Park energy control method and system, computer equipment and storage medium

Abstract

The invention belongs to the technical field of energy control, and particularly relates to a park energy control method and system, computer equipment and a storage medium, and the method comprises the steps: obtaining energy use data in a park in real time, and recording an equipment operation state corresponding to the energy use data, inputting the energy use data and the equipment operation state into a preset energy analysis model to obtain an energy analysis report; according to the energy analysis report, obtaining equipment demand information and equipment operation characteristics of different equipment in the park, and generating energy scheduling strategies corresponding to the different equipment; and according to the energy scheduling strategy, a corresponding energy scheduling instruction is generated by combining a preset intelligent control algorithm, and according to the energy scheduling instruction, equipment response is monitored in real time and control parameters are adjusted. The method has the effect of improving the energy distribution efficiency in the park.

Description

Technical Field

[0001] The present application relates to the technical field of energy control, and in particular to a campus energy control method, system, computer equipment, and storage medium. Background Art

[0002] At present, with the development of industrial parks and the widespread application of distributed energy, energy management and control within the park has gradually become a key task to ensure efficient use of energy and reduce energy costs.

[0003] Existing park energy management technologies mainly rely on manual monitoring and traditional schedule-based scheduling methods, which usually manage energy equipment within the park by setting fixed energy allocation plans. However, due to the wide variety of equipment and complex energy consumption patterns in industrial parks, traditional energy management methods are unable to cope with real-time changes in energy demand within the park. Traditional schedule-based scheduling methods usually lack real-time feedback mechanisms, making it difficult to dynamically adjust energy allocation strategies based on the actual operating conditions of the equipment, resulting in energy waste and unbalanced equipment loads, which not only limits the improvement of the overall energy efficiency of the park, but also increases the operating costs of the park.

[0004] The above-mentioned existing technical solutions have the following defects: the existing park energy control method is difficult to dynamically adjust the energy allocation strategy according to the actual operation of the equipment, resulting in energy waste and unbalanced equipment load, so there is room for improvement. Summary of the invention

[0005] In order to improve the efficiency of energy distribution within a park, the present application provides a park energy control method, system, computer equipment and storage medium.

[0006] The above-mentioned invention objective of the present application is achieved through the following technical solutions: A park energy control method, the park energy control method comprising: Acquire energy usage data in the park in real time, record the equipment operation status corresponding to the energy usage data, input the energy usage data and the equipment operation status into a preset energy analysis model, and obtain an energy analysis report; According to the energy analysis report, the equipment demand information and equipment operation characteristics of different equipment in the park are obtained, and energy scheduling strategies corresponding to the different equipment are generated; According to the energy scheduling strategy, the corresponding energy scheduling instructions are generated in combination with the preset intelligent control algorithm, and the device response is monitored in real time and the control parameters are adjusted according to the energy scheduling instructions.

[0007] By adopting the above technical solution, by acquiring the energy usage data in the park in real time and recording the equipment operation status corresponding to the energy usage data, the energy usage and operation status of the equipment can be grasped in a timely and accurate manner, providing a reliable data basis for subsequent energy analysis and scheduling; by inputting the energy usage data and the equipment operation status into a preset energy analysis model to obtain an energy analysis report, the energy usage of the equipment can be comprehensively analyzed and evaluated, thereby providing a scientific basis for the energy scheduling and optimization of the equipment; by obtaining the equipment demand information and equipment operation characteristics of different equipment in the park according to the energy analysis report, and generating energy scheduling strategies corresponding to different equipment, a more accurate and efficient energy allocation strategy can be formulated according to the actual needs and operation characteristics of the equipment, thereby improving the overall energy utilization efficiency of the park; by generating corresponding energy scheduling instructions according to the energy scheduling strategy in combination with a preset intelligent control algorithm, and monitoring the equipment response in real time and adjusting the control parameters according to the energy scheduling instructions, the effective implementation of the energy scheduling strategy can be ensured, and dynamic adjustments can be made according to the real-time feedback of the equipment, thereby improving the flexibility and control accuracy of the system.

[0008] In one example, the present application may be further configured as follows: the real-time acquisition of energy usage data in the park and the recording of the equipment operation status corresponding to the energy usage data specifically include: Use sensor networks to monitor real-time power data, real-time heat data, and real-time gas data of each device in the park; The operation time information, the startup frequency information and the load condition information are obtained from the local databases of the different devices.

[0009] By adopting the above technical solution and using the sensor network to monitor the real-time power data, real-time heat data and real-time gas data of each device in the park, it is possible to comprehensively capture the energy consumption and operating environment data of each device in the park, providing detailed data information for energy scheduling; by obtaining operating time information, startup frequency information and load condition information from the local database of different devices, it is possible to have a deeper understanding of the operating characteristics and usage status of the equipment, providing a more accurate and reliable basis for subsequent data analysis and scheduling decisions, thereby improving the response speed and decision-making accuracy of the energy control system of the entire park.

[0010] In one example, the present application may be further configured as follows: the construction of the energy analysis model specifically includes: Acquire historical energy usage data and historical equipment operation status, perform stage classification according to the load condition information, and match the equipment operation status and historical energy usage data of different stages according to the stage classification results to obtain model training data sets of different load stages; The preset machine learning algorithm is iteratively trained according to the model training data set to obtain the energy analysis model.

[0011] By adopting the above technical solution, by obtaining historical energy usage data and historical equipment operating status, performing stage classification according to the load situation information, and matching the equipment operating status and historical energy usage data of different stages according to the stage classification results, model training data sets for different load stages are obtained, which can conduct in-depth analysis of the equipment operating status under different load conditions, and provide a solid data foundation for accurate training of the model; by iteratively training the preset machine learning algorithm according to the model training data set, the energy analysis model is obtained, which can effectively improve the prediction ability and adaptability of the model, so as to more accurately analyze and predict the energy usage and operating status of each equipment in the park in actual applications, and provide strong support for energy scheduling.

[0012] In one example, the present application may be further configured as follows: obtaining equipment demand information and equipment operation characteristics of different equipment in the park according to the energy analysis report, and generating energy scheduling strategies corresponding to different equipment, specifically including: Analyze the equipment demand information of different equipment at different operation stages according to the energy analysis report, prioritize the equipment according to the equipment operation characteristics, and determine the energy allocation order of different equipment; Based on the equipment demand information and the equipment operation characteristics, an optimization algorithm is used to calculate the energy distribution, determine the energy distribution schemes for different equipment, and generate the energy scheduling strategy according to the energy distribution schemes.

[0013] By adopting the above technical solution, by analyzing the equipment demand information of different equipment at different operation stages according to the energy analysis report, and prioritizing the equipment according to the equipment operation characteristics, and determining the energy allocation order of different equipment, it is possible to reasonably allocate energy according to the actual needs and operation characteristics of the equipment, and ensure that key equipment obtains priority energy supply; by calculating the energy distribution based on the equipment demand information and equipment operation characteristics, using an optimization algorithm to determine the energy distribution plan for different equipment, and generating the energy scheduling strategy based on the energy distribution plan, it is possible to ensure the scientificity and rationality of energy distribution, thereby improving the operation efficiency and energy utilization rate of each equipment in the park, and optimizing the overall energy scheduling.

[0014] In one example, the present application may be further configured as follows: generating a corresponding energy scheduling instruction according to the energy scheduling strategy in combination with a preset intelligent control algorithm specifically includes: Determining the energy allocation required for different devices within a specific time period according to the energy scheduling strategy; The energy distribution amount is used as input and combined with a preset intelligent control algorithm to generate the energy control instruction.

[0015] By adopting the above technical solution, by determining the energy allocation required for different devices in a specific time period according to the energy scheduling strategy, the energy usage of each device can be accurately allocated according to the actual needs of the equipment and the scheduling strategy, thereby ensuring the rational use of energy; by taking the energy allocation amount as input and combining it with the preset intelligent control algorithm to generate the energy control instruction, it is possible to effectively combine the advantages of the actual scheduling strategy and the intelligent control algorithm to generate efficient energy scheduling instructions, thereby improving the automation level and scheduling accuracy of the park's energy control.

[0016] In one example, the present application may be further configured as follows: monitoring the device response in real time and adjusting the control parameters according to the energy scheduling instruction specifically includes: Synchronize the operating status of different devices through a distributed control system to ensure that the energy scheduling instructions can be accurately executed; Monitor the response of the equipment to the energy scheduling instruction in real time, and record the operation effect and energy usage of different equipment; After the energy scheduling instruction is executed, an effect evaluation is performed, and if the device fails to achieve the expected effect, the scheduling strategy is re-optimized.

[0017] By adopting the above technical solution, the operating status of different devices is synchronized through a distributed control system to ensure that the energy scheduling instructions can be accurately executed. In the process of energy scheduling, the collaborative work and unified management between different devices can be guaranteed, thereby improving the execution accuracy of the scheduling instructions; by real-time monitoring of the device's response to the energy scheduling instructions and recording the operating results and energy usage of different devices, the execution effect of the scheduling instructions can be tracked in real time to ensure the timeliness and accuracy of energy scheduling; by evaluating the effect after the energy scheduling instructions are executed, the scheduling strategy can be re-optimized when the equipment fails to achieve the expected effect, and the scheduling strategy can be dynamically adjusted and optimized in actual operation to ensure that the energy utilization of the equipment reaches the optimal state, thereby further improving the overall energy management efficiency of the park and the reliability of equipment operation.

[0018] The second object of the invention is achieved by the following technical solutions: A park energy control system, the park energy control system comprising: A data acquisition module is used to acquire energy usage data in the park in real time, record the equipment operation status corresponding to the energy usage data, input the energy usage data and the equipment operation status into a preset energy analysis model, and obtain an energy analysis report; A strategy generation module, used to obtain the equipment demand information and equipment operation characteristics of different equipment in the park according to the energy analysis report, and generate energy scheduling strategies corresponding to different equipment; The instruction generation module is used to generate corresponding energy scheduling instructions according to the energy scheduling strategy and in combination with a preset intelligent control algorithm, and to monitor the device response in real time and adjust the control parameters according to the energy scheduling instructions.

[0019] By adopting the above technical solution, by acquiring the energy usage data in the park in real time and recording the equipment operation status corresponding to the energy usage data, the energy usage and operation status of the equipment can be grasped in a timely and accurate manner, providing a reliable data basis for subsequent energy analysis and scheduling; by inputting the energy usage data and the equipment operation status into a preset energy analysis model to obtain an energy analysis report, the energy usage of the equipment can be comprehensively analyzed and evaluated, thereby providing a scientific basis for the energy scheduling and optimization of the equipment; by obtaining the equipment demand information and equipment operation characteristics of different equipment in the park according to the energy analysis report, and generating energy scheduling strategies corresponding to different equipment, a more accurate and efficient energy allocation strategy can be formulated according to the actual needs and operation characteristics of the equipment, thereby improving the overall energy utilization efficiency of the park; by generating corresponding energy scheduling instructions according to the energy scheduling strategy in combination with a preset intelligent control algorithm, and monitoring the equipment response in real time and adjusting the control parameters according to the energy scheduling instructions, the effective implementation of the energy scheduling strategy can be ensured, and dynamic adjustments can be made according to the real-time feedback of the equipment, thereby improving the flexibility and control accuracy of the system.

[0020] The third objective of the present application is achieved through the following technical solutions: A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned park energy control method when executing the computer program.

[0021] The fourth objective of the present application is achieved through the following technical solutions: A computer-readable storage medium stores a computer program, which implements the steps of the above-mentioned park energy control method when executed by a processor.

[0022] In summary, this application includes the following beneficial technical effects: 1. By acquiring the energy usage data in the park in real time and recording the equipment operation status corresponding to the energy usage data, the energy usage and operation status of the equipment can be grasped in a timely and accurate manner, providing a reliable data basis for subsequent energy analysis and scheduling; by inputting the energy usage data and the equipment operation status into a preset energy analysis model to obtain an energy analysis report, the energy usage of the equipment can be comprehensively analyzed and evaluated, thereby providing a scientific basis for the energy scheduling and optimization of the equipment; by obtaining the equipment demand information and equipment operation characteristics of different equipment in the park according to the energy analysis report, and generating energy scheduling strategies corresponding to different equipment, a more accurate and efficient energy allocation strategy can be formulated according to the actual needs and operation characteristics of the equipment, thereby improving the overall energy utilization efficiency of the park; by generating corresponding energy scheduling instructions according to the energy scheduling strategy and the preset intelligent control algorithm, and monitoring the equipment response in real time and adjusting the control parameters according to the energy scheduling instructions, the effective implementation of the energy scheduling strategy can be ensured, and dynamic adjustments can be made according to the real-time feedback of the equipment, thereby improving the flexibility and control accuracy of the system; 2. By analyzing the equipment demand information of different equipment at different operation stages according to the energy analysis report, and prioritizing the equipment according to its operation characteristics, and determining the energy allocation order of different equipment, it is possible to reasonably allocate energy according to the actual needs and operation characteristics of the equipment, and ensure that key equipment obtains priority energy supply; by using an optimization algorithm to calculate energy allocation based on equipment demand information and equipment operation characteristics, and determining the energy allocation plan for different equipment, and generating the energy scheduling strategy based on the energy allocation plan, it is possible to ensure the scientificity and rationality of energy allocation, thereby improving the operation efficiency and energy utilization rate of each equipment in the park, and optimizing the overall energy scheduling; 3. Synchronize the operating status of different devices through a distributed control system to ensure that the energy scheduling instructions can be accurately executed. During the energy scheduling process, it can ensure the collaborative work and unified management of different devices, thereby improving the execution accuracy of the scheduling instructions; by real-time monitoring of the device's response to the energy scheduling instructions and recording the operating results and energy usage of different devices, it can track the execution effect of the scheduling instructions in real time to ensure the timeliness and accuracy of energy scheduling; by evaluating the effect after the energy scheduling instructions are executed, if the equipment fails to achieve the expected effect, re-optimize the scheduling strategy, and dynamically adjust and optimize the scheduling strategy in actual operation to ensure that the energy utilization of the equipment reaches the optimal state, thereby further improving the overall energy management efficiency of the park and the reliability of equipment operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 is a flow chart of a park energy control method in an embodiment of the present application; Figure 2 This is a flowchart for implementing step S10 in the park energy control method in one embodiment of the present application; Figure 3 It is a flow chart for realizing the energy analysis model in the park energy control method in one embodiment of the present application; Figure 4 is a flowchart for implementing step S20 in the park energy control method in one embodiment of the present application; Figure 5 This is a flowchart for implementing step S30 in the park energy control method in one embodiment of the present application; Figure 6 is another implementation flow chart of step S30 in the park energy control method in one embodiment of the present application; Figure 7 This is a principle block diagram of a park energy control system in one embodiment of the present application; Figure 8 It is a schematic diagram of a device in an embodiment of the present application. DETAILED DESCRIPTION

[0024] The present application is further described in detail below in conjunction with the accompanying drawings.

[0025] In one embodiment, if Figure 1 As shown, the present application discloses a park energy control method, which specifically includes the following steps: S10: Acquire energy usage data in the park in real time, and record the equipment operation status corresponding to the energy usage data, input the energy usage data and the equipment operation status into a preset energy analysis model, and obtain an energy analysis report.

[0026] Specifically, the sensor network distributed on various devices in the park is used to collect power, heat and gas consumption data of each device in real time, and transmit these data to the central control system for storage by wireless or wired means. At the same time, combined with the equipment operation log, the equipment operation status information is recorded, such as the equipment start time, stop time, load changes during operation, etc. The collected energy usage data and the corresponding equipment operation status are then used as input and imported into the pre-built energy analysis model. By analyzing the data, an energy analysis report is generated, including the equipment's energy consumption trend, energy utilization efficiency and potential energy waste points, thereby providing a scientific basis for subsequent energy scheduling.

[0027] S20: According to the energy analysis report, the equipment demand information and equipment operation characteristics of different equipment in the park are obtained, and energy scheduling strategies corresponding to different equipment are generated.

[0028] Specifically, by analyzing the data in the energy analysis report, the energy demand information of each device in the park is determined, including the power demand, heat demand and gas consumption demand of the equipment in different time periods. According to the operating characteristics of each device, such as the equipment priority, load characteristics, startup delay and operating frequency, these devices are classified and sorted according to the urgency of their energy demand and the importance of their operation. According to the priority and sorting results of the equipment, the optimal energy scheduling strategy is designed so that the equipment can maximize the use of energy while meeting its operating needs, reducing energy waste and excessive consumption.

[0029] S30: Generate corresponding energy scheduling instructions according to the energy scheduling strategy and in combination with the preset intelligent control algorithm, monitor the device response in real time and adjust the control parameters according to the energy scheduling instructions.

[0030] Specifically, after generating the energy scheduling strategy, the intelligent control algorithm is used to convert the strategy into specific scheduling instructions, including the energy distribution amount and control parameters of the equipment in a specific time period. The intelligent control algorithm combines the real-time status data of the equipment, such as the current power, heat, load, etc., to dynamically adjust the scheduling strategy to ensure that the execution of the instructions can adapt to the actual operating environment. The scheduling instructions are sent to each device through a distributed control system, and the response of the equipment is monitored in real time. Through the feedback control mechanism, the control parameters are adjusted according to the response effect of the equipment to the scheduling instructions to optimize the operating status of the equipment and ensure that the energy scheduling of the entire park can be executed efficiently and accurately.

[0031] In one embodiment, if Figure 2 As shown, in step S10, the energy usage data in the park is obtained in real time, and the equipment operation status corresponding to the energy usage data is recorded, which specifically includes: S11: Use the sensor network to monitor the real-time power data, real-time heat data, and real-time gas data of each device in the park.

[0032] Specifically, power sensors, heat sensors and gas sensors are installed on various equipment in the park. These sensors can collect energy consumption data of the equipment in real time. The power sensors monitor the power consumption of the equipment, the heat sensors record the heat generated or consumed by the equipment during operation, and the gas sensors monitor the type of gas used by the equipment and its consumption. They are connected to the central control system through the Internet of Things technology, and the transmitted real-time data will be summarized in the central database for subsequent analysis.

[0033] S12: Obtaining operation time information, startup frequency information, and load condition information from local databases of different devices.

[0034] Specifically, by accessing the local database of each device in the park, the historical operation data of the device is extracted, including the cumulative operation time of the device, the number of starts per day, load fluctuations, etc. The operation time information can help determine the service life and maintenance requirements of the equipment, the start frequency information can help evaluate the start reliability and energy efficiency of the equipment, and the load condition information reflects the performance of the equipment under different operating conditions.

[0035] In one embodiment, if Figure 3 As shown, in step S10, the construction of the energy analysis model specifically includes: S101: Obtain historical energy usage data and historical equipment operating status, perform stage classification according to load condition information, and match the equipment operating status and historical energy usage data of different stages according to the stage classification results to obtain model training data sets of different load stages.

[0036] Specifically, the energy usage data and corresponding operating status information of all equipment in the park are extracted from the historical database, and divided into different stages according to the load conditions of the equipment, such as high load stage, low load stage and idle stage. In each stage, the equipment's operating status information and energy usage data are further matched and classified to form multiple stage data sets for training machine learning models. Through repeated iterative training, the model can identify the energy usage characteristics of equipment at different load stages, thereby improving the accuracy and efficiency of subsequent energy analysis.

[0037] S102: Iteratively train a preset machine learning algorithm according to a model training data set to obtain an energy analysis model.

[0038] Specifically, after the model training data set is prepared, the data set is input into the preset machine learning algorithm for training. During the training process, the machine learning algorithm adjusts parameters according to the characteristics of the input data, and continuously optimizes the prediction accuracy of the model through an iterative process. In each iteration, the algorithm will analyze the historical energy usage patterns and operating status of the equipment, and gradually form the ability to predict the future energy needs of the equipment. Through multiple iterative training, the energy analysis model is generated when the error value between the result obtained from the input data and the established result does not exceed 5%.

[0039] In one embodiment, if Figure 4 As shown, in step S20, the equipment demand information and equipment operation characteristics of different equipment in the park are obtained according to the energy analysis report, and energy scheduling strategies corresponding to different equipment are generated, which specifically include: S21: Analyze the equipment demand information of different equipment at different operation stages according to the energy analysis report, prioritize the equipment according to its operation characteristics, and determine the energy allocation order of different equipment.

[0040] Specifically, by carefully analyzing the data in the energy analysis report, the specific energy demand information of each device at different operating stages is identified, such as electricity demand and heat demand during high-load periods, and these devices are prioritized according to the operating characteristics of the equipment, such as equipment importance, failure rate, startup time, load stability, etc., and the energy allocation order of each device in different stages is determined. The result of priority sorting will directly affect the energy allocation amount of the equipment in energy scheduling, so that important equipment and high-load equipment can obtain energy supply first, thereby ensuring the overall operating efficiency and stability of the park.

[0041] S22: Based on the equipment demand information and equipment operation characteristics, an optimization algorithm is used to calculate the energy distribution, determine the energy distribution plan for different equipment, and generate an energy scheduling strategy based on the energy distribution plan.

[0042] Specifically, based on the equipment demand information and operating characteristics obtained in the early stage, a mathematical model of energy distribution is established, and the model is solved using an optimization algorithm to calculate the optimal energy distribution plan for each device in different operating stages. The optimization algorithm takes into account a variety of factors in the solution process, including equipment priority, current energy supply, equipment energy efficiency performance, etc., to ensure the scientificity and rationality of energy distribution. After calculating the energy distribution plan, the system generates a specific energy scheduling strategy based on the plan, and will strictly implement these strategies in subsequent scheduling to achieve the optimal configuration of equipment energy.

[0043] In one embodiment, if Figure 5 As shown, in step S30, the corresponding energy scheduling instructions are generated according to the energy scheduling strategy and the preset intelligent control algorithm, which specifically includes: S31: Determine the energy allocation required for different devices within a specific time period according to the energy scheduling strategy.

[0044] Specifically, based on the previously generated energy scheduling strategy and the current operating status of the equipment, the energy allocation required for each device in a specific time period is accurately calculated. By combining multiple factors such as the equipment's operating stage, load conditions, historical energy consumption data, etc., it is ensured that each device can obtain sufficient energy support at the critical moment of its operation. At the same time, according to the overall energy supply situation in the park, the energy allocation of each device is dynamically adjusted to ensure that the energy use of the entire park can achieve the best balance and avoid reduced efficiency or equipment failure due to excessive concentration or uneven distribution of energy.

[0045] S32: Taking the energy distribution amount as input, and combining it with a preset intelligent control algorithm to generate an energy control instruction.

[0046] Specifically, after determining the energy allocation of each device, the system uses these energy allocation data as input and combines them with the preset intelligent control algorithm to generate specific energy control instructions, including the control of the energy supply of each device within a specific time period, as well as the real-time status of the device operation and changes in the external environment. The intelligent control algorithm can automatically adjust the energy control instructions when energy demand changes through dynamic analysis of real-time data to ensure that the device can obtain the optimal energy supply and avoid excessive consumption or energy waste.

[0047] In one embodiment, if Figure 6 As shown, in step S30, the device response is monitored in real time and the control parameters are adjusted according to the energy scheduling instruction, which specifically includes: S33: Synchronize the operating status of different devices through a distributed control system to ensure that energy scheduling instructions can be accurately executed.

[0048] Specifically, these instructions are sent to each device synchronously using the distributed control system within the park. The distributed control system can monitor the operating status of each device in real time and ensure that the energy scheduling instructions are not interfered with during the transmission process. Through the efficient coordination of the control system, the equipment can quickly adjust its operating parameters after receiving the instructions and perform energy scheduling according to the instructions. When the equipment executes the scheduling instructions, its operation status is continuously monitored to ensure the accuracy and consistency of the instruction execution.

[0049] S34: Monitor the response of the equipment to the energy scheduling instruction in real time, and record the operation effect and energy usage of different equipment.

[0050] Specifically, after the energy scheduling instruction is issued and executed, the system monitors the response of each device in real time through distributed sensors and monitoring equipment, and records the operating results of the equipment after executing the energy scheduling instruction, including the actual energy consumption, operating efficiency, temperature changes and other data of the equipment, and uploads them to the central control system in real time.

[0051] S35: After the energy scheduling instruction is executed, the effect is evaluated, and if the equipment fails to achieve the expected effect, the scheduling strategy is re-optimized.

[0052] Specifically, after the energy scheduling instruction is executed, the system will conduct a comprehensive evaluation of the operating results of the equipment, including the comparison of the actual energy consumption of the equipment with the expected energy consumption, changes in operating efficiency, whether there is energy waste or shortage, etc. If it is found that the equipment fails to achieve the expected results after executing the scheduling instruction, the system will immediately start the optimization process to analyze the causes of the deviation, such as equipment aging, environmental changes, or the scheduling strategy is not suitable for the current situation, etc. Based on these analysis results, the energy scheduling strategy will be re-optimized, the energy allocation plan will be adjusted, and new scheduling instructions will be generated to ensure that the equipment can achieve more efficient energy utilization in the next scheduling cycle and avoid the same problems.

[0053] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0054] In one embodiment, a park energy control system is provided, and the park energy control system corresponds one-to-one to the park energy control method in the above embodiment. Figure 7 As shown, the park energy control system includes a data acquisition module, a strategy generation module, and an instruction generation module. The detailed description of each functional module is as follows: The data acquisition module is used to obtain the energy usage data in the park in real time, record the equipment operation status corresponding to the energy usage data, input the energy usage data and equipment operation status into the preset energy analysis model, and obtain the energy analysis report; The strategy generation module is used to obtain the equipment demand information and equipment operation characteristics of different equipment in the park according to the energy analysis report, and generate energy scheduling strategies corresponding to different equipment; The instruction generation module is used to generate corresponding energy scheduling instructions according to the energy scheduling strategy and the preset intelligent control algorithm, and monitor the device response in real time and adjust the control parameters according to the energy scheduling instructions.

[0055] Optionally, the data acquisition module specifically includes: The sensor network module is used to monitor the real-time power data, real-time heat data and real-time gas data of each device in the park using the sensor network; The information acquisition module is used to obtain the operation time information, startup frequency information and load condition information from the local database of different devices.

[0056] Optionally, the construction of the energy analysis model specifically includes: The data acquisition module is used to obtain historical energy usage data and historical equipment operation status, perform stage classification according to load condition information, and match the equipment operation status and historical energy usage data of different stages according to the stage classification results to obtain model training data sets of different load stages; The model training module is used to iteratively train the preset machine learning algorithm according to the model training data set to obtain the energy analysis model.

[0057] Optionally, the strategy generation module specifically includes: The demand analysis module is used to analyze the equipment demand information of different equipment at different operation stages according to the energy analysis report, and prioritize the equipment according to its operation characteristics to determine the energy allocation order of different equipment; The optimization calculation module is used to calculate the energy distribution based on the equipment demand information and equipment operation characteristics, determine the energy distribution plan for different equipment, and generate an energy scheduling strategy based on the energy distribution plan.

[0058] Optionally, the instruction generation module specifically includes: The allocation amount determination module is used to determine the energy allocation amount required by different devices in a specific time period according to the energy scheduling strategy; The instruction generation module is used to take the energy distribution amount as input and generate energy control instructions in combination with a preset intelligent control algorithm.

[0059] Optionally, the instruction generation module specifically includes: The state synchronization module is used to synchronize the operating status of different devices through the distributed control system to ensure that energy scheduling instructions can be accurately executed; The effect monitoring module is used to monitor the response of the equipment to the energy scheduling instructions in real time and record the operation effect and energy usage of different equipment; The effect evaluation module is used to evaluate the effect after the energy scheduling instruction is executed, and to re-optimize the scheduling strategy when the equipment fails to achieve the expected effect.

[0060] For the specific definition of the park energy control system, please refer to the definition of the park energy control method above, which will not be repeated here. Each module in the above-mentioned park energy control system can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0061] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 8As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a park energy control method is implemented.

[0062] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program: Obtain energy usage data in the park in real time, record the equipment operation status corresponding to the energy usage data, input the energy usage data and equipment operation status into the preset energy analysis model, and obtain an energy analysis report; According to the energy analysis report, obtain the equipment demand information and equipment operation characteristics of different equipment in the park, and generate energy scheduling strategies corresponding to different equipment; According to the energy scheduling strategy, combined with the preset intelligent control algorithm, the corresponding energy scheduling instructions are generated, and the equipment response is monitored in real time and the control parameters are adjusted according to the energy scheduling instructions.

[0063] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented: Obtain energy usage data in the park in real time, record the equipment operation status corresponding to the energy usage data, input the energy usage data and equipment operation status into the preset energy analysis model, and obtain an energy analysis report; According to the energy analysis report, obtain the equipment demand information and equipment operation characteristics of different equipment in the park, and generate energy scheduling strategies corresponding to different equipment; According to the energy scheduling strategy, combined with the preset intelligent control algorithm, the corresponding energy scheduling instructions are generated, and the equipment response is monitored in real time and the control parameters are adjusted according to the energy scheduling instructions.

[0064] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0065] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0066] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A park energy control method, characterized in that: The park energy control method comprises: Acquire energy usage data in the park in real time, record the equipment operation status corresponding to the energy usage data, input the energy usage data and the equipment operation status into a preset energy analysis model, and obtain an energy analysis report; According to the energy analysis report, the equipment demand information and equipment operation characteristics of different equipment in the park are obtained, and energy scheduling strategies corresponding to the different equipment are generated; According to the energy scheduling strategy, the corresponding energy scheduling instructions are generated in combination with the preset intelligent control algorithm, and the device response is monitored in real time and the control parameters are adjusted according to the energy scheduling instructions.

2. The park energy control method according to claim 1, characterized in that: The real-time acquisition of energy usage data in the park and recording of the equipment operation status corresponding to the energy usage data specifically includes: Use sensor networks to monitor real-time power data, real-time heat data, and real-time gas data of each device in the park; The operation time information, the startup frequency information and the load condition information are obtained from the local databases of the different devices.

3. The park energy control method according to claim 2, characterized in that: The construction of the energy analysis model specifically includes: Acquire historical energy usage data and historical equipment operation status, perform stage classification according to the load condition information, and match the equipment operation status and historical energy usage data of different stages according to the stage classification results to obtain model training data sets of different load stages; The preset machine learning algorithm is iteratively trained according to the model training data set to obtain the energy analysis model.

4. The park energy control method according to claim 1, characterized in that: The step of obtaining equipment demand information and equipment operation characteristics of different equipment in the park according to the energy analysis report and generating energy scheduling strategies corresponding to different equipment specifically includes: Analyze the equipment demand information of different equipment at different operation stages according to the energy analysis report, prioritize the equipment according to the equipment operation characteristics, and determine the energy allocation order of different equipment; Based on the equipment demand information and the equipment operating characteristics, an optimization algorithm is used to calculate energy distribution, determine energy distribution plans for different equipment, and generate the energy scheduling strategy based on the energy distribution plan.

5. The park energy control method according to claim 4, characterized in that: The generating of the corresponding energy scheduling instruction according to the energy scheduling strategy and in combination with the preset intelligent control algorithm specifically includes: Determining the energy allocation required for different devices within a specific time period according to the energy scheduling strategy; The energy distribution amount is used as input and combined with a preset intelligent control algorithm to generate the energy control instruction.

6. The park energy control method according to claim 1, characterized in that: The real-time monitoring of device response and adjusting control parameters according to the energy scheduling instruction specifically includes: Synchronize the operating status of different devices through a distributed control system to ensure that the energy scheduling instructions can be accurately executed; Monitor the response of the equipment to the energy scheduling instruction in real time, and record the operation effect and energy usage of different equipment; After the energy scheduling instruction is executed, an effect evaluation is performed, and if the device fails to achieve the expected effect, the scheduling strategy is re-optimized.

7. A park energy control system, characterized in that: The park energy control system includes: a data acquisition module, which is used to acquire energy usage data in the park in real time, and record the equipment operation status corresponding to the energy usage data, and input the energy usage data and the equipment operation status into a preset energy analysis model to obtain an energy analysis report; A strategy generation module, used to obtain the equipment demand information and equipment operation characteristics of different equipment in the park according to the energy analysis report, and generate energy scheduling strategies corresponding to different equipment; The instruction generation module is used to generate corresponding energy scheduling instructions according to the energy scheduling strategy and in combination with a preset intelligent control algorithm, and to monitor the device response in real time and adjust the control parameters according to the energy scheduling instructions.

8. The park energy control system according to claim 7, characterized in that: The data acquisition module specifically includes: The sensor network module is used to monitor the real-time power data, real-time heat data and real-time gas data of each device in the park using the sensor network; The information acquisition module is used to acquire the operation time information, the startup frequency information and the load condition information from the local database of the different devices.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the campus energy control method according to any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the campus energy control method as described in any one of claims 1 to 6 are implemented.

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