Park-oriented energy station remote regulation and control method and system and medium

Abstract

The invention discloses a park-oriented energy station remote regulation and control method and system and a medium, and relates to the technical field of intelligent scheduling, and the method comprises the steps: building a park bidirectional data channel, and collecting a data set in real time; performing operation analysis on the energy station unit, formulating an optimization strategy, performing energy consumption characteristic analysis on the tail end energy consumption unit, and determining time-sharing energy consumption reference information; executing optimization strategy regulation and control monitoring according to the time-sharing reference, and generating feedback data; and performing collaborative optimization on the energy station and the end unit based on the feedback data and the real-time data set to form a linkage control scheme. The technical problems of energy waste and low efficiency caused by the fact that an existing energy station regulation and control system lacks real-time optimization capability of intelligent scheduling and cannot dynamically adjust energy consumption distribution between an energy station and a tail end energy consumption unit according to actual requirements are solved. The technical effects that dynamic optimized operation of the energy station and the tail end energy consumption unit is achieved through intelligent scheduling, the energy utilization efficiency is improved, and energy consumption is reduced are achieved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent control technology, and in particular to a remote control method, system and medium for a park-oriented energy station. Background Art

[0002] With the rapid development of urban parks, energy demand is increasing, especially in the coordination and control between energy stations and end-users within the park. Traditional scheduling methods often rely on manual monitoring and fixed operating modes, lacking flexibility and real-time performance, resulting in low energy efficiency and even energy waste. With the development of intelligent technologies, energy management is gradually moving towards intelligent scheduling, which can dynamically adjust energy supply and consumption based on real-time data and operating status, thereby achieving higher efficiency and energy conservation. However, existing energy management systems are mostly limited to the control of single devices, lack the ability to coordinate and optimize between energy stations and end-users, and fail to fully consider the real-time feedback of multi-dimensional data, resulting in an inability to cope with energy demand fluctuations in complex environments. Therefore, how to achieve coordinated optimization between energy stations and end-users through intelligent scheduling methods, combined with bidirectional data channels to collect and analyze various operating data in real time, to improve the overall operational efficiency and energy utilization of park energy stations, has become a pressing technical challenge. Summary of the Invention

[0003] The present invention provides a remote control method, system and medium for energy stations in a park to solve the technical problems that the existing energy station control system lacks real-time optimization capabilities of intelligent scheduling, cannot dynamically adjust the energy consumption distribution between the energy station and the terminal energy-consuming units according to actual needs, and leads to energy waste and low efficiency. The technical effect of achieving dynamic optimization operation of the energy station and the terminal energy-consuming units through intelligent scheduling, improving energy utilization efficiency and reducing energy consumption is achieved.

[0004] In a first aspect, the present invention provides a remote control method for a park-based energy station, wherein the remote control method for a park-based energy station comprises:

[0005] A two-way data channel is established for the target park, and real-time data collection is performed through the two-way data channel to obtain a two-way real-time data set, wherein the two-way data channel includes an energy station unit and a terminal energy-consuming unit; equipment operation analysis is performed on the energy station unit, and a dynamic optimization operation strategy is formulated; energy consumption characteristics analysis is performed on the terminal energy-consuming unit, and time-sharing energy consumption benchmark information is determined; control and monitoring of the dynamic optimization operation strategy is performed according to the time-sharing energy consumption benchmark information to generate control feedback data; based on the control feedback data and combined with the two-way real-time data set, the energy station unit and the terminal energy-consuming unit are collaboratively optimized to obtain a linkage and collaborative control solution.

[0006] In a second aspect, the present invention further provides a remote control system for energy stations in a park, wherein the remote control system for energy stations in a park comprises:

[0007] Real-time acquisition module: establishes a two-way data channel for the target park, performs real-time acquisition through the two-way data channel, and obtains a two-way real-time data set, wherein the two-way data channel includes an energy station unit and a terminal energy-consuming unit; data analysis module: performs equipment operation analysis on the energy station unit, formulates a dynamic optimization operation strategy, performs energy consumption characteristic analysis on the terminal energy-consuming unit, and determines the time-sharing energy consumption benchmark information; control and monitoring module: performs control and monitoring of the dynamic optimization operation strategy according to the time-sharing energy consumption benchmark information, and generates control feedback data; collaborative optimization module: based on the control feedback data and combined with the two-way real-time data set, the energy station unit and the terminal energy-consuming unit are collaboratively optimized to obtain a linkage and collaborative control solution.

[0008] In a third aspect, the present invention further provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the park-oriented energy station remote control method provided by the present invention.

[0009] The present invention discloses a remote control method, system and medium for a park-oriented energy station, comprising: establishing a two-way data channel for a target park, performing real-time data collection through the two-way data channel to obtain a two-way real-time data set, wherein the two-way data channel includes an energy station unit and a terminal energy-consuming unit; performing equipment operation analysis on the energy station unit, formulating a dynamic optimization operation strategy, performing energy consumption characteristic analysis on the terminal energy-consuming unit, and determining time-sharing energy consumption benchmark information; performing control monitoring of the dynamic optimization operation strategy according to the time-sharing energy consumption benchmark information to generate control feedback data; based on the control feedback data and the two-way real-time data set, coordinating the energy station unit and the terminal energy-consuming unit to obtain a linkage and coordinated control scheme. The remote control method, system and medium for a park-oriented energy station disclosed in the present invention solve the technical problems of the lack of real-time optimization capability of intelligent scheduling in existing energy station control systems, the inability to dynamically adjust the energy consumption distribution between the energy station and the terminal energy-consuming unit according to actual needs, resulting in energy waste and low efficiency, and achieves the technical effect of realizing dynamic optimization operation of the energy station and the terminal energy-consuming unit through intelligent scheduling, improving energy utilization efficiency, and reducing energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 This is a flow chart of the remote control method for energy stations in a park according to the present invention.

[0011] Figure 2 This is a schematic diagram of the structure of the park-oriented energy station remote control system of the present invention.

[0012] Description of the accompanying drawings: real-time acquisition module 11, data analysis module 12, control and monitoring module 13, collaborative optimization module 14. DETAILED DESCRIPTION

[0013] The above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods of the specification to better understand the above technical solution. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments of the present invention. It should be understood that the present invention is not limited to the example embodiments used only to explain the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. In addition, it should be noted that, for the convenience of description, only the parts related to the present invention, rather than all, are shown in the drawings.

[0014] Example 1, as Figure 1 The figure is a flow chart of the remote control method of a park-oriented energy station according to the present invention, wherein the remote control method of a park-oriented energy station includes:

[0015] A two-way data channel is established for the target park, and real-time data collection is performed through the two-way data channel to obtain a two-way real-time data set. The two-way data channel includes an energy station unit and a terminal energy consumption unit.

[0016] Specifically, a two-way data interaction network is constructed in the target park, and the energy station units (such as cooling and heating units, energy storage devices, etc.) of the park are interconnected with the terminal energy consumption units (such as building air conditioners, industrial production lines, etc.) through the Internet of Things protocol to form a two-way data channel. The energy station unit uploads the equipment operating parameters (such as compressor frequency, supply and return water temperature) in real time, and the terminal energy consumption unit synchronously feeds back the terminal energy consumption data (such as instantaneous power, load demand). Finally, based on the spatiotemporal coding rules, the equipment operating parameters and the terminal energy consumption data are aligned and packaged to generate a two-way real-time data set containing the equipment status and energy demand. In summary, through this two-way data channel, seamless data exchange and real-time monitoring between the energy station and the terminal energy consumption unit can be achieved, providing reliable data support for subsequent dynamic optimization and coordinated regulation.

[0017] In some embodiments, a bidirectional data channel is established for a target park, and real-time data collection is performed through the bidirectional data channel to obtain a bidirectional real-time data set. The method includes:

[0018] A bidirectional data channel is established between the energy station unit and the terminal energy consumption unit of the target park through the smart energy management and control platform; data is synchronously collected through the energy station unit through the bidirectional data channel to obtain equipment operating parameters, and data is synchronously collected through the terminal energy consumption unit through the bidirectional data channel to obtain terminal energy consumption data; the equipment operating parameters and the terminal energy consumption data are feature-aligned according to spatiotemporal coding rules to construct the bidirectional real-time data set.

[0019] Specifically, the smart energy management and control platform connects to energy station units via an industrial IoT gateway using the OPC UA protocol. It also connects to end-user units via Modbus-TCP or BACnet protocols. The platform then assigns a unique IP address to each device node and establishes a bidirectional data channel to connect and coordinate real-time data transmission between the units, ensuring accurate and timely data flow between them. Subsequently, data from the energy station units is synchronously collected through this bidirectional data channel. This data includes key operating parameters such as power output, operating status, and temperature. Simultaneously, data from end-user units is also collected. This data primarily includes information on power consumption, load demands, and consumption patterns of various terminal devices. This bidirectional data channel ensures real-time and synchronized transmission of both data types. The collected device operating parameters and end-user data are then feature-aligned using spatiotemporal coding rules. This step aligns the two data types in time and space, ensuring they are compared and analyzed within the same timeframe. Spatiotemporal coding rules typically take into account the operational timing and geographic location of different devices and energy-consuming units, enabling data integration under a common standard. Finally, through this series of operations, a bidirectional real-time data set is generated, which includes the real-time operating status and energy consumption data of the energy station unit and the terminal energy-consuming unit. These data sets provide the basis for subsequent regulation and optimization, and provide real-time feedback for intelligent scheduling.

[0020] In some embodiments, the device operating parameters and the terminal energy consumption data are feature aligned according to spatiotemporal coding rules to construct the bidirectional real-time dataset, and the method includes:

[0021] A data quality assessment matrix is constructed to score the equipment operation parameters and the terminal energy consumption data to generate a bidirectional data scoring value, wherein the bidirectional data scoring value includes an equipment operation scoring value and a terminal energy consumption scoring value; time-shift compensation is performed on the equipment operation parameters based on the equipment operation scoring value to obtain equipment operation update parameters; time-shift compensation is performed on the terminal energy consumption data based on the terminal energy consumption scoring value to obtain terminal energy consumption update data; the equipment operation update parameters are associated and aligned with the terminal energy consumption update data to obtain dynamic associated data of the energy station and the terminal, and the dynamic associated data of the energy station and the terminal are added to the bidirectional real-time data set.

[0022] Specifically, when constructing a bidirectional dataset, the quality of the device operating parameters and end-point energy consumption data is first evaluated. This is achieved by constructing a data quality evaluation matrix. Each row of the data quality evaluation matrix represents a device data item or an energy consumption data item, and each column represents an evaluation metric, such as accuracy, completeness, consistency, and timeliness. Accuracy can be determined by comparing with the baseline data and calculating the standard deviation. Completeness can be determined by calculating the ratio of missing data points to the total number of data points. Consistency can be determined by comparing the values of the same data item from different sources or at different time points and calculating the difference between the data. Timeliness can be determined by calculating the ratio of the time delay between acquisition and transmission to the maximum tolerable delay. Subsequently, the device operating parameters and end-point energy consumption data are evaluated based on these metrics defined in the data quality evaluation matrix, and the evaluation results are stored in the data quality evaluation matrix. For each row of data in the data quality evaluation matrix, such as device operating parameters or end-point energy consumption data, a score for that row of data is calculated by weighted summation. This score is used as the device operation score or end-point energy consumption score. These scores together constitute the bidirectional data score. Next, time-shift compensation is performed based on the device operation score and the end-user energy consumption score. Time-shift compensation adjusts the data's time series to eliminate time misalignment caused by data collection delays or other factors. Specifically, the device operation score is multiplied by the maximum time offset to obtain the current time offset. The maximum time offset is determined based on the device's collection frequency, historical experience, and expert decision-making. The time interval between adjacent data collections is then compared with the collection frequency. If the time interval is greater than the collection frequency, indicating a lag in the collected data, the data is shifted forward by the time offset, adjusting the data timestamp to align with the actual time series, thereby obtaining the updated device operation parameters. Similarly, time-shift compensation is performed on the end-user energy consumption data to correct its time deviation, resulting in updated end-user energy consumption data. Finally, the time-shift-compensated device operation update parameters are aligned with the end-user energy consumption update data. This step aligns the two data sets in time and space to ensure they can be analyzed and processed in the same time dimension. After the association is completed, the obtained energy station-terminal dynamic association data will be added to the two-way real-time data center, providing a more accurate and timely data basis for subsequent collaborative optimization and intelligent scheduling.

[0023] Table 1: Data quality assessment table

[0024]

[0025]

[0026] The data item columns in Table 1 represent the data items corresponding to different devices or energy consumption units, including device 1 data, device 2 data, energy consumption 1 data, and energy consumption 2 data. The quality of each data item is evaluated using four main indicators: accuracy, completeness, consistency, and timeliness.

[0027] Conduct equipment operation analysis on the energy station unit, formulate a dynamic optimization operation strategy, conduct energy consumption characteristic analysis on the terminal energy-consuming unit, and determine time-sharing energy consumption benchmark information.

[0028] Specifically, when analyzing the operation of energy station units, the first step is to extract data from each energy device within the energy station from the bidirectional real-time data set to understand its operating status and energy efficiency. The operating status of each device directly affects energy efficiency. By analyzing this device operating data, device performance can be evaluated and future operating trends can be predicted. This allows for the development of dynamic optimization strategies, dynamically adjusting parameters such as device operating mode and operating duration to improve overall system efficiency. Furthermore, the energy consumption characteristics of end-user units are analyzed. During this process, the campus is divided into zones based on factors such as building function and occupancy time. Energy consumption characteristics are then extracted for each zone, and multiple energy usage patterns and load profiles are determined. Subsequently, dynamic time-based adjustments are made to the end-user units based on these multiple load profiles to determine energy usage baseline values. These energy usage baseline values are then added to the time-based energy usage baseline information to improve energy efficiency and avoid energy waste during peak hours. In summary, by determining dynamic optimization strategies and time-based energy usage baseline information, the system can achieve precise energy regulation across the entire campus, minimize energy waste, and enhance the overall energy management efficiency of the campus.

[0029] In some embodiments, the energy station unit is subjected to equipment operation analysis to formulate a dynamic optimization operation strategy, the method comprising:

[0030] A multi-dimensional equipment operation analysis is performed on the energy station unit to generate an equipment status evaluation matrix; an operation prediction is performed on the energy station unit using the equipment status evaluation matrix, and an operation change trend diagram of the energy station unit is drawn according to the prediction results; equipment operation deviation is calculated according to the operation change trend diagram to obtain an operation deviation degree; operation deviation correction is performed on the energy station unit according to the operation deviation degree, and the dynamic optimization operation strategy is formulated.

[0031] Specifically, when conducting a multi-dimensional equipment operation analysis on an energy station unit, it is first necessary to extract various types of equipment operation data from a bidirectional real-time data set. These data include but are not limited to the power, temperature, pressure, vibration, operating time, etc. of the equipment. Based on these data, a multi-dimensional operation analysis is conducted on each device in the energy station unit, including dimensions such as temperature, energy efficiency, stability, load distribution, and safety. The results of each indicator obtained from the analysis are then organized into a matrix form to obtain an equipment status assessment matrix, where the temperature can be determined by the ratio of the real-time temperature to the safety temperature, the energy efficiency can be determined based on the ratio of the output power to the input power, the stability can be determined by subtracting the equipment downtime from the total equipment operating time, and then calculating the ratio of the difference to the total equipment operating time. The load distribution can be determined by dividing the difference between the maximum and minimum loads by the sum of the maximum and minimum loads, and then subtracting the ratio from 1. Safety can be determined based on the ratio of normal operating time (the time allowed for the equipment to not exceed the parameter safety range) to the total operating time. Subsequently, using the device state evaluation matrix as input, a long short-term memory (LSTM) network constructed based on historical data is used to predict the operation of the energy station units, obtaining the unit's operating state indicators—i.e., the future device state evaluation matrix. The LSTM is trained iteratively through forward propagation, loss calculation, backpropagation, and parameter optimization based on the historical device state evaluation matrix and historical operating state indicators. Loss calculation is based on the mean squared error loss function, and parameter optimization is performed using the Adam optimizer. After the predicted operating state indicators are obtained, they are visualized. Specifically, the predicted operating state indicators are plotted chronologically to create a trend chart of the energy station units' operating changes, showing the equipment's operational changes over a period of time. The predicted operating state indicators are then compared with the expected operating state indicators, and the differences between the two indicators are calculated to determine the operational deviation. Based on the operational deviation, appropriate dynamic optimization strategies can be formulated to ensure that the equipment operates at its optimal state. For example, if a device's temperature is too high, the deviation can be corrected by adjusting the device's load or activating backup cooling equipment. These optimization strategies can adjust the equipment's operating state in real time, reducing energy waste, ensuring operational efficiency, and extending its service life.

[0032] In some embodiments, performing energy consumption characteristic analysis on the terminal energy-consuming unit to determine time-sharing energy consumption benchmark information includes:

[0033] Perform partitioned energy consumption feature extraction on the terminal energy consumption unit to determine multi-zone energy consumption features, and construct an energy consumption behavior feature vector of the terminal energy consumption unit based on the multi-zone energy consumption features; perform cluster analysis based on the energy consumption behavior feature vector to determine multiple energy consumption modes, traverse the multiple energy consumption modes to perform load synchronization recording, and obtain multiple load curves, wherein the multiple load curves correspond to the multiple energy consumption modes; perform dynamic time-sharing adjustment on the terminal energy consumption unit based on the multiple load curves, determine an energy consumption benchmark value, and add the energy consumption benchmark value to the time-sharing energy consumption benchmark information.

[0034] Specifically, the terminal energy consumption units are divided into different areas, usage functions or energy consumption types. For example, the office area, production area, storage area, etc. in the park. The energy consumption characteristics of each area may be different, so they need to be analyzed separately. For each partition, the energy consumption data of each area (such as electricity, heat energy, cooling energy consumption, etc.) are extracted from the two-way real-time data set, and the energy consumption characteristics of each area are determined based on these energy consumption data. These characteristics may include the total energy consumption of each area, energy consumption peak value, energy consumption valley value, load fluctuation, energy consumption peak period, valley period, etc. By adding the energy consumption characteristics of these partitions into a set, multi-zone energy consumption characteristics are obtained. Subsequently, the energy consumption characteristics of each partition in the multi-zone energy consumption characteristics are spliced to obtain the energy consumption behavior feature vector of the terminal energy consumption unit. The energy consumption behavior feature vector represents the energy consumption pattern of the terminal energy consumption unit at different times and under different conditions. Afterwards, clustering algorithms (such as K-means and DBSCAN) are used to analyze the energy usage behavior feature vectors of all end-users. Regions with similar energy usage behaviors are grouped together using Euclidean distance, thereby identifying multiple energy usage patterns. Each energy usage pattern is the mean of the corresponding cluster. After obtaining multiple energy usage patterns, these patterns are traversed. For each energy usage pattern, the load synchronization data for that pattern is calculated, namely, the mean load of the partitions belonging to that pattern at the same time. Based on this load synchronization data, a load curve for that pattern is plotted. The load curve reflects the energy consumption trends of the region over different time periods, corresponding to the energy usage pattern. Then, by analyzing the load curve for each energy usage pattern, the load variation patterns and peak energy consumption periods are identified, and energy consumption demands are determined for different time periods. Energy resources are then dynamically allocated. For example, during off-peak periods, excess energy can be stored or used to power other low-load areas, thereby reducing energy pressure during peak periods. During peak periods, backup energy can be introduced or the energy supply ratio adjusted to avoid overload. Based on the analysis results of the load curve, a reasonable energy consumption benchmark value is set for each time period. The benchmark value refers to the optimal energy consumption target of the terminal energy consumption unit within a specific time period, which can maximize energy efficiency while meeting demand. For example, during off-peak hours (such as at night), a lower benchmark value is set to avoid energy waste. During peak hours (such as daytime office hours), a higher benchmark value is set, and backup energy may be activated to ensure that demand is met. The setting of these benchmark values is based on the fluctuations of the current load curve and historical data. Finally, these energy consumption benchmark values are added to the time-sharing energy consumption benchmark information, and are adjusted and monitored in real time through the system. These benchmark information will guide the control strategies of energy stations and terminal energy consumption units to ensure the reasonable distribution of energy.

[0035] The control and monitoring of the dynamic optimization operation strategy is performed according to the time-sharing energy usage benchmark information to generate control feedback data.

[0036] In one embodiment, when implementing a dynamic optimization strategy, the energy load is first decomposed based on time-of-day energy usage benchmark information to identify and quantify changes in energy demand within different time periods. Subsequently, based on the load decomposition results, the operating status of the energy station units is analyzed, the cooling and heating efficiency of the equipment is evaluated, and energy usage and optimization potential are identified, providing a basis for subsequent control strategy formulation. The dynamic optimization strategy is then simulated based on the evaluated cooling and heating efficiency. During the simulation, the effects of different energy configurations and scheduling schemes are analyzed, generating a multi-level control instruction set. These instruction sets guide how the equipment should adjust its operating mode to ensure energy demand is met under efficient and stable conditions. The hierarchical structure of the instruction set enables comprehensive control from the equipment unit to the entire energy station, ensuring optimal energy flow. After the control instruction set is generated, the energy station units and end-user units are controlled according to these instructions. The control parameters of each device and the response data of the end-user units are recorded. These control records provide detailed feedback information to the system to facilitate subsequent adjustment and optimization of the strategy. Finally, the control parameters of the energy station equipment and the end-user response data are continuously monitored to generate control feedback data. These feedback data provide the system with real-time operation status reports, providing support for subsequent collaborative optimization.

[0037] In some embodiments, the control monitoring of the dynamic optimization operation strategy is performed according to the time-sharing energy usage benchmark information to generate control feedback data, and the method includes:

[0038] Based on the multiple load curves, load decomposition is performed, and operation analysis is performed on the energy station unit to obtain the cooling and heating operation efficiency; according to the cooling and heating operation efficiency simulation, a dynamic optimization strategy is analyzed to generate a multi-level control instruction set; according to the multi-level control instruction set, the energy station unit and the terminal energy consumption unit are controlled and recorded to obtain energy station equipment control parameters and terminal energy consumption response data; based on the energy station equipment control parameters and the terminal energy consumption response data, the energy station unit and the terminal energy consumption unit are continuously monitored for operation control to generate the control feedback data.

[0039] Specifically, multiple load curves are decomposed based on time-of-day energy usage benchmark information to identify energy usage fluctuations and trends in different regions over different time periods, thereby understanding actual load demands during peak and off-peak periods. Subsequently, the input power and output cooling capacity of the cooling equipment in the energy station units for the corresponding time period are extracted from the bidirectional real-time data set. The cooling efficiency for that period is calculated by calculating the ratio of output cooling capacity to input power. Similarly, the input power and output heating capacity of the heating equipment are extracted to calculate the heating efficiency for that period. After obtaining the cooling and heating efficiencies, these two efficiencies are added to a single set to obtain the cooling and heating efficiencies. A dynamic optimization strategy is then simulated in a simulation platform based on the cooling and heating efficiencies. The goal of this simulation is to understand the effectiveness of the control strategy and determine the optimal scheduling method while meeting the actual load demands at different time periods. This generates a multi-level control instruction set, with each level representing different control objectives and strategies. From fine-grained control of equipment units to macro-level scheduling of the entire energy station, this ensures optimal energy flow between different regions. After generating a multi-level control instruction set, the energy station unit and the terminal energy consumption unit will be controlled and recorded according to these instructions to determine the energy station equipment control parameters and terminal energy consumption response data that need to be adjusted, such as the operating frequency of the energy station equipment, valve switching, and the load of the refrigeration / heating device, as well as the power consumption of the terminal energy consumption unit, equipment load changes, etc. Finally, based on these control records, continuous operation control monitoring will be carried out. By continuously monitoring the energy station equipment control parameters and terminal energy consumption response data, and mapping the monitoring results with the multi-level control instruction set, the control feedback data is summarized. These feedback data provide real-time feedback for subsequent optimization, which is used to adjust subsequent operation strategies to ensure maximum energy utilization efficiency.

[0040] In some embodiments, the method of continuously monitoring the operation and control of the energy station unit and the terminal energy consumption unit based on the energy station equipment control parameters and the terminal energy consumption response data to generate the control feedback data includes:

[0041] The energy station equipment control parameters and the terminal energy consumption response data are time-regularly aligned to determine the equipment response timing and the control instruction timing; the energy station unit is controlled and analyzed according to the equipment response timing to obtain a first control effect, and the terminal energy consumption unit is controlled and analyzed according to the control instruction timing to obtain a second control effect; according to the first control effect, a first association mapping parameter between the energy station equipment control parameters and the multi-level control instruction set is constructed, and according to the second control effect, a second association mapping parameter between the terminal energy consumption response data and the multi-level control instruction set is constructed; the first association mapping parameter and the second association mapping parameter are evaluated by dimensionality reduction to generate the control feedback data.

[0042] Specifically, after recording the energy station equipment control parameters and end-user energy response data at multiple time points, the energy station equipment control parameters and the end-user energy response data are aligned according to timestamps. Aligning the energy station equipment control parameters yields a device response sequence, which records the control records of the energy station equipment. Similarly, aligning the end-user energy response data yields a control instruction sequence, which records the response of the end-user energy units after the equipment control. Subsequently, based on the aligned sequence data, control analysis is performed on the energy station units and end-user energy units. For energy station units, control is performed according to the device response sequence, and control effect data, such as output power, temperature, and load, is recorded. This results in a primary control effect, demonstrating the response of the energy station equipment to the control instructions. Simultaneously, for end-user energy units, control analysis is performed based on the control instruction sequence, recording the response effects of the end-user energy units, such as changes in regional energy load and peak and valley fluctuations in electricity consumption. This results in a secondary control effect, demonstrating the end-user energy unit's response to the control instructions. After obtaining these two control effects, the first control effect, the energy station equipment control parameters, and the multi-level control instruction set are mapped to construct a first correlation mapping parameter. This parameter describes the relationship between the actual control effect of the energy station equipment and the instruction set, helping to understand the specific impact of the equipment control instructions on equipment operation. Similarly, the second control effect, the end-user energy response data, and the multi-level control instruction set are mapped to construct a second correlation mapping parameter. This parameter reflects the energy consumption changes and load responses of the end-user units under the control instructions. Finally, dimensionality reduction methods such as autoencoders and t-SNE are used to evaluate the first and second correlation mapping parameters. This dimensionality reduction evaluation aims to reduce data complexity while retaining key influencing factors. This dimensionality reduction allows the extraction of the key parameters that influence the overall system control effect, thereby generating control feedback data. This feedback data includes the control effects of the equipment and end-user units, as well as the relationships between various parameters and control instructions. This control feedback data provides a basis for subsequent optimization decisions and adjustments, helping the system further refine control strategies and improve overall energy management efficiency.

[0043] Based on the control feedback data and combined with the bidirectional real-time data set, the energy station unit and the terminal energy-consuming unit are collaboratively optimized to obtain a linkage collaborative control solution.

[0044] Specifically, through data fusion, the control feedback data and the bidirectional real-time data set are integrated into a multidimensional optimization space. Within this optimization space, the energy station unit and the terminal energy consumption unit are collaboratively analyzed through digital twins to determine multiple sets of linkage control parameters. These parameters take into account the relationship and demand matching between the energy station and the terminal energy consumption unit. For example, when the load of a terminal energy consumption unit is too high, its operating mode can be adjusted according to the output capacity of the energy station unit to ensure the rational distribution of energy. Conversely, if the energy supply capacity of the energy station unit is insufficient, the load demand of the terminal energy consumption unit will be automatically adjusted to avoid excessive energy consumption. Finally, based on these collaborative control parameters, multi-objective optimization is performed to obtain a linkage collaborative control scheme. By adjusting the operating strategies of the energy station unit and the terminal energy consumption unit, the entire system maximizes energy efficiency and reduces energy waste while meeting their respective needs, thus realizing linkage control between the energy station unit and the terminal energy consumption unit.

[0045] In some embodiments, based on the control feedback data and the bidirectional real-time data set, the energy station unit and the terminal energy consumption unit are collaboratively optimized to obtain a linkage collaborative control solution, and the method includes:

[0046] The control feedback data is combined with the bidirectional real-time data set to perform data fusion to construct a multidimensional optimization space; the energy station unit and the terminal energy-consuming unit are digitally twinned and mapped to the multidimensional optimization space to generate a collaborative control simulation scenario; based on the collaborative control simulation scenario, the energy station unit and the terminal energy-consuming unit are collaboratively iterated to determine multiple linkage control parameter combinations; multi-objective optimization is performed based on the multiple linkage control parameter combinations, and control verification is performed based on the optimization results. When the verification passes, the linkage collaborative control scheme is generated.

[0047] Specifically, after obtaining control feedback data, the control feedback data is merged with the bidirectional real-time dataset into a single dataset. Based on this dataset, a multidimensional optimization space is constructed. This multidimensional optimization space includes multiple dimensional parameters such as equipment load, efficiency, and energy consumption. Within this multidimensional optimization space, various data not only provide information on the current operating efficiency of the equipment but also demonstrate the interactions between different equipment and areas, providing a basis for subsequent optimization decisions. Subsequently, design information for the energy station unit and the end-user unit, such as geometry, internal configuration, and connectivity, is obtained. This design information is input into digital twin software (such as PTC ThingWorx, Siemens NX / Simcenter, and Ansys Twin Builder) to construct digital twin models of the energy station unit and the end-user unit. The constructed digital twin model is then mapped into the multidimensional optimization space, and boundary conditions are set for the model, such as maximum load, minimum load, energy efficiency range, and regulation capacity. These boundaries reflect the operating limits and maximum potential of the equipment, ensuring that all control parameters remain within reasonable operating ranges to avoid equipment overload or inefficiency. After the boundary setting is complete, a virtual collaborative control simulation scenario can be generated. This scenario simulates the interaction between the energy station unit and the end-user unit under different operating conditions, allowing for early prediction of performance under different scheduling strategies. Then, based on the collaborative control simulation scenario, the operating mode between the energy station unit and the end-user unit is gradually adjusted by simulating different control strategies and regulation parameters. In each iteration, optimization is performed based on the feedback from the simulation scenario, and the control parameters are adjusted to continuously improve the coordination between the energy station and the end-user unit. Multiple iterative processes will help the system find multiple linkage control parameter combinations. After obtaining multiple linkage control parameter combinations, multi-objective optimization is performed based on these multiple linkage control parameter combinations. This process aims to optimize the overall operating efficiency of the energy station unit and the end-user unit. By adjusting the energy flow and scheduling strategies between different devices and regions, the stability of the energy supply is ensured while reducing energy waste. Multi-objective optimization not only considers a single objective (such as minimizing energy costs), but also comprehensively considers multiple factors such as system stability, equipment lifespan, and energy demand. Finally, the optimized multiple linkage control parameter combinations are applied to a collaborative control simulation scenario for control verification, simulating their effects in a real-world environment. By comparing the simulation results with the expected optimization targets, it is verified whether each linkage control parameter combination meets the requirements. If a linkage control parameter combination passes the verification, the linkage control parameter combination with the smallest error from the expected optimization target is extracted from the passed linkage control parameter combinations as the linkage collaborative control solution and put into actual operation, thereby achieving more intelligent and efficient energy management.

[0048] In summary, the park-oriented energy station remote control method provided by the present invention has the following technical effects:

[0049] A two-way data channel is established for the target park, and real-time data collection is performed through the two-way data channel to obtain a two-way real-time data set, wherein the two-way data channel includes an energy station unit and a terminal energy-consuming unit; equipment operation analysis is performed on the energy station unit, and a dynamic optimization operation strategy is formulated; energy consumption characteristics analysis is performed on the terminal energy-consuming unit, and time-sharing energy consumption benchmark information is determined; control and monitoring of the dynamic optimization operation strategy is performed according to the time-sharing energy consumption benchmark information to generate control feedback data; based on the control feedback data and combined with the two-way real-time data set, the energy station unit and the terminal energy-consuming unit are collaboratively optimized to obtain a linkage collaborative control scheme, thereby achieving the technical effect of realizing dynamic optimization operation of the energy station and the terminal energy-consuming unit through intelligent scheduling, improving energy utilization efficiency, and reducing energy consumption.

[0050] Example 2, as Figure 2 This is a schematic diagram of the structure of the energy station remote control system for the park of the present invention. For example, Figure 1 The flow chart of the remote control method of the energy station for the park in the present invention can be shown as follows: Figure 2 The structure shown is implemented.

[0051] Based on the same concept as the park-oriented energy station remote control method in the above embodiment, the present invention also provides a park-oriented energy station remote control system including:

[0052] Real-time acquisition module 11: establishes a two-way data channel for the target park, performs real-time acquisition through the two-way data channel, and obtains a two-way real-time data set, wherein the two-way data channel includes an energy station unit and a terminal energy-consuming unit; data analysis module 12: performs equipment operation analysis on the energy station unit, formulates a dynamic optimization operation strategy, performs energy consumption characteristic analysis on the terminal energy-consuming unit, and determines time-sharing energy consumption benchmark information; control and monitoring module 13: performs control and monitoring of the dynamic optimization operation strategy according to the time-sharing energy consumption benchmark information, and generates control feedback data; collaborative optimization module 14: based on the control feedback data and combined with the two-way real-time data set, the energy station unit and the terminal energy-consuming unit are collaboratively optimized to obtain a linkage and collaborative control solution.

[0053] In some embodiments, the real-time acquisition module 11 includes:

[0054] A bidirectional data channel is established between the energy station unit and the terminal energy consumption unit of the target park through the smart energy management and control platform; data is synchronously collected through the energy station unit through the bidirectional data channel to obtain equipment operating parameters, and data is synchronously collected through the terminal energy consumption unit through the bidirectional data channel to obtain terminal energy consumption data; the equipment operating parameters and the terminal energy consumption data are feature-aligned according to spatiotemporal coding rules to construct the bidirectional real-time data set.

[0055] In some embodiments, the real-time acquisition module 11 includes:

[0056] A data quality assessment matrix is constructed to score the equipment operation parameters and the terminal energy consumption data to generate a bidirectional data scoring value, wherein the bidirectional data scoring value includes an equipment operation scoring value and a terminal energy consumption scoring value; time-shift compensation is performed on the equipment operation parameters based on the equipment operation scoring value to obtain equipment operation update parameters; time-shift compensation is performed on the terminal energy consumption data based on the terminal energy consumption scoring value to obtain terminal energy consumption update data; the equipment operation update parameters are associated and aligned with the terminal energy consumption update data to obtain dynamic associated data of the energy station and the terminal, and the dynamic associated data of the energy station and the terminal are added to the bidirectional real-time data set.

[0057] In some embodiments, the data analysis module 12 further includes:

[0058] A multi-dimensional equipment operation analysis is performed on the energy station unit to generate an equipment status evaluation matrix; an operation prediction is performed on the energy station unit using the equipment status evaluation matrix, and an operation change trend diagram of the energy station unit is drawn according to the prediction results; equipment operation deviation is calculated according to the operation change trend diagram to obtain an operation deviation degree; operation deviation correction is performed on the energy station unit according to the operation deviation degree, and the dynamic optimization operation strategy is formulated.

[0059] In some embodiments, the data analysis module 12 includes:

[0060] Perform partitioned energy consumption feature extraction on the terminal energy consumption unit to determine multi-zone energy consumption features, and construct an energy consumption behavior feature vector of the terminal energy consumption unit based on the multi-zone energy consumption features; perform cluster analysis based on the energy consumption behavior feature vector to determine multiple energy consumption modes, traverse the multiple energy consumption modes to perform load synchronization recording, and obtain multiple load curves, wherein the multiple load curves correspond to the multiple energy consumption modes; perform dynamic time-sharing adjustment on the terminal energy consumption unit based on the multiple load curves, determine an energy consumption benchmark value, and add the energy consumption benchmark value to the time-sharing energy consumption benchmark information.

[0061] In some embodiments, the control and monitoring module 13 includes:

[0062] Based on the multiple load curves, load decomposition is performed, and operation analysis is performed on the energy station unit to obtain the cooling and heating operation efficiency; according to the cooling and heating operation efficiency simulation, a dynamic optimization strategy is analyzed to generate a multi-level control instruction set; according to the multi-level control instruction set, the energy station unit and the terminal energy consumption unit are controlled and recorded to obtain energy station equipment control parameters and terminal energy consumption response data; based on the energy station equipment control parameters and the terminal energy consumption response data, the energy station unit and the terminal energy consumption unit are continuously monitored for operation control to generate the control feedback data.

[0063] In some embodiments, the control and monitoring module 13 includes:

[0064] The energy station equipment control parameters and the terminal energy consumption response data are time-regularly aligned to determine the equipment response timing and the control instruction timing; the energy station unit is controlled and analyzed according to the equipment response timing to obtain a first control effect, and the terminal energy consumption unit is controlled and analyzed according to the control instruction timing to obtain a second control effect; according to the first control effect, a first association mapping parameter between the energy station equipment control parameters and the multi-level control instruction set is constructed, and according to the second control effect, a second association mapping parameter between the terminal energy consumption response data and the multi-level control instruction set is constructed; the first association mapping parameter and the second association mapping parameter are evaluated by dimensionality reduction to generate the control feedback data.

[0065] In some embodiments, the collaborative optimization module 14 includes:

[0066] The control feedback data is combined with the bidirectional real-time data set to perform data fusion to construct a multidimensional optimization space; the energy station unit and the terminal energy-consuming unit are digitally twinned and mapped to the multidimensional optimization space to generate a collaborative control simulation scenario; based on the collaborative control simulation scenario, the energy station unit and the terminal energy-consuming unit are collaboratively iterated to determine multiple linkage control parameter combinations; multi-objective optimization is performed based on the multiple linkage control parameter combinations, and control verification is performed based on the optimization results. When the verification passes, the linkage collaborative control scheme is generated.

[0067] In embodiment three, the present invention also provides a computer-readable storage medium that can be used to store software programs, computer executable programs and modules, such as program instructions / modules corresponding to the remote control method for a park-oriented energy station in an embodiment of the present invention, thereby realizing the above-mentioned remote control method for a park-oriented energy station.

[0068] It should be understood that the embodiments disclosed in the present invention and the above description can enable those skilled in the art to use the present invention to implement the present invention. At the same time, the present invention is not limited to the embodiments mentioned above. It should be understood that those skilled in the art can still modify the technical solutions described in the above embodiments or replace some of the technical features therein with equivalents; and such modifications or replacements do not deviate from the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention and are all included in the scope of protection of the present invention.

Claims

1. A remote control method for energy stations in a park, characterized in that: The method comprises: Establish a bidirectional data channel for the target park, perform real-time data collection through the bidirectional data channel, and obtain a bidirectional real-time data set, wherein the bidirectional data channel includes energy station units and terminal energy consumption units; Conduct equipment operation analysis on the energy station unit, formulate dynamic optimization operation strategy, conduct energy consumption characteristic analysis on the terminal energy consumption unit, and determine time-sharing energy consumption benchmark information; Executing the control and monitoring of the dynamic optimization operation strategy according to the time-sharing energy usage benchmark information to generate control feedback data; Based on the control feedback data and combined with the bidirectional real-time data set, the energy station unit and the terminal energy-consuming unit are collaboratively optimized to obtain a linkage collaborative control solution.

2. The remote control method for a park-oriented energy station according to claim 1, characterized in that: Establishing a bidirectional data channel for the target park, performing real-time data collection through the bidirectional data channel, and obtaining a bidirectional real-time data set, the method includes: Establishing a bidirectional data channel between the energy station unit and the terminal energy consumption unit of the target park through the smart energy management and control platform; The energy station unit performs data synchronous collection through the bidirectional data channel to obtain equipment operating parameters, and the terminal energy consumption unit performs data synchronous collection through the bidirectional data channel to obtain terminal energy consumption data; The equipment operating parameters and the terminal energy consumption data are feature aligned according to spatiotemporal coding rules to construct the bidirectional real-time data set.

3. The remote control method for a park-oriented energy station according to claim 2, characterized in that: Aligning the device operating parameters with the terminal energy consumption data according to spatiotemporal coding rules to construct the bidirectional real-time data set, the method comprising: Constructing a data quality assessment matrix to score the equipment operation parameters and the terminal energy consumption data, and generating a two-way data scoring value, wherein the two-way data scoring value includes an equipment operation scoring value and a terminal energy consumption scoring value; Performing time shift compensation on the device operation parameters based on the device operation score value to obtain device operation update parameters, and performing time shift compensation on the terminal energy consumption data based on the terminal energy consumption score value to obtain terminal energy consumption update data; The equipment operation update parameters are associated and aligned with the terminal energy consumption update data to obtain dynamic energy station-terminal association data, and the energy station-terminal dynamic association data is added to the bidirectional real-time data set.

4. The remote control method for a park-oriented energy station according to claim 1, characterized in that: Performing equipment operation analysis on the energy station unit and formulating a dynamic optimization operation strategy, the method includes: Perform multi-dimensional equipment operation analysis on the energy station unit to generate an equipment status assessment matrix; Performing an operation prediction on the energy station unit using the equipment status evaluation matrix, and drawing an operation change trend diagram of the energy station unit according to the prediction result; Calculate the equipment operation deviation according to the operation change trend diagram to obtain the operation deviation degree; The energy station unit is operated with corrections based on the operation deviation, and the dynamic optimization operation strategy is formulated.

5. The remote control method for a park-oriented energy station according to claim 1, characterized in that: Analyzing the energy consumption characteristics of the terminal energy-consuming unit to determine time-sharing energy consumption benchmark information includes: Extracting energy consumption characteristics of the terminal energy-consuming unit by partition, determining multi-zone energy consumption characteristics, and constructing an energy consumption behavior feature vector of the terminal energy-consuming unit according to the multi-zone energy consumption characteristics; Performing cluster analysis based on the energy usage behavior feature vector to determine multiple energy usage patterns, traversing the multiple energy usage patterns to perform load synchronization recording to obtain multiple load curves, wherein the multiple load curves correspond to the multiple energy usage patterns; Dynamically adjust the terminal energy consumption unit in a time-sharing manner based on the multiple load curves, determine an energy consumption reference value, and add the energy consumption reference value to the time-sharing energy consumption reference information.

6. The remote control method for a park-oriented energy station according to claim 5, characterized in that: The method includes: performing control monitoring of the dynamic optimization operation strategy according to the time-sharing energy usage benchmark information to generate control feedback data; Decomposing the load based on the multiple load curves, performing operation analysis on the energy station units, and obtaining cooling and heating operation efficiency; Analyze the dynamic optimization strategy according to the cold and hot operation efficiency simulation to generate a multi-level control instruction set; According to the multi-level control instruction set, the energy station unit and the terminal energy consumption unit are controlled and recorded to obtain energy station equipment control parameters and terminal energy consumption response data; Based on the energy station equipment control parameters and the terminal energy consumption response data, the energy station unit and the terminal energy consumption unit are continuously monitored for operation control to generate the control feedback data.

7. The remote control method for a park-oriented energy station according to claim 6, characterized in that: The method includes: continuously monitoring the operation and control of the energy station unit and the terminal energy consumption unit based on the energy station equipment control parameters and the terminal energy consumption response data to generate the control feedback data; Time-align the energy station equipment control parameters and the terminal energy response data to determine the equipment response timing and the control instruction timing; Performing control analysis on the energy station unit according to the device response sequence to obtain a first control effect, and performing control analysis on the terminal energy-consuming unit according to the control instruction sequence to obtain a second control effect; Constructing first association mapping parameters between the energy station equipment control parameters and the multi-level control instruction set according to the first control effect, and constructing second association mapping parameters between the terminal energy consumption response data and the multi-level control instruction set according to the second control effect; Performing dimensionality reduction evaluation on the first association mapping parameter and the second association mapping parameter to generate the control feedback data.

8. The remote control method for a park-oriented energy station according to claim 1, characterized in that: Based on the control feedback data and the bidirectional real-time data set, the energy station unit and the terminal energy consumption unit are collaboratively optimized to obtain a linkage collaborative control solution, the method comprising: The control feedback data is combined with the bidirectional real-time data set to perform data fusion to construct a multi-dimensional optimization space; Mapping the energy station unit and the terminal energy-consuming unit into digital twins in the multi-dimensional optimization space to generate a collaborative control simulation scenario; Based on the collaborative control simulation scenario, the energy station unit and the terminal energy consumption unit are collaboratively iterated to determine multiple linkage control parameter combinations; Multi-objective optimization is performed based on the multiple linkage control parameter combinations, control verification is performed based on the optimization results, and when the verification passes, the linkage collaborative control scheme is generated.

9. The energy station remote control system for the park is characterized by: The method for implementing the remote control of a park-oriented energy station according to any one of claims 1 to 8 comprises: Real-time acquisition module: establishes a two-way data channel for the target park, performs real-time acquisition through the two-way data channel, and obtains a two-way real-time data set. The two-way data channel includes energy station units and terminal energy consumption units; Data analysis module: performs equipment operation analysis on the energy station unit, formulates dynamic optimization operation strategy, analyzes energy consumption characteristics of the terminal energy-consuming unit, and determines time-sharing energy consumption benchmark information; Control and monitoring module: performs control and monitoring of the dynamic optimization operation strategy according to the time-sharing energy usage benchmark information, and generates control feedback data; Collaborative optimization module: Based on the control feedback data and the two-way real-time data set, the energy station unit and the terminal energy consumption unit are collaboratively optimized to obtain a linkage collaborative control solution.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the remote control method for a park-oriented energy station as described in any one of claims 1 to 8 is implemented.

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