Energy-saving management method and system for smart energy station

Abstract

The invention provides an energy-saving management method and system for a smart energy station, and relates to the field of electric digital data processing, and the method comprises the steps: obtaining weather prediction data of the location of the smart energy station in a first preset time period, and determining a weather type corresponding to the location of the smart energy station; matching a corresponding life time prediction model according to the weather type; acquiring historical working condition data and maintenance data of each functional equipment module in a second preset time period; inputting the historical working condition data and the maintenance data into a life time prediction model to obtain the residual life corresponding to each functional equipment module; and generating a maintenance plan of each functional equipment module in the first preset time period according to the residual life. By implementing the method, the maintenance time arrangement in the maintenance plan can be more reasonable, the equipment problem can be found in time, and the energy loss caused by the equipment fault is reduced.

Description

Technical Field

[0001] The present application relates to the field of electrical digital data processing, and in particular to a smart energy station energy-saving management method and system. Background Art

[0002] As an important part of the modern energy system, smart energy stations aim to improve energy efficiency and reduce operating costs through data analysis and management methods. These stations usually combine multiple energy generation sources, such as solar energy, wind energy, etc., as well as various energy storage and conversion equipment to achieve optimal configuration and utilization of energy.

[0003] In the related art, the management system of smart energy stations usually adopts a static equipment maintenance strategy. It mainly includes regular inspection and maintenance of all equipment. Of course, the life cycle of each equipment component can also be predicted through the historical operation data of the equipment in the smart energy station, so as to carry out more targeted inspection and maintenance.

[0004] However, not all historical operating data show regular changes, which may lead to inaccurate equipment life cycle prediction based on historical operating data, causing equipment to fail due to failure to maintain it in time, resulting in waste of resources. Summary of the invention

[0005] The present application provides a smart energy station energy-saving management method and system for addressing the problem that a maintenance plan generated based on equipment life cycle prediction results fails to detect equipment failures in a timely manner, resulting in waste of resources.

[0006] In a first aspect, the present application provides a smart energy station energy-saving management method, which is applied to an energy-saving management system. The method includes: Obtain weather forecast data for the location of the smart energy station within a first preset time period; Determine the weather type corresponding to the location of the smart energy station based on the weather forecast data; The corresponding life span prediction model is matched according to the weather type, and different life span prediction models correspond to different weather types; Acquire historical operating condition data and maintenance data of each functional equipment module within a second preset time period, the functional equipment module including a plurality of functional equipment; The historical operating condition data and maintenance data are input into the life time prediction model to obtain the remaining life corresponding to each functional equipment module; A maintenance plan for each functional equipment module within a first preset time period is generated according to the remaining life.

[0007] Through the above embodiment, the energy-saving management system obtains the weather forecast data within a specific time period to match the corresponding life time prediction model, inputs the historical operating condition data and maintenance data into the model, obtains the corresponding remaining life of each functional equipment module, and then formulates a corresponding maintenance plan, so that the maintenance time arrangement is more reasonable, equipment problems can be discovered in time, and energy losses caused by equipment failures can be reduced.

[0008] In some embodiments, before the step of matching the corresponding life time prediction model according to the weather type, and different weather types corresponding to different life time prediction models are different, the step further includes: Obtain historical weather data of smart energy stations, as well as corresponding historical operating data and maintenance data; The historical weather data is divided according to preset weather types to obtain historical operating condition data and maintenance data under different weather types, wherein the historical operating condition data includes normal operating data and fault data; Building multiple data sets based on the historical operating data and maintenance data under different weather types, each data set corresponds to a weather type; A life span prediction model is trained based on the data set to obtain training life span prediction models corresponding to different weather types.

[0009] Through the above embodiments, the energy-saving management system divides the historical weather, working conditions and maintenance data of the smart energy station according to the preset weather types, and trains the life prediction model for different weather types based on these data. This makes the model training more targeted, thereby improving the accuracy of the model's prediction of the remaining life of the equipment, allowing the energy station to maintain the optimal maintenance strategy under different environmental conditions, effectively extending the service life of the equipment and reducing the failure rate.

[0010] In some embodiments, the step of constructing multiple data sets based on the historical operating data and maintenance data under different weather types, each data set corresponding to a weather type, specifically includes: Determine the remaining life of each functional equipment module according to the fault data and the maintenance data to obtain marked data; Data sets corresponding to different weather types are constructed based on the normal operation data, the maintenance data and the corresponding annotated data.

[0011] Through the above embodiments, the energy-saving management system determines the remaining life of the equipment through fault data and maintenance data to form labeled data, which not only improves the quality of the data set, but also enables the model to more comprehensively understand the performance of the equipment under different working conditions, and can more finely distinguish the possible failure modes and life expectancy of the equipment.

[0012] In some embodiments, after the step of training the life time prediction model based on the data set to obtain the training life time prediction model corresponding to different weather types, the method further includes: The model data corresponding to the training life time prediction model corresponding to different weather types is uploaded to the cloud sharing platform to realize model data sharing.

[0013] Through the above embodiments, the energy-saving management system realizes the sharing and updating of model data by uploading the data of the prediction model to the cloud sharing platform. This not only improves the versatility and update speed of the model, but also enables different smart energy stations to learn from each other and jointly optimize their own operation and maintenance strategies.

[0014] In some embodiments, the step of matching the corresponding life time prediction model according to the weather type, wherein different weather types correspond to different life time prediction models, specifically includes: Obtain the weather type and corresponding model data stored in other smart energy stations, where the model data is the construction parameter of the life time prediction model; Determine the weather type that has the highest matching degree with the weather forecast data, and the corresponding model data; A life time prediction model corresponding to the first preset time period is determined based on the model data.

[0015] Through the above embodiment, the energy-saving management system improves the adaptability and accuracy of the forecast by acquiring the model data and weather type information of other smart energy stations and matching the model that best suits the current forecast weather. This method enables each energy station to select the optimal forecast model according to the actual environmental conditions, so that the maintenance plan is more in line with actual needs, optimizes resource allocation, and improves overall operating efficiency.

[0016] In some embodiments, before the step of obtaining the historical operating data and maintenance data of each functional equipment module within the second preset time period, the step further includes: Obtain the location information of each functional device and the corresponding functional category; The corresponding functional device module is determined based on the position information and the corresponding functional category.

[0017] Through the above embodiments, the energy-saving management system obtains the location information and functional category of the equipment before obtaining the equipment operating condition and maintenance data, thereby providing data support for equipment classification and modular management, helping to more accurately identify and analyze the specific needs and performance status of the equipment, making maintenance plans more targeted and efficient.

[0018] In some embodiments, the step of generating a maintenance plan for each functional equipment module within a first preset time period according to the remaining life specifically includes: The functional equipment modules are prioritized according to the remaining life, and the priorities include a high-risk group, a medium-risk group, and a low-risk group; Different maintenance frequencies are assigned to functional equipment modules of different priorities, and the maintenance frequencies are positively correlated with the priorities.

[0019] Through the above embodiments, the energy-saving management system prioritizes equipment according to its remaining lifespan and adjusts the maintenance frequency strategy according to the risk level, thereby improving the efficiency and effectiveness of maintenance and ensuring that high-risk equipment receives sufficient attention and timely maintenance, while avoiding excessive maintenance of low-risk equipment. This reduces operating and maintenance costs while maintaining equipment reliability.

[0020] In a second aspect, the present application provides an energy-saving management system, the energy-saving management system comprising: one or more processors and a memory; The memory is coupled to the one or more processors, and the memory is used to store computer program code, which includes computer instructions. The one or more processors call the computer instructions so that the energy-saving management system can implement a smart energy station energy-saving management method provided in the above embodiment, which will not be repeated here.

[0021] In a third aspect, the present application provides a computer-readable storage medium, including instructions. When the instructions are executed on an energy-saving management system, the energy-saving management system can implement a smart energy station energy-saving management method provided in the above-mentioned embodiment, which will not be repeated here.

[0022] In a fourth aspect, the present application provides a computer program product. When the computer program product runs on an energy-saving management system, the energy-saving management system can implement a smart energy station energy-saving management method provided in the above embodiment, which will not be repeated here.

[0023] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. By classifying the historical weather, working conditions and maintenance data of the smart energy station, and building a life prediction model for different weather types based on these data, this method makes model training more targeted, improves the accuracy of the model's prediction of the remaining life of the equipment, allows the energy station to maintain the optimal maintenance strategy under different environmental conditions, effectively extends the service life of the equipment, and reduces energy losses caused by equipment failures.

[0024] 2. By uploading the data of the prediction model to the cloud sharing platform, the model data is shared and updated. This not only speeds up the model update and enhances its versatility, but also enables different smart energy stations to learn from each other, improve their own operation and maintenance strategies through shared data, and further optimize resource allocation and operation efficiency.

[0025] 3. Prioritize equipment based on its remaining lifespan and adjust maintenance frequency based on risk levels. This strategy not only improves the efficiency and effectiveness of maintenance work, ensuring that high-risk equipment receives adequate attention and timely maintenance, but also avoids excessive maintenance of low-risk equipment, maximizing equipment operational reliability and maintenance cost efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 This is a flow chart of a smart energy station energy saving management method in an embodiment of the present application; Figure 2 This is another flowchart of a smart energy station energy saving management method in an embodiment of the present application; Figure 3 It is a schematic diagram of the structure of a physical device of the energy-saving management system in an embodiment of the present application. DETAILED DESCRIPTION

[0027] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification and appended claims of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more listed items.

[0028] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0029] For ease of understanding, the following is a flow chart of the method provided in this implementation. Figure 1 The figure shows a flow chart of a smart energy station energy-saving management method in an embodiment of the present application.

[0030] S101. Obtain weather forecast data for the location of the smart energy station within a first preset time period.

[0031] The energy-saving management system obtains weather forecast data for the future period (the first preset time period) of the location of the smart energy station. These data can come from weather forecasts issued by the meteorological department or obtained through the energy station's own meteorological monitoring equipment and forecasting models. Among them, weather forecast data usually includes multiple parameters such as temperature, humidity, wind speed, and sunshine intensity. The time span can range from a few days to a few weeks in the future, which is not limited here.

[0032] Specifically, the energy-saving management system can obtain formatted weather forecast data from the meteorological department through the API interface, or it can obtain structured data by parsing the weather forecast text released by the meteorological department. If the energy station is equipped with its own meteorological monitoring equipment such as temperature and humidity sensors, wind speed and direction meters, etc., the system can also use the real-time meteorological data collected by these devices, combined with historical data and machine learning models, to predict the recent weather.

[0033] S102. Determine the weather type corresponding to the location of the smart energy station based on weather forecast data.

[0034] After obtaining the weather forecast data, the energy-saving management system further analyzes the data to determine the main weather type in the energy station's location in the future.

[0035] Among them, common weather types include sunny, cloudy, rainy, hot, cold, etc. Some energy stations are located in special climate zones and may also encounter extreme weather such as typhoons and sandstorms. When classifying weather types, the energy-saving management system comprehensively considers various meteorological factors and finds out the factors that have the greatest impact on energy station equipment. For example, the "high temperature" type in summer and the "low temperature" type in winter.

[0036] Specifically, the energy-saving management system can set the threshold range of meteorological elements such as temperature, humidity, and wind force, corresponding to different weather types. When the weather forecast data falls into the threshold range of a certain type, it is determined that the future period of time belongs to this weather type. If the forecast data shows multiple types of features, the main type can be taken. For example, in one embodiment, the system sets the daily maximum temperature above 35°C as high temperature weather, below 15°C as cold weather, and between the two as normal temperature weather. The forecast data shows that the maximum temperatures for the next 5 days are 38°C, 37°C, 36°C, 32°C, and 33°C, respectively. It can be judged that the first 3 days are high temperature weather and the next 2 days are normal temperature weather.

[0037] S103, matching the corresponding life span prediction model according to the weather type.

[0038] Since the degradation rate and failure mode of equipment under different weather conditions are not the same, after the weather type is determined, the energy-saving management system further selects the corresponding life time prediction model to participate in the subsequent remaining life prediction of each functional module.

[0039] Specifically, the energy-saving management system establishes a correspondence between weather types and prediction models. For example, sunny, cloudy, and rainy weather types correspond to models A, B, and C respectively. When it is determined that the weather will be mainly sunny in the next few days, model A is selected for equipment life prediction. If a new weather type appears that has no corresponding model in advance, the system can select the closest model or use a general model.

[0040] In specific implementation, some common weather type prediction models can be preset in the model library of the energy-saving management system, and the model can be regularly updated and optimized using the operating data and weather data of the energy station. After the weather type for a period of time in the future is determined, the system retrieves the corresponding type of prediction model from the model library, or retrieves the model data parameters uploaded by other smart energy stations from the relevant data sharing platform to dynamically generate a local life prediction model. Among them, the input of the life prediction model is the historical operating data and maintenance data in the corresponding time period, and the output is the life distribution of components and complete machines.

[0041] S104: Acquire historical operating condition data and maintenance data of each functional equipment module within a second preset time period.

[0042] The energy-saving management system obtains the operating condition records and maintenance records of each functional equipment in the energy station within a historical period (the second preset time period). Among them, the operating condition data reflects the real-time operating status of the equipment, such as power generation, speed, temperature and other parameters, which can be collected from the equipment's monitoring system or sensor. The maintenance data records the time, content and effect of activities such as regular maintenance, fault repair, and component replacement of the equipment, and can be obtained from the maintenance management system or work order system. The length of the second preset time period should be sufficient to reflect the long-term trend changes in equipment performance, and at the same time, it should be as similar as possible to the future weather conditions of the first preset time period.

[0043] In specific implementation, the energy-saving management system is interconnected with the monitoring system, production management system, equipment management system, etc. in the energy station, and can obtain the structural working condition and maintenance data of the equipment from these systems regularly or on demand, and store them in the life analysis database after pre-processing such as cleaning, alignment, and missing value processing. For equipment that is not connected to the network, the system can also accept data in the form of manually entered electronic spreadsheets, paper reports, etc.

[0044] S105 , inputting historical operating condition data and maintenance data into a life time prediction model to obtain the remaining life corresponding to each functional equipment module.

[0045] When the equipment life prediction model that meets future weather conditions has been selected and the historical full-cycle data of the equipment has been obtained, the energy-saving management system starts the life prediction calculation, inputs the historical operating condition data and maintenance data into the life prediction model, predicts the time distribution of the end of the equipment life, and obtains the remaining life time of each functional equipment module.

[0046] S106: Generate a maintenance plan for each functional equipment module within a first preset time period according to the remaining life.

[0047] After predicting the remaining life distribution of each functional module, the energy-saving management system finally formulates specific maintenance measures and time arrangements for the inspection, maintenance, replacement, etc. of each module within the first preset time period to form an executable maintenance plan.

[0048] Specifically, the energy-saving management system can break down equipment maintenance tasks into specific activity types, execution time, responsible persons, and other elements. Among them, activity types include routine inspections, regular maintenance, fault diagnosis, component replacement, and whole machine scrapping. Equipment maintenance plans can be presented intuitively in the form of charts, Gantt charts, work order tables, etc., and coordinated with the production plan and manpower arrangement of the energy station, and sent to relevant personnel for execution through the automation system or manually.

[0049] In the above embodiment, the energy-saving management system obtains weather forecast data within a specific time period to match the corresponding life time prediction model, inputs historical operating condition data and maintenance data into the model, obtains the corresponding remaining life of each functional equipment module, and then formulates a corresponding maintenance plan, so that the maintenance time arrangement is more reasonable, equipment problems can be discovered in time, and energy losses caused by equipment failures can be reduced.

[0050] The following is a more detailed description of the process of the method provided by this embodiment. Figure 2 As shown, it is another flow chart of a smart energy station energy-saving management method in an embodiment of the present application.

[0051] S201. Obtain historical weather data of the smart energy station, as well as corresponding historical operating condition data and maintenance data.

[0052] The energy-saving management system extracts relevant historical weather data from the database or storage system of the energy station. The historical weather data includes meteorological parameters such as temperature, humidity, wind speed, and sunshine intensity, and the time span can be the past year or longer. These data can come from the energy station's own meteorological monitoring equipment or from the local meteorological department, which is not limited here.

[0053] In addition, the energy-saving management system also obtains historical operating data and maintenance data corresponding to historical weather data. Among them, the historical operating data reflects the operating status of various functional equipment in the energy station under different weather conditions, such as power generation, efficiency, failure rate and other indicators. The maintenance data records the maintenance and repair history of each equipment, including maintenance time, replacement parts, maintenance reasons and other information.

[0054] S202: Divide the historical weather data according to the preset weather types to obtain the historical operating condition data and maintenance data under different weather types.

[0055] The energy-saving management system classifies data according to preset weather types. The preset weather types can be defined according to the climate characteristics of the area where the energy station is located, such as high temperature, low temperature, rainy, drought, etc. Each weather type corresponds to a certain range of meteorological parameters, such as high temperature when the temperature is above 30℃, heavy rain when the rainfall exceeds 100 mm / day, etc.

[0056] The energy-saving management system classifies each piece of historical data (historical weather data, historical operating data, and maintenance data) into corresponding categories by comparing the meteorological parameters in the historical weather data with the preset weather type definition. For example, if the maximum temperature on August 1, 2022 reaches 35°C, then all historical data on that day will be classified as high temperature. Similarly, if the rainfall on a certain day reaches the heavy rain standard, the data on that day will belong to the heavy rain category.

[0057] After completing the weather classification, the system can obtain the historical operating conditions and maintenance data of each equipment in the energy station under different weather types. Each weather type corresponds to a data subset, reflecting the operation and maintenance status of the equipment under this environmental condition.

[0058] S203: Determine the remaining life of each functional equipment module according to the fault data and the repair and maintenance data to obtain marked data.

[0059] After obtaining historical operating data and maintenance data under different weather types, the energy-saving management system further analyzes these data to determine the health status and expected life of each functional equipment module. Among them, the remaining life is the expected time for the equipment to reach the end of its service life from the current moment.

[0060] Specifically, the energy-saving management system infers the remaining life of the equipment based on the fault information and maintenance records in the historical data. Fault data includes the time, type and cause of the equipment failure, while maintenance data records the equipment maintenance cycle, replacement of parts and other maintenance behaviors. By analyzing indicators such as the equipment's failure interval and mean time between failures, the life cycle of the equipment under normal use conditions can be estimated. For example, if the mean time between failures of a certain model of transformer is 10 years, the current use time is 6 years and the operating condition is good, then its remaining life can be estimated to be about 4 years.

[0061] By comparing the actual operating time of the equipment with the maintenance cycle recommended by the manufacturer, it can be determined whether the equipment is operating in the best condition. If the recommended maintenance cycle of a fan is six months, but it has actually been running for a year without maintenance, it can be inferred that its remaining life may be lower than normal.

[0062] After estimating the remaining life of each equipment module, the energy-saving management system adds it as a data tag to the corresponding historical operating condition data, forming a data set with life annotations. The annotation data reflects the health status and expected life of the equipment at different historical moments and under different weather conditions.

[0063] S204: construct data sets corresponding to different weather types based on the normal operation data, maintenance data and corresponding annotation data.

[0064] After obtaining the labeled data, the energy-saving management system further integrates the data to construct a structured data set suitable for model training. Specifically, for each weather type, the energy-saving management system extracts the normal operating parameters of the equipment in the corresponding time period from the labeled data, such as temperature, speed, efficiency, etc., as the input features of the model. At the same time, the remaining life labels of these time points are extracted as the prediction targets of the model. In addition, some maintenance information is added, such as the number of days since the last maintenance time, as an auxiliary feature reflecting the health status of the equipment. Through the above methods, the energy-saving management system can obtain multiple structured data subsets, each subset corresponds to a weather type, and contains key information such as the operating characteristics, health status and life labels of the equipment. These data sets are usually stored in the form of tables, with each row corresponding to a complete record of a historical moment.

[0065] S205. Training a life span prediction model based on the data set to obtain a training life span prediction model corresponding to different weather types.

[0066] Once the structured data sets for different weather types are built, the energy-saving management system trains multiple life prediction models based on these data, each model corresponding to a specific weather scenario.

[0067] Specifically, the energy-saving management system can build a model framework based on machine learning algorithms such as deep neural networks, support vector machines, and random forests. Taking deep neural networks as an example, the input data is first randomly divided into a training set and a validation set, with a ratio of 8:2 or 9:1.

[0068] Among them, the training set is used to learn and update the model parameters. For each data in the training set, the model calculates the predicted remaining life value based on the current parameters, compares it with the true label value (corresponding to the labeled data), and calculates the prediction error. Then, the model uses the back-propagation algorithm to propagate the error from the output layer to each neuron, and adjusts the weight coefficient of the neuron accordingly. This process is repeated until the prediction error of the model on the training set converges to a lower stable level. The model is then tested and verified using the validation set. The optimal model is selected based on the validation set indicators, and finally a life time prediction model dedicated to multiple scenarios is obtained.

[0069] In the above embodiment, the energy-saving management system divides the historical weather, working conditions and maintenance data of the smart energy station according to the preset weather types, and trains the life prediction model for different weather types based on these data. This makes the model training more targeted, thereby improving the accuracy of the model's prediction of the remaining life of the equipment, allowing the energy station to maintain the optimal maintenance strategy under different environmental conditions, effectively extending the service life of the equipment and reducing the failure rate.

[0070] S206, uploading the training life time prediction model data corresponding to different weather types to the cloud sharing platform for realizing model data sharing.

[0071] The energy-saving management system uploads the relevant data of the life time prediction model for different weather types obtained by previous training, such as the model's structural parameters, weight coefficients, etc., to a cloud-based sharing platform. This platform can be a cloud service dedicated to storing and sharing smart energy station operation data and models, or a general industrial Internet platform, which is not limited here.

[0072] In the above embodiment, the energy-saving management system realizes the sharing and updating of model data by uploading the data of the prediction model to the cloud sharing platform. This not only improves the versatility and update speed of the model, but also enables different smart energy stations to learn from each other and jointly optimize their own operation and maintenance strategies.

[0073] S207: Obtain weather types and corresponding model data stored in other smart energy stations.

[0074] Specifically, the energy-saving management system initiates a query request to the specified data directory or index by calling the data access interface provided by the cloud platform, such as RESTful API. The request contains the metadata conditions of the required data, such as the data provider (i.e. the identifier of other energy stations), weather type, time range, model name or version number, etc. The cloud platform retrieves matching data records in the storage system based on these conditions and returns the results to the energy-saving management system.

[0075] It should be noted that for model data, the cloud platform usually returns a packaged model object, which contains complete information such as the model's structure definition, parameter weights, and instructions for use. The energy-saving management system can directly load it into the local model execution engine for subsequent reasoning and prediction.

[0076] S208: Determine the weather type that has the highest matching degree with the weather forecast data and the corresponding life span prediction model.

[0077] After obtaining the weather classification standards and prediction models of other smart energy stations, the energy-saving management system selects the type that best matches the current and future weather conditions of its own site, and calls the corresponding model to predict the life of the equipment.

[0078] Specifically, the energy-saving management system extracts weather forecast data for the current and future period (such as the next week), mainly including daily temperature, humidity, air pressure, wind speed, precipitation and other meteorological element parameters. Then, these parameters are compared with the shared weather classification standards to calculate the matching degree of each category. Optional matching degree calculation methods include but are not limited to Euclidean distance, cosine similarity, KL divergence, etc., which are not limited here.

[0079] Furthermore, the system calculates and sorts the matching degree of each shared weather type, and finally selects the weather type with the highest score, and sets the prediction model corresponding to the weather type as the final life span prediction model. In addition, if a weather type corresponds to multiple prediction models, the prediction model with the best validation set index is selected as the final life span prediction model.

[0080] In the above embodiment, the energy-saving management system improves the adaptability and accuracy of the forecast by obtaining the model data and weather type information of other smart energy stations and matching the model that best suits the current forecast weather. This method enables each energy station to select the optimal forecast model according to the actual environmental conditions, so that the maintenance plan is more in line with actual needs, optimizes resource allocation, and improves overall operating efficiency.

[0081] S209: Determine the remaining life of each functional equipment module based on the historical operating data and maintenance data of each functional equipment module within a second preset time period.

[0082] This step has been explained in step S104 and step S105 and will not be repeated here.

[0083] S210 , prioritizing each functional device module according to its remaining lifespan.

[0084] After obtaining the predicted remaining life of each functional equipment module, the energy-saving management system further classifies and sorts the equipment modules according to the length of the remaining life to determine the urgency of their maintenance. Generally speaking, the shorter the remaining life of the equipment, the higher the risk of failure and should be classified as a higher maintenance priority.

[0085] For example, the system can preset several critical values ​​of remaining life expectancy, such as those with a remaining life expectancy of less than 3 months are classified as a high-risk group, those with a remaining life expectancy of 3-6 months are classified as a medium-risk group, and those with a remaining life expectancy of more than 6 months are classified as a low-risk group.

[0086] S211. Different maintenance frequencies are assigned to functional equipment modules with different priorities.

[0087] Specifically, the energy-saving management system can formulate a weekly inspection and maintenance plan for equipment modules in the high-risk group (such as equipment with a remaining life of less than 3 months) to comprehensively check the equipment status; for equipment in the medium-risk group (such as equipment with a remaining life of 3-6 months), routine maintenance can be arranged every half month; for equipment in the low-risk group (equipment with a remaining life of more than 6 months), regular inspections can be maintained quarterly or semi-annually. Of course, the specific maintenance frequency can be flexibly adjusted according to actual conditions and is not limited here.

[0088] In the above embodiment, the energy-saving management system prioritizes equipment according to its remaining lifespan and adjusts the maintenance frequency strategy according to the risk level, thereby improving the efficiency and effectiveness of maintenance and ensuring that high-risk equipment receives adequate attention and timely maintenance, while avoiding excessive maintenance of low-risk equipment. This reduces operating and maintenance costs while maintaining equipment reliability.

[0089] S212: Obtain location information of each functional device and the corresponding functional category.

[0090] Specifically, the energy-saving management system can obtain the location and function information of the equipment through a variety of channels. Optionally, during the equipment installation and commissioning phase, the construction and commissioning personnel will enter the installation location, nameplate information, technical parameters, etc. of each device into the system database to form a static information library of the equipment. Optionally, large equipment itself usually has built-in positioning and self-diagnosis modules, which can provide real-time feedback on its geographical location and operating status information. At the same time, during daily inspections and maintenance, maintenance personnel can also continuously update and supplement equipment file information. The system can integrate data from multiple sources to ultimately form a complete and accurate set of equipment location and function category data.

[0091] S213: Determine the corresponding functional device module based on the location information and the corresponding functional category.

[0092] After obtaining the location and function information of a single device, the energy-saving management system further divides them into different functional modules according to the correlation between the devices. Among them, the functional device module refers to a group of devices that are close to each other in space and cooperate with each other in system functions.

[0093] In the above embodiment, the energy-saving management system provides data support for equipment classification and modular management by obtaining the location information and functional category of the equipment before obtaining the equipment operating condition and maintenance data, which helps to more accurately identify and analyze the specific needs and performance status of the equipment, making the maintenance plan more targeted and efficient.

[0094] The energy-saving management system of the embodiment of the present invention is applied to electronic equipment. Figure 3 A schematic diagram of the architecture of an electronic device suitable for implementing an embodiment of the present invention is shown.

[0095] It should be noted that Figure 3 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0096] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments can be completed by instructions (computer programs), or by controlling related hardware through instructions (computer programs), and the instructions can be stored in a computer-readable storage medium and loaded and executed by a processor. The electronic device of this embodiment includes a storage medium and a processor, wherein a plurality of instructions are stored in the storage medium, and the instructions can be loaded by the processor to execute any step of the method provided in the embodiment of the present invention.

[0097] Specifically, the storage medium and the processor are electrically connected directly or indirectly to realize data transmission or interaction. For example, these elements can be electrically connected to each other through one or more signal lines. The storage medium stores computer execution instructions for implementing the data access control method, including at least one software function module that can be stored in the storage medium in the form of software or firmware. The processor executes various functional applications and data processing by running the software program and module stored in the storage medium. The storage medium can be, but is not limited to, random access storage medium (Random Access Memory, referred to as: RAM), read-only storage medium (Read Only Memory, referred to as: ROM), programmable read-only storage medium (Programmable Read-Only Memory, referred to as: PROM), erasable read-only storage medium (Erasable Programmable Read-Only Memory, referred to as: EPROM), electrically erasable read-only storage medium (Electric Erasable Programmable Read-Only Memory, referred to as: EEPROM), etc. Among them, the storage medium is used to store programs, and the processor executes the program after receiving the execution instruction.

[0098] Furthermore, the software programs and modules in the above-mentioned storage medium may also include an operating system, which may include various software components and / or drivers for managing system tasks (such as memory management, storage device control, power management, etc.), and may communicate with various hardware or software components to provide an operating environment for other software components. The processor may be an integrated circuit chip having signal processing capabilities. The above-mentioned processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc., which may implement or execute the various methods, steps, and logic flow diagrams disclosed in this embodiment. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0099] Since the instructions stored in the storage medium can execute the steps in any method provided in the embodiments of the present invention, the beneficial effects of any method provided in the embodiments of the present invention can be achieved. Please refer to the previous embodiments for details and will not be repeated here.

[0100] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A smart energy station energy-saving management method, applied to an energy-saving management system, characterized in that: The method comprises: Obtain weather forecast data for the location of the smart energy station within a first preset time period; Determine the weather type corresponding to the location of the smart energy station based on the weather forecast data; According to the weather type, a corresponding life span prediction model is matched, and different life span prediction models correspond to different weather types; Acquire historical operating condition data and maintenance data of each functional equipment module within a second preset time period, wherein the functional equipment module includes a plurality of functional equipment; Inputting the historical operating condition data and maintenance data into the life time prediction model to obtain the remaining life corresponding to each functional equipment module; A maintenance plan for each of the functional equipment modules within the first preset time period is generated according to the remaining life.

2. The method according to claim 1, characterized in that Before the step of matching the corresponding life time prediction model according to the weather type, wherein different life time prediction models correspond to different weather types, the method further includes: Obtain historical weather data of smart energy stations, as well as corresponding historical operating data and maintenance data; The historical weather data is divided according to preset weather types to obtain historical operating condition data and maintenance data under different weather types, wherein the historical operating condition data includes normal operating data and fault data; Building multiple data sets based on the historical operating data and maintenance data under the different weather types, each of the data sets corresponds to a weather type; A life span prediction model is trained based on the data set to obtain training life span prediction models corresponding to different weather types.

3. The method according to claim 2, characterized in that The step of constructing a plurality of data sets based on the historical operating condition data and maintenance data under the different weather types, each data set corresponding to a weather type, specifically includes: Determine the remaining life of each functional equipment module according to the fault data and the maintenance data to obtain marked data; Data sets corresponding to different weather types are constructed based on the normal operation data, the maintenance data and the corresponding annotated data.

4. The method according to claim 2, characterized in that: After the step of training the life time prediction model based on the data set to obtain the training life time prediction model corresponding to different weather types, the method further includes: The model data corresponding to the training life time prediction model corresponding to different weather types is uploaded to the cloud sharing platform to realize model data sharing.

5. The method according to claim 1, characterized in that The step of matching the corresponding life time prediction model according to the weather type, wherein different life time prediction models correspond to different weather types, specifically includes: Obtain weather types and corresponding model data stored in other smart energy stations, where the model data is a construction parameter of a life time prediction model; Determine the weather type that has the highest matching degree with the weather forecast data, and the corresponding model data; A life time prediction model corresponding to the first preset time period is determined based on the model data.

6. The method according to claim 1, characterized in that Before the step of obtaining the historical operating data and maintenance data of each functional equipment module within the second preset time period, the method further includes: Obtain the location information of each functional device and the corresponding functional category; The corresponding functional device module is determined based on the position information and the corresponding functional category.

7. The method according to claim 1, characterized in that The step of generating a maintenance plan for each functional equipment module within a first preset time period according to the remaining life specifically includes: Prioritizing the functional equipment modules according to the remaining lifespan, wherein the priorities include a high-risk group, a medium-risk group, and a low-risk group; Different maintenance frequencies are assigned to functional equipment modules of different priorities, and the maintenance frequencies are positively correlated with the priorities.

8. An energy-saving management system, characterized in that: The energy-saving management system includes: one or more processors and memory; The memory is coupled to the one or more processors, and the memory is used to store computer program codes, wherein the computer program codes include computer instructions, and the one or more processors call the computer instructions to enable the energy saving management system to execute the method according to any one of claims 1 to 7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instruction is executed on the energy-saving management system, the energy-saving management system is enabled to execute the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that When the computer program product runs on an energy-saving management system, the energy-saving management system is enabled to execute the method according to any one of claims 1 to 7.

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