintenance tasks using smart contracts; The operation and maintenance feedback module is used to feed back the processed abnormal power generation behavior to the self-supervised learning model sample library after the operation and maintenance task is executed, and dynamically adjust the model; The report generation module is used to generate an operation and maintenance report based on the execution content of the operation and maintenance task and upload it to the blockchain.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the photovoltaic power station operation and maintenance management system and method based on big data are implemented as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the photovoltaic power station operation and maintenance management system and method based on big data are implemented as described in any one of claims 1 to 7.

Images