Method and system for controlling photovoltaic stock bin of mixing station

Abstract

The invention discloses a mixing station photovoltaic stock bin control method and system, and the method comprises the steps: obtaining weather forecast data of the surrounding environment of a photovoltaic stock bin of a mixing station in a next time period, and predicting the power generation data of a photovoltaic module in the next time period through a photovoltaic power generation prediction model; acquiring power consumption data of the photovoltaic stock bin in the current time period, and configuring a preliminary power consumption mode in the next time period according to the power generation data and the power consumption data; predicting production data of the photovoltaic stock bin in the next time period based on the initial power utilization mode; if the production data does not meet the preset production standard, a final power utilization mode of the next time period is configured based on the preliminary power utilization mode, otherwise, the preliminary power utilization mode is directly used as the final power utilization mode, and an environment parameter configuration scheme of the bin environment auxiliary equipment under the final power utilization mode is obtained. Through scientific electric power mode configuration, economical and efficient utilization of electric power is realized, and stable electric power support is provided for production activities of the mixing station.

Description

Technical Field

[0001] The present invention belongs to the technical field of mixing stations, and in particular to a photovoltaic silo control method and system for a mixing station. Background Art

[0002] With the rapid development of renewable energy technology, photovoltaic power generation, as a clean and efficient form of energy, has received increasing attention from all walks of life. In the building and construction industry, especially in the application of mixing plants, the introduction of photovoltaic power generation systems not only helps to reduce traditional energy consumption, but also effectively alleviates the problem of energy shortage. However, in actual applications, the volatility of photovoltaic power generation and the instantaneous demand for electricity in mixing plants have brought great challenges to the stable supply of electricity and the improvement of production efficiency.

[0003] During the production process of the mixing plant, the photovoltaic power generation system must ensure that its power generation capacity can meet the power demand of the mixing plant, especially during construction demand during peak hours. In order to achieve efficient power management, it is necessary to scientifically and rationally plan and configure the photovoltaic power generation and power consumption mode of the mixing plant. Traditional power supply methods usually rely on empirical data and fixed production plans, which can easily lead to power shortages or waste during the production process, thus affecting production efficiency and economic benefits. Summary of the invention

[0004] The purpose of the present invention is to provide a photovoltaic silo control method and system for a mixing station to solve the deficiencies in the prior art, to achieve economical and efficient use of electricity through scientific power mode configuration, and to provide stable power support for the production activities of the mixing station.

[0005] An embodiment of the present application provides a photovoltaic silo control method for a mixing station, the method comprising: Obtain the weather forecast data for the surrounding environment of the photovoltaic silo of the mixing station for the next period of time; According to the weather forecast data, using a pre-trained photovoltaic power generation prediction model, predicting the power generation data of the photovoltaic components in the next period; Obtain the electricity consumption data of the photovoltaic silo of the mixing station in the current period, and configure the preliminary electricity consumption mode for the next period based on the predicted power generation data and the current electricity consumption data; Based on the preliminary electricity consumption pattern of the next period, and according to the mapping relationship between the historical electricity consumption pattern and the historical production data, the production data of the photovoltaic silo of the mixing station in the next period is predicted; If the production data for the next time period does not meet the preset production standards, the final power consumption mode for the next time period will be configured based on the preliminary power consumption mode. Otherwise, the preliminary power consumption mode will be directly used as the final power consumption mode, and the environmental parameter configuration plan of the silo environmental auxiliary equipment under the final power consumption mode will be obtained. Based on the environmental parameter configuration plan, the environmental suitability index of the silo will be calculated, and the environmental parameter configuration plan in the final power consumption mode will be fine-tuned according to the environmental suitability index.

[0006] Optionally, configuring a preliminary power consumption mode for the next period according to the predicted power generation data and the current power consumption data includes: If the power generation in the predicted power generation data is greater than the power consumption in the current power consumption data, the preliminary power consumption mode is configured as a preset high production performance mode, and the power consumption corresponding to the preset high production performance mode does not exceed the power generation in the predicted power generation data; Otherwise, the preliminary power consumption mode is configured as a preset energy-saving production mode, and the power consumption corresponding to the preset energy-saving production mode does not exceed the power generation in the predicted power generation data.

[0007] Optionally, configuring a final power usage mode for the next period based on the preliminary power usage mode includes: When the initial power consumption mode is the preset energy-saving production mode, an early warning signal is sent to the control terminal of the photovoltaic silo of the mixing station; After receiving the backup power supply activation signal returned by the control terminal, the backup power supply is enabled and the preliminary power consumption mode is adjusted so that the production data reaches the preset production standard.

[0008] Optionally, the fine-tuning of the environmental parameter configuration scheme in the final power consumption mode according to the environmental suitability index includes: If the final power consumption mode is directly the preliminary power consumption mode, and the environmental suitability index is less than the preset environmental threshold, the environmental parameter configuration scheme in the final power consumption mode is fine-tuned so that the environmental suitability index reaches the preset environmental threshold, and the corresponding power consumption does not exceed the power generation in the predicted power generation data, and the corresponding production data meets the preset production standard; If the final power consumption mode is not the preliminary power consumption mode, and the environmental suitability index is less than the preset environmental threshold, the environmental parameter configuration scheme in the final power consumption mode is fine-tuned so that the environmental suitability index reaches the preset environmental threshold and the corresponding production data meets the preset production standards.

[0009] Another embodiment of the present application provides a photovoltaic silo control system for a mixing station, the system comprising: The first acquisition module is used to obtain the weather forecast data of the surrounding environment of the photovoltaic silo of the mixing station for a period of time; A first prediction module is used to predict the power generation data of the photovoltaic components in the next period of time according to the weather forecast data and using a pre-trained photovoltaic power generation prediction model; The first configuration module is used to obtain the power consumption data of the photovoltaic silo of the mixing station in the current period, and configure the preliminary power consumption mode of the next period according to the predicted power generation data and the current power consumption data; The second prediction module is used to predict the production data of the photovoltaic silo of the mixing station in the next period based on the preliminary power consumption pattern in the next period and the mapping relationship between the historical power consumption pattern and the historical production data; The second configuration module is used to configure the final power consumption mode for the next time period based on the preliminary power consumption mode if the production data for the next time period does not meet the preset production standards. Otherwise, the preliminary power consumption mode is directly used as the final power consumption mode, and the environmental parameter configuration scheme of the silo environmental auxiliary equipment under the final power consumption mode is obtained. Based on the environmental parameter configuration scheme, the environmental suitability index of the silo is calculated, and the environmental parameter configuration scheme in the final power consumption mode is fine-tuned according to the environmental suitability index.

[0010] Yet another embodiment of the present application provides a storage medium, wherein the storage medium stores a computer program, wherein the computer program is configured to execute any of the above methods when running.

[0011] Yet another embodiment of the present application provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute any of the methods described above.

[0012] Compared with the prior art, the present invention provides a method for controlling a photovoltaic silo of a mixing station, which obtains weather forecast data of a period of time in the surrounding environment of the photovoltaic silo of the mixing station; predicts the power generation data of the photovoltaic components of the next period of time by using a photovoltaic power generation prediction model according to the weather forecast data; obtains the power consumption data of the photovoltaic silo of the mixing station in the current period of time, and configures a preliminary power consumption mode of the next period of time according to the power generation data and the power consumption data; based on the preliminary power consumption mode, predicts the production data of the photovoltaic silo of the mixing station in the next period of time according to the mapping relationship between the historical power consumption mode and the historical production data; if the production data does not meet the preset production standard, configures the final power consumption mode of the next period of time based on the preliminary power consumption mode, otherwise, directly uses the preliminary power consumption mode as the final power consumption mode, so that through scientific power mode configuration, economical and efficient use of electricity can be achieved, and stable power support can be provided for the production activities of the mixing station. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 A hardware structure block diagram of a computer terminal of a photovoltaic silo control method for a mixing station provided by an embodiment of the present invention; Figure 2 A schematic flow chart of a photovoltaic silo control method for a mixing station provided in an embodiment of the present invention; Figure 3 A schematic structural diagram of a photovoltaic silo control system for a mixing station provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0014] The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, but should not be construed as limiting the present invention.

[0015] The embodiment of the present invention first provides a method for controlling a photovoltaic silo in a mixing station. The method can be applied to electronic devices, such as computer terminals, specifically ordinary computers, etc.

[0016] The following describes it in detail by taking running on a computer terminal as an example. Figure 1 The hardware structure block diagram of a computer terminal of a photovoltaic silo control method for a mixing station provided by an embodiment of the present invention. Figure 1 As shown, the computer terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data. Optionally, the computer terminal may also include a transmission device 106 for communication functions and an input and output device 108. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above-mentioned computer terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown.

[0017] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / modules corresponding to the photovoltaic silo control method of the mixing station in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, to implement the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0018] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of a computer terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0019] See also Figure 2 , an embodiment of the present invention provides a photovoltaic silo control method for a mixing station, which may include the following steps: S201, obtaining weather forecast data for the surrounding environment of the photovoltaic silo of the mixing station for a period of time; The data collection at this stage not only provides a basis for the prediction of photovoltaic power generation, but also directly affects the configuration of subsequent power consumption patterns and the prediction of production data. These meteorological factors include temperature, humidity, light intensity, wind speed, etc. Changes in these factors will significantly affect the power generation efficiency of photovoltaic modules and the power demand of mixing plants.

[0020] 1. Accurate forecast: By obtaining weather forecast data, we can accurately judge future weather changes, thus providing strong support for the prediction of photovoltaic power generation. This is crucial to reducing the uncertainty of photovoltaic power generation and improving the prediction accuracy of power generation.

[0021] 2. Optimize resource allocation: After understanding the environmental conditions, the mixing plant can better optimize the allocation of power resources, ensure high output during high power generation periods, and avoid unnecessary energy consumption during low power generation periods.

[0022] 3. Improve production efficiency: By reasonably predicting environmental conditions and power demand, the mixing plant can achieve smooth and efficient production processes to meet production needs and time requirements. One way to achieve this may include: 1. Data source selection: Determine the main data source for meteorological data acquisition, such as the National Weather Service, local weather stations, or commercial weather service provider APIs. When selecting a data source, consider the timeliness, accuracy, and completeness of its historical data.

[0023] 2. Establish a data acquisition model: Enter the geographical location (latitude and longitude) and altitude information of the mixing plant in the data source, write a model, automatically call the meteorological API, and obtain the weather forecast data for a period of time in the future (for example, the next 24 hours). This model also needs to consider the timeliness of weather models and meteorological data to ensure that data for different periods can be automatically obtained according to the preset frequency (such as updating once every hour).

[0024] 3. Data screening and processing: Perform preliminary screening on the acquired meteorological data, extract key parameters (such as temperature, humidity, sunshine duration, wind speed, etc.), and remove invalid or missing data records. Use interpolation methods (such as linear interpolation or spline interpolation) to process missing data to ensure the temporal consistency and accuracy of meteorological conditions.

[0025] 4. Meteorological data fusion: Use heterogeneous data fusion technology to integrate weather forecast data from different data sources to improve the accuracy of weather forecasts. For example, combine the macro forecast of the National Meteorological Bureau with the micro forecast provided by the local meteorological station to form a more detailed weather forecast model.

[0026] 5. Output weather forecast data: Convert the processed weather forecast data into a readable format and output it to the monitoring interface or data platform of the mixing plant at a preset frequency, so that production managers can monitor and analyze it in real time. In addition, the system can automatically send notifications within a preset time period according to weather changes to remind relevant staff to take corresponding preparation measures.

[0027] Through the implementation of the above steps, the mixing station can efficiently and accurately obtain the weather forecast data of the surrounding environment for a period of time, thus laying a solid foundation for the prediction of photovoltaic power generation and the configuration of power consumption patterns.

[0028] S202, predicting the power generation data of the photovoltaic modules in the next period of time using a pre-trained photovoltaic power generation prediction model according to the weather forecast data; By using the acquired weather forecast data and combining it with the pre-trained photovoltaic power generation prediction model, the power output of photovoltaic modules in the future can be effectively predicted. This process is crucial for the power dispatching and management of the mixing plant, and it can grasp the changing trend of photovoltaic power generation in advance and ensure the stability and efficiency of production.

[0029] 1. Power resource management: Through accurate power generation forecasting, the mixing plant can arrange the use of electricity more reasonably and avoid production stagnation or delays caused by insufficient power generation.

[0030] 2. Reduce costs: Accurately predicting future power generation capacity can reduce dependence on external power grids, thereby reducing the cost of purchasing electricity and improving economic benefits.

[0031] 3. Environmentally friendly operation: Using photovoltaic power generation as a clean energy can reduce dependence on fossil fuels, reduce carbon emissions, and achieve sustainable development goals. One way to achieve this can include: 1. Meteorological data input: Collect and integrate multi-dimensional environmental data from the meteorological forecast system, including temperature, humidity, solar radiation intensity, wind speed, etc., and perform time series analysis on these data to ensure data integrity and availability.

[0032] 2. Feature selection and extraction: Extract the characteristic variables that have the greatest impact on photovoltaic power generation from meteorological data. For example, use solar radiation intensity, temperature and historical power generation data to build composite features and enhance the model's prediction ability. Normalize meteorological data to eliminate dimensional differences and improve the stability of model training.

[0033] 3. Model building: Select a machine learning algorithm suitable for the characteristics of photovoltaic power generation data (such as random forest, long short-term memory network (LSTM), etc.), and train the model based on historical power generation data. Perform hyperparameter tuning and use cross-validation and other technologies to improve the generalization ability of the model to ensure the accuracy of the model under different meteorological conditions.

[0034] 4. Historical data fusion: Combine historical power generation data with meteorological data to build a multivariate linear regression, decision tree regression or deep learning model. Use time series models to capture the impact of time and meteorological factors on power generation.

[0035] 5. Real-time data update: During the operation of the model, new weather forecast data is accessed in real time, and the model is dynamically updated using online learning methods to adapt to new weather conditions.

[0036] 6. Power generation forecast: Use the trained model and input real-time weather forecast data to generate the predicted power generation value of the photovoltaic module in the next period (such as 1 hour or 30 minutes). Through error analysis and model evaluation of the predicted data, further adjust the output power generation forecast to ensure its accuracy and reliability.

[0037] 7. Visualization and feedback mechanism: The forecast results are presented through visualization tools so that operators can intuitively understand the future power generation capacity. At the same time, a feedback mechanism is established to collect the differences between actual power generation and predicted values, so as to continuously optimize the model in future forecasts.

[0038] Through the implementation of the above steps, the weather forecast data and photovoltaic power generation prediction model can be effectively used to accurately predict the power generation data of photovoltaic modules in the next period. This process not only enhances the power management capabilities of the mixing station, but also improves the scientificity and flexibility of production scheduling.

[0039] S203, obtaining the electricity consumption data of the photovoltaic silo of the mixing station in the current period, and configuring the preliminary electricity consumption mode for the next period according to the predicted power generation data and the current electricity consumption data; First, it is necessary to monitor the power consumption in the current period in real time to obtain the current power consumption data of the photovoltaic silo of the mixing plant. These data can be fed back in real time through smart meters or monitoring systems to ensure accuracy. Next, combined with the photovoltaic module power generation data predicted by the weather forecast data in the previous stage, the system will conduct a comparative analysis to configure a preliminary power consumption mode to adapt to the upcoming production needs.

[0040] The main purpose of this operation is to ensure that the power demand of the mixing plant in the next period can be met, so as to ensure the continuity and stability of production. By integrating the current power consumption data with the predicted power generation data, the availability of existing power resources can be effectively evaluated and a scientific basis can be provided for subsequent power configuration. This timely adjustment method helps to optimize resource use, reduce power waste, and improve the overall operational efficiency of the mixing plant.

[0041] Specifically, if the power generation in the predicted power generation data is greater than the power consumption in the current power consumption data, the preliminary power consumption mode is configured as a preset high production performance mode, and the power consumption corresponding to the preset high production performance mode does not exceed the power generation in the predicted power generation data; When the predicted photovoltaic power generation is higher than the instantaneous power consumption, it indicates that the power supply is relatively sufficient and a more efficient production mode can be selected. At this time, the system will be configured to the preset high production performance mode to make full use of the available power resources to improve production efficiency. This setting can ensure that the equipment in production is adequately supplied with power, avoid production stagnation due to insufficient power, improve the working efficiency of the mixing plant, and maximize the use of photovoltaic power generation.

[0042] High production performance mode means that when the photovoltaic power generation is sufficient and the power supply is stable, the mixing plant will adjust all equipment to the best working state to achieve the highest production efficiency and capacity. In this mode, the production equipment will make full use of the available power resources to achieve maximum production output.

[0043] In high production performance mode, all key production equipment (such as mixers, conveyors, etc.) are running at full load to ensure the fast operation of the production line. This means that the equipment is not working in an energy-saving state, but is trying to maintain the highest working efficiency to meet high-demand production.

[0044] The system can adjust production scheduling in real time and flexibly adjust production plans according to actual photovoltaic power generation and production demand changes, so as to better cope with sudden production needs. This flexibility helps to respond quickly to market changes.

[0045] Otherwise, the preliminary power consumption mode is configured as a preset energy-saving production mode, and the power consumption corresponding to the preset energy-saving production mode does not exceed the power generation in the predicted power generation data.

[0046] When the power generation is insufficient to meet the current electricity demand, the system will adopt energy-saving production mode to limit the power consumption so that it does not exceed the predicted power generation. This mode focuses on reducing energy consumption while ensuring that basic production needs are met. With this configuration, the mixing plant can effectively control power consumption and avoid cost increases, while ensuring that production activities are not completely interrupted in the case of low power supply, thereby maintaining operational flexibility.

[0047] The power generation data can be combined with the power consumption data to generate a preliminary power consumption pattern through decision tree analysis. The engine uses a rule engine to decide whether to use a high production performance mode or an energy-saving production mode based on the current power consumption status and power generation forecast results. In actual operation, the real-time data of power consumption and power generation are continuously monitored and fed back to the decision engine to form a closed-loop control system.

[0048] Energy-saving production mode is a mode enabled when photovoltaic power generation is insufficient or power supply is unstable. Its main purpose is to ensure that basic production needs are maintained under limited resources while reducing costs by limiting electricity consumption and reducing resource consumption.

[0049] In energy-saving production mode, the mixing plant will reduce the operating load of the equipment, and some equipment may be temporarily shut down or run in low-power mode. This strategy ensures that the power consumption does not exceed the predicted power generation to avoid power shortages or power outages.

[0050] In the case of limited resources, this model prioritizes power supply to critical links in the production process, ensuring that core production activities can continue, while restricting or temporarily shutting down non-core equipment.

[0051] By introducing energy-saving measures such as time scheduling and equipment rotation, the system optimizes overall production energy consumption, ensures efficient use of power resources and reduces waste.

[0052] The high production performance mode and energy-saving production mode each respond to different power supply conditions and production needs. The high production performance mode improves production efficiency by maximizing equipment utilization when photovoltaic power generation is sufficient; while the energy-saving production mode emphasizes controlling energy consumption to ensure the continuity of basic production needs when power supply is tight. The configuration and switching of the two can effectively guarantee the operational flexibility and economy of the mixing station under different power conditions, ensuring efficient production under limited resources.

[0053] S204, based on the preliminary electricity consumption pattern of the next period, according to the mapping relationship between the historical electricity consumption pattern and the historical production data, predicting the production data of the photovoltaic silo of the mixing station in the next period; In this step, the goal is to accurately predict the production data of the photovoltaic silo of the mixing plant based on the preliminary power consumption pattern of the next period, combined with the mapping relationship between the historical power consumption pattern and the historical production data. Through this prediction, the system can make timely adjustments and optimizations in a complex production environment, thereby achieving efficient use of resources and improving production efficiency. 1. Dynamic adaptability: By combining historical data with current power demand, the system can flexibly adjust production plans to meet the ever-changing market demands and environmental changes.

[0054] 2. Intelligently optimize production: Accurate production data prediction can guide decision-making in the production process, reduce resource waste and energy consumption, and improve overall production efficiency.

[0055] 3. Risk management: The forecasting system can identify potential production bottlenecks early, allowing management to take necessary measures to ensure that production standards are met and enhance the company's market competitiveness. One way to achieve this may include: Collect the historical electricity consumption patterns and historical production data of the mixing plant and store them in the database. Ensure that the data timestamps and categories are consistent for subsequent analysis.

[0056] Use statistical analysis methods (such as time series analysis) to understand the correlation between historical electricity consumption patterns and production data, and extract key features, such as: the matching of peak electricity consumption hours with peak production hours; the proportional relationship between electricity consumption and product output.

[0057] Use machine learning algorithms (such as regression analysis, random forests, or neural networks) to establish a mapping relationship between power consumption patterns and production data. The model needs to be trained with known historical power consumption data and corresponding production results in order to provide accurate output in subsequent forecasts.

[0058] Obtain and analyze the preliminary electricity consumption pattern for the next period, and evaluate its rationality by combining the current power generation data and historical electricity consumption patterns. Identify potential production shortcomings and ensure that they meet production needs.

[0059] Using the trained model, the preliminary electricity consumption pattern for the next period is input into the model to predict the corresponding production data. The process should include: inputting the current electricity consumption pattern data and related features; generating the production data forecast for the next period through the model.

[0060] The generated forecast data can also be verified. Historical data can be used for comparison to assess the accuracy and reliability of the forecast, and model adjustments or parameter optimization can be performed if necessary.

[0061] A feedback mechanism can also be built based on the difference between the forecast results and the actual production data. The mapping model can be reviewed and adjusted regularly to better adapt to future production patterns and changes in electricity demand. The forecasted production data is integrated with the preliminary electricity consumption pattern to form a comprehensive forecast of the next period of the photovoltaic silo of the mixing plant and provide a basis for decision-making. At the same time, ensure that all forecasted process data is recorded for subsequent analysis and evaluation.

[0062] S205: If the production data for the next time period does not meet the preset production standards, the final power consumption mode for the next time period is configured based on the preliminary power consumption mode. Otherwise, the preliminary power consumption mode is directly used as the final power consumption mode, and the environmental parameter configuration scheme of the silo environmental auxiliary equipment under the final power consumption mode is obtained. Based on the environmental parameter configuration scheme, the environmental suitability index of the silo is calculated, and the environmental parameter configuration scheme in the final power consumption mode is fine-tuned according to the environmental suitability index.

[0063] In this step, the system needs to evaluate the predicted production data to determine whether it meets the preset production standards. If the production data fails to meet the standards, it means that there may be insufficient production, substandard quality or other problems in the upcoming production cycle, so the preliminary power consumption mode needs to be adjusted to ensure stable production efficiency and quality. On the contrary, if the predicted data meets the preset standards, the preliminary power consumption mode can be directly adopted to simplify the decision-making process and improve response speed. Function and significance: 1. Ensure production quality: Through real-time evaluation of production data, the system can effectively monitor production quality standards, ensure that the output at each stage is within an acceptable range, and reduce the risk of unqualified products.

[0064] 2. Optimize resource allocation: When production data does not meet the standards, adjusting the power consumption mode can optimize resource allocation through different production strategies (such as adding backup power supplies, adjusting the operating status of production equipment, etc.), thereby improving overall production efficiency.

[0065] 3. Flexibly respond to changes: This decision-making mechanism enables the system to flexibly respond to changes in the production environment to reduce operational bottlenecks or stagnation caused by inadaptability, thereby enhancing the resilience of the production line.

[0066] Specifically, based on the preliminary power consumption mode, the final power consumption mode of the next period is configured, and when the preliminary power consumption mode is the preset energy-saving production mode, an early warning signal can be sent to the control terminal of the photovoltaic silo of the mixing station; When the initial power consumption mode is the energy-saving production mode, the system sends out an early warning signal to notify the control terminal of possible production risks. This signal can promptly remind operators to pay attention to production conditions and increase sensitivity to potential problems. Sending early warning signals can enhance the foresight and initiative of production management, ensure that potential production bottlenecks or resource shortages are identified at an early stage, and provide a basis for timely scheduling and resource allocation.

[0067] By monitoring production data and electricity consumption patterns in real time, the system can automatically trigger an early warning mechanism when it detects that production data is lower than the preset standard. Using IoT technology, the early warning signal is sent to the control terminal via a wireless network to ensure that the information is communicated in a timely manner.

[0068] After receiving the backup power supply activation signal returned by the control terminal, the backup power supply is enabled and the preliminary power consumption mode is adjusted so that the production data reaches the preset production standard.

[0069] Once the control terminal confirms that the backup power supply needs to be activated, the system will automatically adjust the initial power consumption mode to ensure that the production data meets the standards. This process includes scheduling the activation of the backup power supply and adjusting the corresponding production equipment. By activating the backup power supply and adjusting the power consumption mode, the system can ensure continuous production, avoid production stagnation caused by insufficient power supply, and improve the production stability of the mixing plant.

[0070] A dynamic power management system can be designed and implemented that monitors power supply status in real time and quickly activates backup power when needed. Load balancing algorithms can be applied to automatically adjust power consumption patterns based on the capacity of backup power and current power demand, thereby optimizing power distribution.

[0071] Furthermore, the environmental parameter configuration scheme of the silo environmental auxiliary equipment under the final power consumption mode can also be obtained; After determining the final power consumption mode, the system will rely on the configuration scheme of environmental auxiliary equipment to ensure the suitability of production environment conditions. This scheme may cover a variety of environmental parameters such as temperature, humidity, ventilation, and light to ensure that the materials in the silo are stored and used under optimal conditions. Good environmental conditions have a direct impact on the quality of materials and production efficiency. Obtaining a suitable environmental parameter configuration scheme can reduce the risk of defective materials and production stagnation in production, thereby improving overall production efficiency. A variety of sensors can be deployed, such as temperature and humidity sensors, airflow monitoring equipment, etc., to collect current environmental parameters in real time.

[0072] Calculate the environmental suitability index of the silo based on the environmental parameter configuration scheme; Based on the acquired environmental parameter configuration scheme, the system will calculate an environmental suitability index to quantify whether the current environmental status meets the preset environmental standards. This index is usually calculated by combining different environmental parameters. The environmental suitability index provides a quantitative basis for decision-making. Management can use it to evaluate the impact of current environmental conditions on production efficiency and take necessary adjustment measures in a timely manner.

[0073] A weighted scoring model can be designed to assign different weights to each environmental parameter, and the overall environmental suitability index can be calculated based on its impact on production. The environmental suitability index can also be calculated using the calculation formula designed in this application: Among them, EAI represents the environmental suitability index, T represents the current ambient temperature, H represents the current ambient humidity, V represents the current airflow velocity, L represents the current light intensity, T_{opt}, H_{opt}, V_{opt}, L_{opt} represent the ideal values ​​of these parameters, alpha, beta, gamma, delta are the weights of the corresponding parameters, reflecting their relative importance to production suitability, f, g, h, j are functions that adjust the parameter deviation through nonlinear function models (such as sigmoid or tanh functions), and are used to calculate the suitability between the current value and the optimal value. For example, each nonlinear function can be designed as: Among them, k_1, k_2, k_3, and k_4 are constants for adjusting parameter sensitivity. This formula takes into account the comprehensive impact of multiple environmental parameters on production suitability, making the evaluation of the suitability index more comprehensive. The nonlinear function makes the adaptability calculation of environmental parameters more accurate, especially when the parameters deviate from the optimal value, which can effectively reflect the changes in environmental suitability.

[0074] If the final power consumption mode is directly the preliminary power consumption mode, and the environmental suitability index is less than the preset environmental threshold, the environmental parameter configuration scheme in the final power consumption mode is fine-tuned so that the environmental suitability index reaches the preset environmental threshold, and the corresponding power consumption does not exceed the power generation in the predicted power generation data, and the corresponding production data meets the preset production standard; When the final power consumption mode is still the preliminary power consumption mode and the environmental suitability index is lower than the preset threshold, the system will start the fine-tuning mechanism. This process adjusts the environmental parameter configuration plan to ensure that the environment meets the standards while not exceeding the existing power supply capacity. By fine-tuning the environmental parameters, the continuity of production and the safety of materials can be ensured, and the decline in production quality caused by an unsuitable environment can be reduced. This flexible adjustment capability improves the resilience of the production process.

[0075] Based on the intelligent regulation of fuzzy logic control, environmental parameters can be analyzed in real time and adjusted to ensure that the environmental suitability index reaches the preset threshold. During the regulation process, dynamic power supply restrictions are adopted to optimize the configuration of environmental parameters according to real-time power generation and power consumption data to keep it within the power generation capacity range.

[0076] If the final power consumption mode is not the preliminary power consumption mode, and the environmental suitability index is less than the preset environmental threshold, the environmental parameter configuration scheme in the final power consumption mode is fine-tuned so that the environmental suitability index reaches the preset environmental threshold and the corresponding production data meets the preset production standards.

[0077] In this case, the system needs to fine-tune the environmental parameter configuration under the final power consumption mode to ensure that the environment meets the suitability standards and meets the requirements of production data. This fine-tuning process under constraint conditions can effectively improve production stability and material storage quality, ensuring that the production process can achieve the best environmental conditions regardless of energy saving or high efficiency mode.

[0078] Through the above steps, the control method of the photovoltaic silo of the entire mixing station is not only flexible and intelligent in the decision-making process, but also can be adaptively adjusted according to dynamically changing environmental conditions to ensure the efficiency and stability of the production process.

[0079] It can be seen that the weather forecast data of the surrounding environment of the photovoltaic silo of the mixing station for the next period of time is obtained; according to the weather forecast data, the photovoltaic power generation prediction model is used to predict the power generation data of the photovoltaic components in the next period of time; the power consumption data of the photovoltaic silo of the mixing station in the current period of time is obtained, and the preliminary power consumption mode of the next period of time is configured according to the power generation data and the power consumption data; based on the preliminary power consumption mode, according to the mapping relationship between the historical power consumption mode and the historical production data, the production data of the photovoltaic silo of the mixing station in the next period of time is predicted; if the production data does not meet the preset production standards, the final power consumption mode of the next period of time is configured based on the preliminary power consumption mode, otherwise, the preliminary power consumption mode is directly used as the final power consumption mode, so that through the scientific power mode configuration, the economical and efficient use of electricity can be realized, and stable power support can be provided for the production activities of the mixing station.

[0080] Another embodiment of the present invention provides a photovoltaic silo control system for a mixing station, see Figure 3, the system may include: The first acquisition module 301 is used to obtain the weather forecast data of the surrounding environment of the photovoltaic silo of the mixing station for a period of time; The first prediction module 302 is used to predict the power generation data of the photovoltaic components in the next period of time according to the weather forecast data and using a pre-trained photovoltaic power generation prediction model; The first configuration module 303 is used to obtain the power consumption data of the photovoltaic silo of the mixing station in the current period, and configure the preliminary power consumption mode of the next period according to the predicted power generation data and the current power consumption data; The second prediction module 304 is used to predict the production data of the photovoltaic silo of the mixing station in the next period based on the preliminary power consumption pattern in the next period and the mapping relationship between the historical power consumption pattern and the historical production data; The second configuration module 305 is used to configure the final power consumption mode for the next time period based on the preliminary power consumption mode if the production data for the next time period does not meet the preset production standards. Otherwise, the preliminary power consumption mode is directly used as the final power consumption mode, and the environmental parameter configuration scheme of the silo environmental auxiliary equipment under the final power consumption mode is obtained. Based on the environmental parameter configuration scheme, the environmental suitability index of the silo is calculated, and the environmental parameter configuration scheme in the final power consumption mode is fine-tuned according to the environmental suitability index.

[0081] It can be seen that the weather forecast data of the surrounding environment of the photovoltaic silo of the mixing station for the next period of time is obtained; according to the weather forecast data, the photovoltaic power generation prediction model is used to predict the power generation data of the photovoltaic components in the next period of time; the power consumption data of the photovoltaic silo of the mixing station in the current period of time is obtained, and the preliminary power consumption mode of the next period of time is configured according to the power generation data and the power consumption data; based on the preliminary power consumption mode, according to the mapping relationship between the historical power consumption mode and the historical production data, the production data of the photovoltaic silo of the mixing station in the next period of time is predicted; if the production data does not meet the preset production standards, the final power consumption mode of the next period of time is configured based on the preliminary power consumption mode, otherwise, the preliminary power consumption mode is directly used as the final power consumption mode, so that through the scientific power mode configuration, the economical and efficient use of electricity can be realized, and stable power support can be provided for the production activities of the mixing station.

[0082] An embodiment of the present invention further provides a storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0083] Specifically, in this embodiment, the above storage medium may be configured to store a computer program for performing the following steps: S201, obtaining weather forecast data for the surrounding environment of the photovoltaic silo of the mixing station for a period of time; S202, predicting the power generation data of the photovoltaic modules in the next period of time using a pre-trained photovoltaic power generation prediction model according to the weather forecast data; S203, obtaining the electricity consumption data of the photovoltaic silo of the mixing station in the current period, and configuring the preliminary electricity consumption mode for the next period according to the predicted power generation data and the current electricity consumption data; S204, based on the preliminary electricity consumption pattern of the next period, according to the mapping relationship between the historical electricity consumption pattern and the historical production data, predicting the production data of the photovoltaic silo of the mixing station in the next period; S205: If the production data for the next time period does not meet the preset production standards, the final power consumption mode for the next time period is configured based on the preliminary power consumption mode. Otherwise, the preliminary power consumption mode is directly used as the final power consumption mode, and the environmental parameter configuration scheme of the silo environmental auxiliary equipment under the final power consumption mode is obtained. Based on the environmental parameter configuration scheme, the environmental suitability index of the silo is calculated, and the environmental parameter configuration scheme in the final power consumption mode is fine-tuned according to the environmental suitability index.

[0084] It can be seen that the weather forecast data of the surrounding environment of the photovoltaic silo of the mixing station for the next period of time is obtained; according to the weather forecast data, the photovoltaic power generation prediction model is used to predict the power generation data of the photovoltaic components in the next period of time; the power consumption data of the photovoltaic silo of the mixing station in the current period of time is obtained, and the preliminary power consumption mode of the next period of time is configured according to the power generation data and the power consumption data; based on the preliminary power consumption mode, according to the mapping relationship between the historical power consumption mode and the historical production data, the production data of the photovoltaic silo of the mixing station in the next period of time is predicted; if the production data does not meet the preset production standards, the final power consumption mode of the next period of time is configured based on the preliminary power consumption mode, otherwise, the preliminary power consumption mode is directly used as the final power consumption mode, so that through the scientific power mode configuration, the economical and efficient use of electricity can be realized, and stable power support can be provided for the production activities of the mixing station.

[0085] An embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0086] Specifically, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0087] Specifically, in this embodiment, the processor may be configured to perform the following steps through a computer program: S201, obtaining weather forecast data for the surrounding environment of the photovoltaic silo of the mixing station for a period of time; S202, predicting the power generation data of the photovoltaic modules in the next period of time using a pre-trained photovoltaic power generation prediction model according to the weather forecast data; S203, obtaining the electricity consumption data of the photovoltaic silo of the mixing station in the current period, and configuring the preliminary electricity consumption mode for the next period according to the predicted power generation data and the current electricity consumption data; S204, based on the preliminary electricity consumption pattern of the next period, according to the mapping relationship between the historical electricity consumption pattern and the historical production data, predicting the production data of the photovoltaic silo of the mixing station in the next period; S205: If the production data for the next time period does not meet the preset production standards, the final power consumption mode for the next time period is configured based on the preliminary power consumption mode. Otherwise, the preliminary power consumption mode is directly used as the final power consumption mode, and the environmental parameter configuration scheme of the silo environmental auxiliary equipment under the final power consumption mode is obtained. Based on the environmental parameter configuration scheme, the environmental suitability index of the silo is calculated, and the environmental parameter configuration scheme in the final power consumption mode is fine-tuned according to the environmental suitability index.

[0088] It can be seen that the weather forecast data of the surrounding environment of the photovoltaic silo of the mixing station for the next period of time is obtained; according to the weather forecast data, the photovoltaic power generation prediction model is used to predict the power generation data of the photovoltaic components in the next period of time; the power consumption data of the photovoltaic silo of the mixing station in the current period of time is obtained, and the preliminary power consumption mode of the next period of time is configured according to the power generation data and the power consumption data; based on the preliminary power consumption mode, according to the mapping relationship between the historical power consumption mode and the historical production data, the production data of the photovoltaic silo of the mixing station in the next period of time is predicted; if the production data does not meet the preset production standards, the final power consumption mode of the next period of time is configured based on the preliminary power consumption mode, otherwise, the preliminary power consumption mode is directly used as the final power consumption mode, so that through the scientific power mode configuration, the economical and efficient use of electricity can be realized, and stable power support can be provided for the production activities of the mixing station.

[0089] The above describes in detail the structure, features and effects of the present invention based on the embodiments shown in the drawings. The above is only a preferred embodiment of the present invention, but the present invention is not limited to the scope of implementation shown in the drawings. Any changes made according to the concept of the present invention, or modifications to equivalent embodiments with equivalent changes, which still do not exceed the spirit covered by the description and drawings, should be within the protection scope of the present invention.

Claims

1. A photovoltaic silo control method for a mixing station, characterized in that: The method comprises: Obtain the weather forecast data for the surrounding environment of the photovoltaic silo of the mixing station for the next period of time; According to the weather forecast data, using a pre-trained photovoltaic power generation prediction model, predicting the power generation data of the photovoltaic components in the next period; Obtain the electricity consumption data of the photovoltaic silo of the mixing station in the current period, and configure the preliminary electricity consumption mode for the next period based on the predicted power generation data and the current electricity consumption data; Based on the preliminary electricity consumption pattern of the next period, and according to the mapping relationship between the historical electricity consumption pattern and the historical production data, the production data of the photovoltaic silo of the mixing station in the next period is predicted; If the production data for the next time period does not meet the preset production standards, the final power consumption mode for the next time period will be configured based on the preliminary power consumption mode. Otherwise, the preliminary power consumption mode will be directly used as the final power consumption mode, and the environmental parameter configuration plan of the silo environmental auxiliary equipment under the final power consumption mode will be obtained. Based on the environmental parameter configuration plan, the environmental suitability index of the silo will be calculated, and the environmental parameter configuration plan in the final power consumption mode will be fine-tuned according to the environmental suitability index.

2. The method according to claim 1, characterized in that The configuring of a preliminary power consumption mode for the next period according to the predicted power generation data and the current power consumption data includes: If the power generation in the predicted power generation data is greater than the power consumption in the current power consumption data, the preliminary power consumption mode is configured as a preset high production performance mode, and the power consumption corresponding to the preset high production performance mode does not exceed the power generation in the predicted power generation data; Otherwise, the preliminary power consumption mode is configured as a preset energy-saving production mode, and the power consumption corresponding to the preset energy-saving production mode does not exceed the power generation in the predicted power generation data.

3. The method according to claim 2, characterized in that The configuring the final power usage mode for the next period based on the preliminary power usage mode includes: When the initial power consumption mode is the preset energy-saving production mode, an early warning signal is sent to the control terminal of the photovoltaic silo of the mixing station; After receiving the backup power supply activation signal returned by the control terminal, the backup power supply is enabled and the preliminary power consumption mode is adjusted so that the production data reaches the preset production standard.

4. The method according to claim 3, characterized in that The fine-tuning of the environmental parameter configuration scheme in the final power consumption mode according to the environmental suitability index includes: If the final power consumption mode is directly the preliminary power consumption mode, and the environmental suitability index is less than the preset environmental threshold, the environmental parameter configuration scheme in the final power consumption mode is fine-tuned so that the environmental suitability index reaches the preset environmental threshold, and the corresponding power consumption does not exceed the power generation in the predicted power generation data, and the corresponding production data meets the preset production standard; If the final power consumption mode is not the preliminary power consumption mode, and the environmental suitability index is less than the preset environmental threshold, the environmental parameter configuration scheme in the final power consumption mode is fine-tuned so that the environmental suitability index reaches the preset environmental threshold and the corresponding production data meets the preset production standards.

5. A photovoltaic silo control system for a mixing station, characterized in that: The system comprises: The first acquisition module is used to obtain the weather forecast data of the surrounding environment of the photovoltaic silo of the mixing station for a period of time; A first prediction module is used to predict the power generation data of the photovoltaic components in the next period of time according to the weather forecast data and using a pre-trained photovoltaic power generation prediction model; The first configuration module is used to obtain the power consumption data of the photovoltaic silo of the mixing station in the current period, and configure the preliminary power consumption mode of the next period according to the predicted power generation data and the current power consumption data; The second prediction module is used to predict the production data of the photovoltaic silo of the mixing station in the next period based on the preliminary power consumption pattern in the next period and the mapping relationship between the historical power consumption pattern and the historical production data; The second configuration module is used to configure the final power consumption mode for the next time period based on the preliminary power consumption mode if the production data for the next time period does not meet the preset production standards. Otherwise, the preliminary power consumption mode is directly used as the final power consumption mode, and the environmental parameter configuration scheme of the silo environmental auxiliary equipment under the final power consumption mode is obtained. Based on the environmental parameter configuration scheme, the environmental suitability index of the silo is calculated, and the environmental parameter configuration scheme in the final power consumption mode is fine-tuned according to the environmental suitability index.

6. The system according to claim 5, characterized in that The first configuration module is specifically used to: If the power generation in the predicted power generation data is greater than the power consumption in the current power consumption data, the preliminary power consumption mode is configured as a preset high production performance mode, and the power consumption corresponding to the preset high production performance mode does not exceed the power generation in the predicted power generation data; Otherwise, the preliminary power consumption mode is configured as a preset energy-saving production mode, and the power consumption corresponding to the preset energy-saving production mode does not exceed the power generation in the predicted power generation data.

7. The system according to claim 6, characterized in that The second configuration module is specifically used to: When the initial power consumption mode is the preset energy-saving production mode, an early warning signal is sent to the control terminal of the photovoltaic silo of the mixing station; After receiving the backup power supply activation signal returned by the control terminal, the backup power supply is enabled and the preliminary power consumption mode is adjusted so that the production data reaches the preset production standard.

8. The system according to claim 7, characterized in that The second configuration module is specifically used to: If the final power consumption mode is directly the preliminary power consumption mode, and the environmental suitability index is less than the preset environmental threshold, the environmental parameter configuration scheme in the final power consumption mode is fine-tuned so that the environmental suitability index reaches the preset environmental threshold, and the corresponding power consumption does not exceed the power generation in the predicted power generation data, and the corresponding production data meets the preset production standard; If the final power consumption mode is not the preliminary power consumption mode, and the environmental suitability index is less than the preset environmental threshold, the environmental parameter configuration scheme in the final power consumption mode is fine-tuned so that the environmental suitability index reaches the preset environmental threshold and the corresponding production data meets the preset production standards.

9. A storage medium, characterized in that: The storage medium stores a computer program, wherein the computer program is configured to execute the method according to any one of claims 1 to 4 when executed.

10. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to perform the method according to any one of claims 1 to 4.

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