Park energy optimization configuration method, system, equipment and medium

Abstract

The invention provides a park energy optimization configuration method, system and device and a medium, and belongs to the technical field of park energy management. The method comprises the following steps: acquiring equipment state data and people flow data in a park in real time, judging the current energy consumption condition and personnel activity condition of each area and each equipment, and generating power consumption data of each area and each equipment in the park; formulating a control strategy of a lighting system, an air conditioning system and standby equipment in the park according to the real-time analysis result, generating a corresponding control instruction, and issuing the control instruction to related equipment for execution; historical electricity price data and weather forecast information are combined, the future electricity price trend is predicted, and the state of the energy storage equipment is monitored; according to the monitoring result of the future electricity price trend and the state of the energy storage equipment, making and executing a charging plan of the energy storage equipment; and calculating an energy consumption trend and an energy-saving effect by using a data analysis algorithm based on the power utilization data of each region and each device in the park, identifying the device with abnormal energy consumption, and performing power regulation on the device with abnormal energy consumption.

Description

Technical Field

[0001] The present invention belongs to the technical field of park energy management, and more specifically relates to a park energy optimization configuration method, system, equipment and medium. Background Art

[0002] With the rapid development of social economy, the importance of industrial parks as core areas of industrial production and commercial activities has become increasingly prominent. However, with the continuous expansion of industrial parks and the increase in the number of enterprises settled in them, energy consumption has also increased dramatically. The efficiency, flexibility and sustainability of industrial park energy management have become important challenges faced by industrial park managers and decision makers.

[0003] In the existing park energy management system, the problem of energy supply and demand mismatch is particularly prominent. Due to the significant volatility and uncertainty of energy demand among enterprises in the park, and the lack of sufficient flexibility and response speed of the energy supply system, it is difficult for energy supply to meet the changes in actual demand. This mismatch not only causes serious waste of energy and increases the operating costs of the park, but may also seriously affect the normal operation and production efficiency of the park due to insufficient energy supply.

[0004] Specifically, the existing park energy management has the following major defects: 1. Lack of real-time monitoring and intelligent analysis capabilities: Traditional energy management systems can only provide simple data recording and query functions, but cannot monitor energy usage in real time and conduct intelligent analysis. This makes it difficult for managers to understand the energy supply and demand status of the park in a timely manner and make accurate decisions and adjustments.

[0005] 2. Insufficient flexibility of energy supply system: The existing energy supply system often adopts fixed power supply modes and strategies, and lacks the ability to flexibly adjust according to actual needs. When the energy demand of enterprises in the park changes, the supply system is difficult to respond in time, resulting in insufficient or excessive energy supply.

[0006] 3. Low energy efficiency: Due to the mismatch between energy supply and demand and the lack of effective energy-saving measures, the energy efficiency of the park is generally low. This not only increases the energy consumption and operating costs of the park, but also aggravates environmental pollution and carbon emissions.

[0007] 4. Lack of a unified energy management platform: The energy management systems of various enterprises are often independent of each other and lack a unified management and coordination mechanism. This leads to low overall energy management efficiency in the park and makes it difficult to achieve optimal allocation and efficient use of energy. Summary of the invention

[0008] In response to the above problems, the purpose of the present invention is to provide a method, system, equipment and medium for optimizing the energy configuration of a park, which can fully consider the energy demand fluctuations of various enterprises in the park and the flexibility of the energy supply system, realize the optimal configuration and efficient utilization of energy through intelligent means, and provide strong support for the sustainable development of the park.

[0009] In order to achieve the above object, the present invention is implemented through the following technical solutions: In a first aspect, an embodiment of the present application provides a method for optimizing energy configuration in a park, including: Use IoT technology to collect real-time equipment status data and human flow data in the park, use artificial intelligence algorithms for real-time analysis, determine the current energy consumption and personnel activities of each area and each device, and generate electricity consumption data for each area and each device in the park; Formulate control strategies for the lighting system, air conditioning system and backup equipment in the park based on real-time analysis results, generate corresponding control instructions and send them to relevant equipment for execution; Use time series analysis algorithms, combined with historical electricity price data and weather forecast information, to predict future electricity price trends and monitor the status of energy storage equipment; Formulate and implement a charging plan for energy storage equipment based on future electricity price trends and monitoring results of energy storage equipment status; Based on the power consumption data of each area and each device in the park, the data analysis algorithm is used to calculate the energy consumption trend and energy-saving effect, identify the equipment with abnormal energy consumption, and adjust the power of the equipment with abnormal energy consumption; Regularly summarize the power consumption data of each device, evaluate the energy-saving effect, and generate energy consumption feedback reports.

[0010] In an optional implementation, the Internet of Things technology is used to collect real-time equipment status data and human flow data in the park, and artificial intelligence algorithms are used for real-time analysis to determine the current energy consumption and personnel activities of each area and each device, and generate power consumption data of each area and each device in the park, including: Use IoT technology to collect real-time data on lighting brightness, air conditioning temperature, backup equipment power status, and human flow in the park; Based on the collected data, the long short-term memory network algorithm is used for real-time analysis to determine the energy consumption data, personnel density information, personnel information and area occupancy information of each area and each device, and generate analysis results.

[0011] In an optional implementation, the control strategy for the lighting system, air conditioning system and backup equipment in the park is formulated according to the real-time analysis results, and the corresponding control instructions are generated and sent to the relevant equipment for execution, including: Formulate a device control strategy based on the analysis results, wherein the device control strategy includes adjusting the brightness parameters of the lighting system, switching the operating mode of the air conditioning system, shutting down the backup equipment or reducing the power of the backup equipment; The device control strategy is converted into specific control instructions and sent to the corresponding device for execution via the OPC UA protocol.

[0012] In an optional implementation, the use of a time series analysis algorithm, combined with historical electricity price data and weather forecast information, to predict future electricity price trends and monitor the state of energy storage equipment includes: Collect historical electricity price data and real-time electricity price data, and perform cleaning, denoising and normalization processing; The ARIMA model is used to build a forecasting model, and historical electricity price data is used for model training; Collect weather forecast information and use it as input variables, together with real-time electricity price data, into the prediction model to generate electricity price trends for a period of time in the future; The data acquisition card is used to collect monitoring data of the temperature sensor, voltage sensor, and current sensor preset on the energy storage device as the energy storage device status information.

[0013] In an optional implementation, formulating and executing a charging plan for the energy storage device according to the monitoring results of the future electricity price trend and the energy storage device status includes: By using machine learning and Kalman filter algorithm models to collect, preprocess, analyze and evaluate the status information of energy storage equipment, the monitoring results of the energy storage equipment status are determined; the monitoring results include equipment status, fault warning information and performance evaluation results; Based on the electricity price forecast and the status monitoring results of the energy storage equipment, the particle swarm optimization algorithm is used to formulate the charging plan of the energy storage equipment to ensure that the corresponding energy storage equipment is charged during the low electricity price period and discharged during the peak period during the day; The charging plan is converted into charging instructions and sent to the energy storage device for execution via the Modbus protocol.

[0014] In an optional implementation, based on the power consumption data of each area and each device in the park, the energy consumption trend and energy-saving effect are calculated using a data analysis algorithm, the device with abnormal energy consumption is identified, and the power of the device with abnormal energy consumption is adjusted, including: Based on the electricity consumption data of each area and each device in the park, a cluster analysis algorithm is used to calculate the energy consumption trend and energy-saving effect index; the cluster analysis algorithm uses Chebyshev distance to calculate the distance from the data point to the cluster center; The energy consumption trend and energy-saving effect indicators are judged based on the preset thresholds. When the energy consumption trend and energy-saving effect indicators are identified to be abnormal, the corresponding energy consumption abnormal equipment is determined, and the Modbus protocol is used to reduce the power consumption of the equipment.

[0015] In an optional implementation, the periodic aggregation of power consumption data of each device, evaluation of energy-saving effects, and generation of an energy consumption feedback report includes: Regularly summarize the power consumption data of each device, and use Tableau tools to calculate the power consumption, power consumption time, and peak power consumption of each device; Evaluate the energy-saving effect of each device and identify potential energy-saving space by comparing the corresponding historical data with the set standards; Based on the evaluation results, an energy consumption feedback report is generated, which includes an overview of equipment power consumption, energy-saving effect evaluation, and energy-saving suggestions.

[0016] In a second aspect, the embodiment of the present application further provides a park energy optimization configuration system, including: The data collection module is used to collect the equipment status data and personnel flow data in the park in real time using the Internet of Things technology, and use artificial intelligence algorithms to perform real-time analysis to determine the current energy consumption and personnel activities of each area and each device, and generate electricity consumption data for each area and each device in the park; The control module is used to formulate control strategies for the lighting system, air conditioning system and backup equipment in the park based on real-time analysis results, generate corresponding control instructions and send them to relevant equipment for execution; The prediction module is used to use the time series analysis algorithm, combined with historical electricity price data and weather forecast information, to predict future electricity price trends and monitor the status of energy storage equipment; The charging control module is used to formulate and execute the charging plan of the energy storage equipment according to the monitoring results of the future electricity price trend and the status of the energy storage equipment; The energy consumption management module is used to calculate the energy consumption trend and energy-saving effect based on the power consumption data of each area and each device in the park using data analysis algorithms, identify devices with abnormal energy consumption, and adjust the power of devices with abnormal energy consumption; The feedback module is used to regularly summarize the power consumption data of each device, evaluate the energy-saving effect, and generate energy consumption feedback reports.

[0017] In a third aspect, an embodiment of the present application further provides an electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein when the processor executes the program, the steps of the campus energy optimization configuration method as described in any one of the above items are implemented.

[0018] In a fourth aspect, an embodiment of the present application further provides a storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps of the campus energy optimization configuration method as described in any one of the above items are implemented.

[0019] It can be seen from the above technical solutions that the present invention has the following advantages: This application uses the Internet of Things technology to collect real-time equipment status data and human flow data in the park, and combines it with artificial intelligence algorithms for real-time analysis, which can accurately determine the current energy consumption of each area and each device and the activities of personnel, thereby generating accurate electricity consumption data. This helps to detect energy consumption anomalies in a timely manner and optimize energy use.

[0020] Based on the real-time analysis results, this application develops and implements intelligent control strategies for lighting systems, air conditioning systems, and backup equipment, such as adjusting lighting brightness, switching air conditioning operation modes, shutting down or reducing backup equipment power, etc. These strategies can be automatically adjusted according to actual needs and personnel activities to achieve efficient use of energy.

[0021] This application uses time series analysis algorithms to predict future electricity price trends, and combines the monitoring results of the energy storage device status to formulate and implement a charging plan for the energy storage device. This helps to charge during low electricity price periods and discharge during peak periods, thereby reducing energy costs.

[0022] This application uses data analysis algorithms to calculate energy consumption trends and energy-saving effects, identify devices with abnormal energy consumption, and adjust their power. This helps to promptly discover and solve energy consumption problems and further improve energy efficiency.

[0023] This application can regularly summarize the power consumption data of each device, evaluate the energy-saving effect, and generate energy consumption feedback reports. This helps enterprises understand the current energy usage status, discover potential energy-saving space, and formulate corresponding improvement measures. Through continuous improvement, the level of energy management is continuously improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solution of the present invention, the accompanying drawings required for use in the description will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0025] Figure 1 A flow chart of the campus energy optimization configuration method provided for this application.

[0026] Figure 2 A schematic diagram of the structure of the campus energy optimization configuration system provided for this application.

[0027] Figure 3 A schematic diagram of the structure of the electronic device provided in this application. DETAILED DESCRIPTION

[0028] In the specific steps of the building cooling and heating load adaptive calculation method described in detail below, various embodiments of the present disclosure will be described more comprehensively. The present disclosure may have various embodiments, and adjustments and changes may be made therein. However, it should be understood that there is no intention to limit the various embodiments of the present disclosure to the specific embodiments disclosed herein, but the present disclosure should be understood to cover all adjustments, equivalents and / or alternatives that fall within the spirit and scope of the various embodiments of the present disclosure.

[0029] Hereinafter, the terms "include" or "may include" used in various embodiments of the present disclosure indicate the presence of disclosed functions, operations, or elements, and do not limit the addition of one or more functions, operations, or elements. In addition, as used in various embodiments of the present disclosure, the terms "include", "have", and their cognates are intended only to indicate specific features, numbers, steps, operations, elements, components, or a combination of the foregoing, and should not be understood as first excluding the presence of one or more other features, numbers, steps, operations, elements, components, or a combination of the foregoing or the possibility of adding one or more features, numbers, steps, operations, elements, components, or a combination of the foregoing.

[0030] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] See also Figure 1 The figure is a method flow chart of a method for optimizing energy configuration in a park in a specific embodiment, the method comprising: S1: Use IoT technology to collect real-time equipment status data and personnel flow data within the park, use artificial intelligence algorithms to perform real-time analysis, determine the current energy consumption and personnel activities of each area and each device, and generate electricity consumption data for each area and each device in the park.

[0032] In a specific implementation method, the Internet of Things technology is first used to collect real-time data on lighting brightness, air-conditioning temperature, backup equipment power status and traffic flow in the park; then, based on the collected data, a long short-term memory network algorithm is used to perform real-time analysis to determine the energy consumption data, personnel density information, personnel information and area occupancy information of each area and each device, and generate analysis results.

[0033] S2: Develop control strategies for the lighting system, air conditioning system, and backup equipment within the park based on real-time analysis results, generate corresponding control instructions, and send them to relevant equipment for execution.

[0034] In a specific real-time manner, firstly, a device control strategy is formulated according to the analysis results, wherein the device control strategy includes adjusting the brightness parameters of the lighting system, switching the operation mode of the air-conditioning system, shutting down the backup equipment or reducing the power of the backup equipment.

[0035] Then, the device control strategy is converted into specific control instructions and sent to the corresponding device for execution through the OPC UA protocol.

[0036] As an example, this step can realize intelligent control of the lighting system, energy-saving optimization of the air-conditioning system and intelligent management of backup equipment.

[0037] As an important part of the energy consumption of the park, the lighting system has great energy-saving potential. By installing intelligent sensors and controllers to implement the control strategy of the lighting system, the lighting system can be automatically adjusted. For example, when there are few people or at night, the intelligent system can automatically adjust the lighting brightness according to the light intensity and crowd density, and even completely turn it off in some areas. This intelligent regulation not only reduces energy consumption, but also improves the efficiency of the lighting system, while ensuring the safety and comfort of the park.

[0038] As another major energy consumption of the park, the air conditioning system and its energy-saving strategy are also crucial. By installing intelligent temperature control systems and sensors to implement the control strategy of the air conditioning system, the park can monitor indoor temperature, humidity and other parameters in real time, and automatically adjust the operating status of the air conditioner according to actual needs. For example, during periods of sparse personnel, the air conditioning system can automatically switch to energy-saving mode, reduce cooling or heating power, while maintaining the comfort of the indoor environment. In addition, the air conditioning operation strategy can be adjusted in advance according to the weather forecast and the temperature difference between indoor and outdoor to avoid unnecessary energy consumption.

[0039] In addition, the park is usually equipped with a large number of backup equipment, such as backup generators and backup power supplies. These devices are in standby mode under normal circumstances, but they also consume a certain amount of electricity. Intelligent scheduling of backup equipment is achieved through backup equipment control strategies. For example, during periods of sparse personnel or low power demand, the power of backup equipment can be automatically shut down or reduced to reduce unnecessary energy consumption.

[0040] S3: Use time series analysis algorithms, combined with historical electricity price data and weather forecast information, to predict future electricity price trends and monitor the status of energy storage equipment.

[0041] In the specific real-time method, historical electricity price data and real-time electricity price data are first collected, and then cleaned, denoised and normalized; then the ARIMA model is used to build a prediction model, and the historical electricity price data is used for model training; at this time, weather forecast information is collected and used as an input variable, and is input into the prediction model together with the real-time electricity price data to generate the electricity price trend for a period of time in the future.

[0042] At the same time, the data acquisition card is used to collect monitoring data of the temperature sensor, voltage sensor, and current sensor preset on the energy storage device as the energy storage device status information.

[0043] S4: Develop and implement a charging plan for energy storage equipment based on future electricity price trends and monitoring results of the energy storage equipment status.

[0044] In a specific implementation, first, the status information of the energy storage device is collected, preprocessed, analyzed and evaluated by using machine learning and Kalman filter algorithm models to determine the monitoring results of the energy storage device status; the monitoring results include device status, fault warning information and performance evaluation results.

[0045] Then, based on the electricity price forecast and the status monitoring results of the energy storage equipment, the particle swarm optimization algorithm is used to formulate a charging plan for the energy storage equipment to ensure that the corresponding energy storage equipment is charged during the low electricity price period and discharged during the peak period during the day.

[0046] Finally, the charging plan is converted into charging instructions and sent to the energy storage device for execution via the Modbus protocol.

[0047] Through this step, low-cost electricity can be fully utilized, effectively reducing electricity costs.

[0048] S5: Based on the electricity consumption data of each area and each device in the park, the data analysis algorithm is used to calculate the energy consumption trend and energy-saving effect, identify the equipment with abnormal energy consumption, and adjust the power of the equipment with abnormal energy consumption.

[0049] In a specific implementation, firstly, based on the electricity consumption data of each area and each device in the park, a cluster analysis algorithm is used to calculate the energy consumption trend and energy-saving effect index; the cluster analysis algorithm uses Chebyshev distance to calculate the distance from the data point to the cluster center.

[0050] Then, the energy consumption trend and energy-saving effect index are judged based on the preset threshold. When the energy consumption trend and energy-saving effect index are identified to be abnormal, the corresponding energy consumption abnormal equipment is determined, and the Modbus protocol is used to reduce the power consumption of the equipment.

[0051] This step can monitor the power consumption of each area and each device in the park in real time, helping managers to see clearly which places are still "secretly" consuming power during low-demand periods. It can also automatically adjust the power consumption of some equipment and adjust the output according to the demand size to avoid wasting unnecessary electricity. Even better, this step can also analyze power consumption habits through historical data to help managers develop more appropriate energy-saving plans and achieve more accurate power saving.

[0052] S6: Regularly summarize the power consumption data of each device, evaluate the energy-saving effect, and generate energy consumption feedback reports.

[0053] In a specific implementation method, the power consumption data of each device is first regularly summarized, and the power consumption, power consumption time, and power consumption peak of each device are calculated using the Tableau tool. Then, by comparing the corresponding historical data and set standards, the energy-saving effect of each device is evaluated, and potential energy-saving space is identified. Finally, based on the evaluation results, an energy consumption feedback report is generated that includes an overview of the power consumption of the device, an evaluation of the energy-saving effect, and energy-saving suggestions.

[0054] As an example, first of all, we take a periodic approach to centrally organize and analyze the power consumption data of all equipment in the park, and use Tableau, a powerful data analysis tool, to accurately calculate the power consumption, power consumption duration and peak power consumption of each device. Then, compare these key data with past historical records and preset energy-saving standards to evaluate the energy-saving effect of each device and keenly capture those areas with further potential for energy saving. Finally, based on the detailed evaluation results, an energy consumption feedback report is compiled, which not only outlines the power consumption of each device, but also deeply evaluates the energy-saving effect, and puts forward targeted energy-saving improvement suggestions, aiming to help the park achieve greener and more efficient energy management.

[0055] Through this step, managers can regularly grasp energy consumption data and analyze where there is room for improvement. This can not only save electricity bills in the short term, but also help the park gradually form a consciousness of energy conservation among all employees. Every department and every person can participate in energy conservation, and the electricity bills saved can be used to improve the park environment or other projects.

[0056] In this embodiment, efficient energy utilization and cost reduction are achieved through real-time data collection, intelligent analysis, intelligent control, electricity price forecasting and energy storage optimization, energy consumption anomaly identification and power regulation, energy consumption feedback and continuous improvement, etc., providing strong support for the sustainable development of the enterprise.

[0057] like Figure 2As shown, the following is an embodiment of the park energy optimization configuration system provided by the embodiment of the present disclosure. The system and the park energy optimization configuration method of the above-mentioned embodiments belong to the same inventive concept. For details not described in detail in the embodiment of the park energy optimization configuration system, please refer to the embodiment of the above-mentioned park energy optimization configuration method.

[0058] A park energy optimization configuration system includes: a data acquisition module 1, a control module 2, a prediction module 3, a charging control module 4, an energy consumption management module 5 and a feedback module 6.

[0059] Data collection module 1 is used to collect equipment status data and personnel flow data in the park in real time using Internet of Things technology, and use artificial intelligence algorithms to perform real-time analysis to determine the current energy consumption and personnel activities of each area and each device, and generate electricity consumption data for each area and each device in the park.

[0060] Control module 2 is used to formulate control strategies for the lighting system, air conditioning system and backup equipment in the park according to the real-time analysis results, generate corresponding control instructions and send them to related equipment for execution.

[0061] The prediction module 3 is used to use the time series analysis algorithm, combined with historical electricity price data and weather forecast information, to predict future electricity price trends and monitor the status of energy storage equipment.

[0062] The charging control module 4 is used to formulate and execute a charging plan for the energy storage device according to the monitoring results of the future electricity price trend and the state of the energy storage device.

[0063] The energy consumption management module 5 is used to calculate the energy consumption trend and energy-saving effect based on the power consumption data of each area and each device in the park, use the data analysis algorithm, identify the equipment with abnormal energy consumption, and adjust the power of the equipment with abnormal energy consumption.

[0064] The feedback module 6 is used to regularly summarize the power consumption data of each device, evaluate the energy saving effect, and generate an energy consumption feedback report.

[0065] The park energy optimization configuration system provided in this embodiment can fully consider the energy demand fluctuations of various enterprises in the park and the flexibility of the energy supply system, realize the optimal configuration and efficient utilization of energy through intelligent means, and provide strong support for the sustainable development of the park.

[0066] Figure 3 A schematic diagram of the hardware structure of an electronic device for implementing various embodiments of the present invention.

[0067] The park energy optimization configuration method provided in the embodiment of the present application can be applied to electronic devices. Those skilled in the art will appreciate that the electronic device structure involved in the embodiment of the present invention does not constitute a limitation on the electronic device, and the electronic device may include more or fewer components than shown in the figure, or combine certain components, or arrange different components. In the embodiment of the present invention, the electronic device includes but is not limited to a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments of the present application described and / or required herein.

[0068] The electronic device may include a processor, an external memory interface, an internal memory, a universal serial bus (USB) interface, a charging management module, a power management module, a battery, a wireless communication module, an audio module, a speaker, a microphone, a sensor module, buttons, a camera, a display, and a SIM card interface, etc.

[0069] The processor may include one or more processing units, for example, the processor may include a central processing unit (CPU), an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, a memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural-network processing unit (NPU), etc. Among them, different processing units may be independent devices or integrated into one or more processors.

[0070] The processor can be the nerve center and command center of the electronic device. The controller can generate an operation control signal according to the instruction operation code and timing signal to complete the control of fetching and executing instructions.

[0071] A memory may also be provided in the processor for storing instructions and data. In some embodiments, the memory in the processor is a cache memory. The memory may store instructions or data that the processor has just used or is cyclically used. If the processor needs to use the instruction or data again, it may be directly called from the memory. This avoids repeated access, reduces the waiting time of the processor, and thus improves system efficiency.

[0072] The external memory interface can be used to connect an external memory card, such as a MicroSD card, to expand the storage capacity of the electronic device. The external memory card communicates with the processor through the external memory interface to implement data storage functions. For example, files such as music and videos can be saved in the external memory card.

[0073] The internal memory can be used to store computer executable program codes, which include instructions. The processor executes various functional applications and data processing of the electronic device by running the instructions stored in the internal memory. The internal memory may include a program storage area and a data storage area. The internal memory may include a high-speed random access memory and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, a universal flash storage (UFS), etc.

[0074] The wireless communication function of an electronic device can be realized through an antenna, a wireless communication module, a modem processor, and a baseband processor.

[0075] The wireless communication module can provide wireless communication solutions for electronic devices, including wireless local area networks (WLAN) (such as wireless fidelity (Wi-Fi) networks), Bluetooth (BT), global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), infrared technology (IR), etc.

[0076] Electronic devices can implement audio functions, etc. through audio modules, speakers, receivers, microphones, headphone jacks, and application processors.

[0077] Electronic devices can achieve shooting functions through ISP, camera, video codec, GPU, display and application processor.

[0078] Electronic devices can achieve display functions through GPU, display screen and application processor.

[0079] The GPU is a microprocessor for image processing that connects the display screen and the application processor. The GPU is used to perform mathematical and geometric calculations for graphics rendering. The processor may include one or more GPUs that execute program instructions to generate or change display information.

[0080] The display screen is used to display images, videos, etc. The display screen includes a display panel.

[0081] The above-mentioned electronic equipment realizes the energy optimization configuration method of the building park in this application by integrating technologies such as the Internet of Things, artificial intelligence, time series analysis and machine learning, and realizes real-time monitoring of the energy consumption of equipment in the park, intelligent regulation, electricity price prediction and energy storage optimization, energy consumption anomaly identification and adjustment, and regular energy consumption feedback and evaluation, which significantly improves energy utilization efficiency, reduces operating costs, and promotes the beneficial effects of sustainable development of the park.

[0082] The storage medium provided in the present application stores a program product that can implement a method for optimizing campus energy configuration.

[0083] The energy optimization configuration method of the park includes: using the Internet of Things technology to collect equipment status data and personnel flow data in the park in real time, using artificial intelligence algorithms for real-time analysis, judging the current energy consumption and personnel activities of each area and each device, and generating electricity consumption data for each area and each device in the park; formulating control strategies for the lighting system, air-conditioning system and backup equipment in the park based on the real-time analysis results, generating corresponding control instructions and sending them to relevant equipment for execution; using time series analysis algorithms, combined with historical electricity price data and weather forecast information, predicting future electricity price trends and monitoring the status of energy storage equipment; formulating and executing charging plans for energy storage equipment based on the monitoring results of future electricity price trends and the status of energy storage equipment; based on the electricity consumption data of each area and each device in the park, using data analysis algorithms to calculate energy consumption trends and energy-saving effects, identifying equipment with abnormal energy consumption, and adjusting the power of equipment with abnormal energy consumption; regularly summarizing the electricity consumption data of each device, evaluating the energy-saving effect, and generating energy consumption feedback reports.

[0084] In some possible implementations, the campus energy optimization configuration method disclosed herein may be implemented in the form of a program product, which includes a program code. When the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps described in the above “Exemplary Method” section of this specification according to various exemplary implementations of the present disclosure.

[0085] The storage medium of the present disclosure can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0086] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for optimizing energy configuration in a park, characterized in that: include: Use IoT technology to collect real-time equipment status data and human flow data in the park, use artificial intelligence algorithms for real-time analysis, determine the current energy consumption and personnel activities of each area and each device, and generate electricity consumption data for each area and each device in the park; Formulate control strategies for the lighting system, air conditioning system and backup equipment in the park based on real-time analysis results, generate corresponding control instructions and send them to relevant equipment for execution; Use time series analysis algorithms, combined with historical electricity price data and weather forecast information, to predict future electricity price trends and monitor the status of energy storage equipment; Formulate and implement a charging plan for energy storage equipment based on future electricity price trends and monitoring results of energy storage equipment status; Based on the power consumption data of each area and each device in the park, the data analysis algorithm is used to calculate the energy consumption trend and energy-saving effect, identify the equipment with abnormal energy consumption, and adjust the power of the equipment with abnormal energy consumption; Regularly summarize the power consumption data of each device, evaluate the energy-saving effect, and generate energy consumption feedback reports.

2. The park energy optimization configuration method according to claim 1, characterized in that: The IoT technology is used to collect real-time equipment status data and human flow data in the park, and artificial intelligence algorithms are used for real-time analysis to determine the current energy consumption and personnel activities of each area and each device, and generate power consumption data of each area and each device in the park, including: Use IoT technology to collect real-time data on lighting brightness, air conditioning temperature, backup equipment power status, and human flow in the park; Based on the collected data, the long short-term memory network algorithm is used for real-time analysis to determine the energy consumption data, personnel density information, personnel information and area occupancy information of each area and each device, and generate analysis results.

3. The park energy optimization configuration method according to claim 2, characterized in that: The control strategies for the lighting system, air conditioning system and backup equipment in the park are formulated according to the real-time analysis results, and the corresponding control instructions are generated and sent to the relevant equipment for execution, including: Formulate a device control strategy based on the analysis results, wherein the device control strategy includes adjusting the brightness parameters of the lighting system, switching the operating mode of the air conditioning system, shutting down the backup equipment or reducing the power of the backup equipment; The device control strategy is converted into specific control instructions and sent to the corresponding device for execution via the OPC UA protocol.

4. The park energy optimization configuration method according to claim 3 is characterized in that: The time series analysis algorithm is used to combine historical electricity price data and weather forecast information to predict future electricity price trends and monitor the status of energy storage equipment, including: Collect historical electricity price data and real-time electricity price data, and perform cleaning, denoising and normalization processing; The ARIMA model is used to build a forecasting model, and historical electricity price data is used for model training; Collect weather forecast information and use it as input variables, together with real-time electricity price data, into the prediction model to generate electricity price trends for a period of time in the future; The data acquisition card is used to collect monitoring data of the temperature sensor, voltage sensor, and current sensor preset on the energy storage device as the energy storage device status information.

5. The park energy optimization configuration method according to claim 4, characterized in that: The charging plan for the energy storage equipment is formulated and implemented based on the monitoring results of future electricity price trends and the status of the energy storage equipment, including: By using machine learning and Kalman filter algorithm models to collect, preprocess, analyze and evaluate the status information of energy storage equipment, the monitoring results of the energy storage equipment status are determined; the monitoring results include equipment status, fault warning information and performance evaluation results; Based on the electricity price forecast and the status monitoring results of the energy storage equipment, the particle swarm optimization algorithm is used to formulate the charging plan of the energy storage equipment to ensure that the corresponding energy storage equipment is charged during the low electricity price period and discharged during the peak period during the day; The charging plan is converted into charging instructions and sent to the energy storage device for execution via the Modbus protocol.

6. The park energy optimization configuration method according to claim 5, characterized in that: Based on the power consumption data of each area and each device in the park, the data analysis algorithm is used to calculate the energy consumption trend and energy-saving effect, identify the equipment with abnormal energy consumption, and adjust the power of the equipment with abnormal energy consumption, including: Based on the electricity consumption data of each area and each device in the park, a cluster analysis algorithm is used to calculate the energy consumption trend and energy-saving effect index; the cluster analysis algorithm uses Chebyshev distance to calculate the distance from the data point to the cluster center; The energy consumption trend and energy-saving effect indicators are judged based on the preset thresholds. When the energy consumption trend and energy-saving effect indicators are identified to be abnormal, the corresponding energy consumption abnormal equipment is determined, and the Modbus protocol is used to reduce the power consumption of the equipment.

7. The park energy optimization configuration method according to claim 6, characterized in that: The electricity consumption data of each device is regularly summarized, the energy saving effect is evaluated, and an energy consumption feedback report is generated, including: Regularly summarize the power consumption data of each device, and use Tableau tools to calculate the power consumption, power consumption time, and peak power consumption of each device; Evaluate the energy-saving effect of each device and identify potential energy-saving space by comparing the corresponding historical data with the set standards; Based on the evaluation results, an energy consumption feedback report is generated, which includes an overview of equipment power consumption, energy-saving effect evaluation, and energy-saving suggestions.

8. A park energy optimization configuration system, characterized in that: The system adopts the park energy optimization configuration method as described in any one of claims 1 to 7; The system comprises: The data collection module is used to collect the equipment status data and personnel flow data in the park in real time using the Internet of Things technology, and use artificial intelligence algorithms to perform real-time analysis to determine the current energy consumption and personnel activities of each area and each device, and generate electricity consumption data for each area and each device in the park; The control module is used to formulate control strategies for the lighting system, air conditioning system and backup equipment in the park based on real-time analysis results, generate corresponding control instructions and send them to relevant equipment for execution; The prediction module is used to use the time series analysis algorithm, combined with historical electricity price data and weather forecast information, to predict future electricity price trends and monitor the status of energy storage equipment; The charging control module is used to formulate and execute the charging plan of the energy storage equipment according to the monitoring results of the future electricity price trend and the status of the energy storage equipment; The energy consumption management module is used to calculate the energy consumption trend and energy-saving effect based on the power consumption data of each area and each device in the park using data analysis algorithms, identify devices with abnormal energy consumption, and adjust the power of devices with abnormal energy consumption; The feedback module is used to regularly summarize the power consumption data of each device, evaluate the energy-saving effect, and generate energy consumption feedback reports.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the campus energy optimization configuration method as described in any one of claims 1 to 7 are implemented.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the park energy optimization configuration method as described in any one of claims 1 to 7 are implemented.

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