Intelligent energy scheduling method and system based on information processing

Abstract

The invention discloses an intelligent energy scheduling method and system based on information processing, and the method comprises the steps: obtaining an energy scheduling network, and determining each energy scheduling region in the energy scheduling network; collecting energy consumption data of a consumption end node, analyzing the energy consumption data, and determining an energy scheduling region to be subjected to energy scheduling as an energy receiving region; according to a transmission end line of the energy receiving area, determining an energy regulation area having a connection relationship with the energy receiving area as an energy distribution area; obtaining energy production data of each production end node in each energy distribution area, determining a plurality of production end nodes as energy distribution nodes according to the energy production data, and determining a final target energy distribution node according to the energy scheduling network; and performing energy production regulation and control on the target energy distribution node according to an analysis result of the energy consumption data, and performing energy scheduling through the transmission end line.

Description

Technical Field

[0001] This invention relates to the field of energy dispatching technology, and in particular to an intelligent energy dispatching method and system based on information processing. Background Technology

[0002] With the development of artificial intelligence and big data technologies, systems that use these technologies for energy dispatch have emerged. In the dispatch of distributed energy resources (such as solar and wind power), big data analysis is used to integrate information from different distributed energy sources, and intelligent algorithms are used to optimize the access and dispatch of these distributed energy sources, thereby improving energy utilization efficiency.

[0003] However, some existing information processing systems suffer from slow processing speeds and untimely responses when handling large-scale energy data. For example, when processing power generation data from a large number of distributed energy sources (such as numerous solar photovoltaic panels and energy storage systems) in real time, if the processing speed cannot keep up with the data acquisition speed, the data cannot be applied to energy dispatch decisions in a timely manner. This leads to the dispatch system being unable to respond quickly to changes in energy supply and demand, especially during peak energy consumption periods. If existing intelligent optimization algorithms are used to collect historical data before decision analysis, it will result in the inability to process large-scale energy dispatch data in a timely manner. Summary of the Invention

[0004] This invention provides an intelligent energy scheduling method based on information processing to solve the technical problems of slow processing speed, untimely response, and reliance on high-computing-power intelligent optimization algorithms in existing technologies, which prevent timely energy scheduling.

[0005] To address the aforementioned technical problems, embodiments of the present invention provide an intelligent energy dispatching method based on information processing, comprising: An energy dispatch network is acquired, and each energy dispatch area is determined within the energy dispatch network; wherein the energy dispatch network includes a number of consumer nodes, a number of production nodes, and transmission lines connecting the consumer nodes and the production nodes, and each energy dispatch area includes at least one consumer node and at least one production node. Collect energy consumption data from consumer nodes, analyze the energy consumption data, and determine the energy dispatch area to be dispatched, which is then designated as the energy receiving area. Based on the transmission line of the energy receiving area, determine the energy regulation area that is connected to the energy receiving area, and designate it as the energy distribution area. Acquire and, based on the energy production data of each production node in each energy distribution area, determine several production nodes as energy distribution nodes, and, based on the energy dispatch network, determine the final target energy distribution node. Based on the analysis results of the energy consumption data, energy production is regulated at the target energy distribution node, and energy scheduling is carried out through the transmission line.

[0006] As a preferred embodiment, the energy dispatch network, and the determination of each energy dispatch area within the energy dispatch network, specifically includes: An energy dispatch network is obtained; wherein the energy dispatch network is constructed based on the geographic location information of each consumer node and each production node, and by connecting the consumer nodes and production nodes that have energy transmission connection relationships through transmission lines; Based on the geographic location information of each consumer node and each production node, the region to which each consumer node and each production node belong is determined, and each region is designated as a controllable energy region.

[0007] As a preferred embodiment, the step of collecting energy consumption data from consumer nodes and analyzing the energy consumption data to determine the energy dispatchable area as the energy receiving area specifically includes: Collect energy consumption data from each consumer node; The energy consumption data is input into the energy prediction model, and the first future energy consumption data of each energy regulation area in the future preset time period is output. Obtain the energy production data of the production node in the energy regulation zone where the current consumer node is located, and calculate the first future energy production data of the production node at maximum operating power in a future preset time period based on the energy production data of the production node. If the first future energy consumption data in an energy regulation region is greater than the first future energy production data, then that energy regulation region is designated as an energy receiving region.

[0008] As a preferred embodiment, determining the energy distribution area as an energy delivery area based on the transmission line of the energy receiving area specifically includes: Based on the transmission lines connected to each consumer node in the energy receiving area, determine the production node that has a transmission line connection with each consumer node in the energy receiving area. Remove the production-end nodes located in the energy receiving area, and use the energy regulation areas corresponding to the removed production-end nodes as energy distribution areas.

[0009] As a preferred embodiment, the step of removing production-end nodes located in the energy receiving area and designating the energy regulation areas corresponding to the removed production-end nodes as energy distribution areas specifically includes: Production nodes located in the energy receiving area are removed, and the energy regulation areas corresponding to the removed production nodes are determined as preliminary energy distribution areas; wherein, the number of preliminary energy distribution areas is at least one. Acquire energy consumption data of consumer nodes and energy production data of production nodes in each initial energy distribution area, and calculate the second future energy consumption data and the second future energy production data of each initial energy distribution area within a preset time period based on the energy consumption data of consumer nodes and the energy production data of production nodes in each initial energy distribution area. If the difference between the second future energy consumption data and the second future energy production data of the initial energy distribution area is greater than a preset threshold, then the initial energy distribution area is designated as the energy distribution area.

[0010] As a preferred embodiment, the step of acquiring and determining several production-end nodes as energy distribution nodes based on energy production data of each production-end node in each energy distribution area, and determining the final target energy distribution node based on the energy dispatch network, specifically includes: Based on the energy production data of each production node in each energy distribution area, the load data of each production node is calculated, and the production node whose load data is less than a preset scheduling threshold is designated as an energy distribution node. Calculate the third future energy production data of the energy distribution node at maximum operating power within a preset time period; Calculate the target energy receiving data for the energy receiving area based on the first future energy consumption data and the first future energy production data in the energy receiving area; Based on the third future energy production data and the target energy receiving data, the production-end nodes in the energy distribution area are combined to obtain several node combinations; wherein, each node combination includes at least two production-end nodes, and the sum of the energy data delivered to the energy receiving area by each production-end node in the node combination is greater than the target energy receiving data. Based on the energy dispatch network, determine the sum of the transmission distances through which each production node in a combination of nodes accesses the energy receiving area via the transmission line; The energy distribution node in the node combination with the smallest sum of transmission distances is determined as the target energy distribution node.

[0011] As a preferred embodiment, the step of regulating energy production at the target energy distribution node based on the analysis results of the energy consumption data, and scheduling energy through the transmission line, specifically includes: Based on the target energy receiving data, as well as the energy production data of each production node and the energy consumption data of each consumption node in the energy receiving area, determine the consumable energy data that each consumption node needs to schedule. Based on the consumable energy data, an energy reception assessment is performed on the energy receiving area. Based on the assessment results and the current energy production data of the target energy distribution node, an energy dispatch decision is generated. Based on the energy dispatch decision, energy production of the target energy distribution node is regulated, and finally, the regulated energy production is dispatched through the transmission line.

[0012] Accordingly, the present invention also provides an intelligent energy dispatching system based on information processing, comprising: A regional module is used to acquire an energy dispatch network and determine each energy dispatch region within the energy dispatch network; wherein, the energy dispatch network includes a number of consumer nodes, a number of production nodes, and transmission lines connecting the consumer nodes and the production nodes, and each energy dispatch region includes at least one consumer node and at least one production node. The analysis module is used to collect energy consumption data from consumer nodes, analyze the energy consumption data, and determine the energy dispatch area to be dispatched as the energy receiving area. The connection module is used to determine, based on the transmission line of the energy receiving area, the energy regulation area that is connected to the energy receiving area, and to designate it as the energy distribution area. The delivery module is used to acquire and determine several production-end nodes as energy delivery nodes based on the energy production data of each production-end node in each energy delivery area, and determine the final target energy delivery node based on the energy dispatch network. The scheduling module is used to regulate energy production at the target energy distribution node based on the analysis results of the energy consumption data, and to schedule energy through the transmission line.

[0013] Accordingly, the present invention also provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the intelligent energy scheduling method based on information processing as described above.

[0014] Accordingly, the present invention also provides a computer-readable storage medium comprising a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the intelligent energy scheduling method based on information processing as described above.

[0015] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: The technical solution of this invention determines each energy dispatching region by acquiring energy dispatching network data. This allows for the identification of energy dispatching regions to be dispatched by collecting energy consumption data from consumer nodes. These regions serve as energy receiving regions. The transmission lines of these receiving regions then identify energy dispatching regions connected to them, avoiding the increased computational burden of unconnected or indirectly connected regions. Furthermore, energy dispatching through directly connected regions ensures dispatching efficiency and improves response speed. Energy production data from each production node in each energy distribution region is used to determine the final target energy distribution node. Based on the analysis of energy consumption data, energy production at the target energy distribution node is regulated, and energy dispatching is performed through transmission lines. This improves response efficiency, reduces the impact of large-scale computation on timely response, and avoids the need for complex intelligent algorithm optimization. This approach is effectively applicable to scenarios such as grid faults and sudden changes in peak electricity demand. Attached Figure Description

[0016] Figure 1 : A flowchart illustrating the steps of an intelligent energy scheduling method based on information processing provided in an embodiment of the present invention; Figure 2 : This is a structural diagram of the intelligent energy dispatching system based on information processing provided in an embodiment of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Example 1

[0018] Please refer to Figure 1 The present invention provides an intelligent energy scheduling method based on information processing, comprising the following steps S101-S105: S101: Obtain the energy dispatch network and determine each energy dispatch area in the energy dispatch network; wherein, the energy dispatch network includes a number of consumer nodes, a number of production nodes, and transmission lines connecting the consumer nodes and the production nodes, and each energy dispatch area includes at least one consumer node and at least one production node.

[0019] As a preferred embodiment, the energy dispatch network, and the determination of each energy dispatch area within the energy dispatch network, specifically includes: An energy dispatch network is obtained; wherein the energy dispatch network is constructed based on the geographic location information of each consumer node and each production node, and by connecting the consumer nodes and production nodes that have energy transmission connection relationships through transmission lines; Based on the geographic location information of each consumer node and each production node, the region to which each consumer node and each production node belong is determined, and each region is designated as a controllable energy region.

[0020] In this embodiment, the production-end node is a distributed energy production scheduling center; the transmission line is used to connect the production end and the consumption end, and coordinate the distance and real-time processing relationship between the production-end node and the consumption-end node on both sides of the transmission line; the consumption-end node determines the energy scheduling target, thereby determining the specific energy scheduling center and carrying out energy scheduling.

[0021] In this embodiment, an energy dispatch network is constructed based on geolocation information and geolocation technology. Geolocation technology can accurately determine the location of consumer-end nodes (such as factories, communities, commercial buildings, etc. that consume various types of energy) and production-end nodes (such as various power plants, solar power plants, wind farms, etc.). Through this location information, it is possible to identify which consumer-end and production-end nodes have energy transmission connections and connect them with transmission lines to form an energy dispatch network.

[0022] Furthermore, based on geographical, administrative, or energy characteristics in the geographic information, consumer-end nodes and production-end nodes are divided into different energy dispatch zones. One energy dispatch zone can cover multiple nodes within a certain range, which facilitates targeted energy dispatch management for different regions.

[0023] In this embodiment, technologies such as the Global Positioning System (GPS), BeiDou Navigation Satellite System, and Geographic Information System (GIS) are employed to accurately acquire the latitude and longitude coordinates and other geographic positioning information of each consumer and production node, ensuring positioning accuracy for subsequent network connectivity analysis. Furthermore, based on factors such as the feasibility, economics, and capacity limitations of energy transmission, the system analyzes which consumer and production nodes can establish energy transmission connections. For example, factors such as voltage levels, transmission capacity, and distance loss of power transmission lines are considered, while for energy sources like natural gas, pipeline routes and pressures are taken into account. This identifies node pairs with potential for actual energy transmission and connects them to form transmission lines. During network construction, the network topology is also comprehensively considered, employing tree, ring, or hybrid structures to ensure the reliability and efficiency of energy transmission. Simultaneously, the network is optimized, such as reducing transmission line crossings and minimizing line losses.

[0024] In this embodiment, the construction of an energy dispatch network is not a necessary technical feature for solving the technical problem of this case. There are related technical solutions for constructing energy dispatch networks in the prior art. In this embodiment, after directly obtaining the energy dispatch network, reasonable regional division criteria are set, including but not limited to administrative boundaries (such as provinces, cities, counties, etc.), geographical features (such as mountain ranges, rivers, etc.), and energy supply and demand balance areas (divided according to the matching of energy production capacity and consumption). Based on these criteria, all nodes are classified into different regions. Then, geographic information system (GIS) related tools and algorithms are used to delineate the boundaries of each energy dispatch area on the map according to the division criteria, so as to cover all relevant nodes and avoid overlap and omission between regions.

[0025] S102: Collect energy consumption data from consumer nodes, analyze the energy consumption data, and determine the energy dispatch area to be dispatched as the energy receiving area.

[0026] In a preferred embodiment, the step of collecting energy consumption data from consumer nodes and analyzing the energy consumption data to determine the energy dispatch area to be designated as the energy receiving area specifically includes: Collect energy consumption data from each consumer node; The energy consumption data is input into the energy prediction model, and the first future energy consumption data of each energy regulation area in the future preset time period is output. Obtain the energy production data of the production node in the energy regulation zone where the current consumer node is located, and calculate the first future energy production data of the production node at maximum operating power in a future preset time period based on the energy production data of the production node. If the first future energy consumption data in an energy regulation region is greater than the first future energy production data, then that energy regulation region is designated as an energy receiving region.

[0027] In this embodiment, the energy consumption data collected from consumer-end nodes includes: user power consumption, power consumption habits, and electrical equipment information. Data from production-end nodes includes power generation capacity, fuel inventory, and equipment status.

[0028] In this embodiment, the energy prediction model is built by training on historical data. Taking time series analysis as an example, it utilizes the temporal characteristics (such as seasonality, periodicity, and trend) of historical energy consumption data and fits the data's changing patterns using a mathematical model. For instance, the ARIMA (Autoregressive Integral Moving Average) model is used. By performing autoregressive and moving average calculations on historical energy consumption data, the non-stationarity of the data is considered, thereby predicting energy consumption within a preset future time period. Exemplarily, by employing machine learning models such as time series neural networks, the model's structure and parameters are determined, such as the number of neurons in the hidden layer of the neural network and the type of activation function. Historical energy consumption data from various energy-controlled regions are used as training data for the model. By adjusting the model parameters, the model can fit the training data well, ultimately completing the construction of the energy prediction model.

[0029] In this embodiment, various energy metering devices (such as electricity meters, gas meters, and heat meters) are installed at the consumer-end nodes. Sensor technology is used to sense energy consumption in real time, and the data is collected in the form of digital signals. These devices accurately measure different energy types according to the corresponding energy metering standards.

[0030] In this embodiment, the calculation of energy production data for the production-end nodes is mainly based on the physical characteristics and operating principles of their equipment. Based on the energy production data of the production-end nodes (such as current power generation, remaining available power generation capacity, etc.), combined with the maximum operating power limit of the equipment, the total amount of energy that may be generated at maximum operating power within a preset future time period is calculated using the law of conservation of energy and relevant equipment performance formulas. Then, the calculated energy production data of the production-end nodes is numerically compared with the predicted energy consumption data to determine whether the area needs to be designated as an energy receiving area.

[0031] S103: Based on the transmission line of the energy receiving area, determine the energy regulation area that is connected to the energy receiving area, and designate it as the energy distribution area.

[0032] As a preferred embodiment, the step of determining the energy distribution area as an energy delivery area based on the transmission line of the energy receiving area specifically includes: Based on the transmission lines connected to each consumer node in the energy receiving area, determine the production node that has a transmission line connection with each consumer node in the energy receiving area. Remove the production-end nodes located in the energy receiving area, and use the energy regulation areas corresponding to the removed production-end nodes as energy distribution areas.

[0033] In this embodiment, by identifying the transmission lines connected to each consumer node in the energy receiving area, the production nodes that can be directly connected to each consumer node in the energy receiving area can be extracted, and the production nodes located in the energy receiving area can be eliminated, thereby obtaining the production nodes outside the energy receiving area, and the energy regulation area corresponding to the production node is taken as the energy distribution area.

[0034] In a preferred embodiment, the step of removing production-end nodes located in the energy receiving area and designating the energy regulation areas corresponding to the removed production-end nodes as energy distribution areas specifically includes: Production nodes located in the energy receiving area are removed, and the energy regulation areas corresponding to the removed production nodes are determined as preliminary energy distribution areas; wherein, the number of preliminary energy distribution areas is at least one. Acquire energy consumption data of consumer nodes and energy production data of production nodes in each initial energy distribution area, and calculate the second future energy consumption data and the second future energy production data of each initial energy distribution area within a preset time period based on the energy consumption data of consumer nodes and the energy production data of production nodes in each initial energy distribution area. If the difference between the second future energy consumption data and the second future energy production data of the initial energy distribution area is greater than a preset threshold, then the initial energy distribution area is designated as the energy distribution area.

[0035] In this embodiment, by removing energy production nodes located in the energy receiving area, a preliminary energy distribution area is determined based on the energy regulation area to which the nodes originally belong. The energy consumption data of the consumption nodes and the energy production data of the production nodes in the preliminary energy distribution area are integrated, and an energy prediction model is used to predict energy consumption and production in the future time period. The energy prediction model is usually based on historical data and related algorithms, taking into account factors such as time series and trends to predict future energy changes. The specific construction process can refer to the above-mentioned energy prediction model construction method.

[0036] In this embodiment, the degree of future energy supply and demand imbalance in the region is assessed by comparing the difference between the second future energy consumption data and the second future energy production data of the initial energy distribution area. When the difference exceeds a preset threshold, the region is considered to have an energy demand gap, thus identifying it as an energy distribution area.

[0037] In this embodiment, accurately identifying and removing energy production ends located within the energy receiving area can be achieved through geographic location information and regional division rules. Specifically, Geographic Information System (GIS) technology is used to precisely match the geographic coordinates of energy production ends with the boundaries of the energy receiving area, determining which production ends are located within that area and removing them. Then, the energy dispatching areas corresponding to the removed energy production ends are determined. This requires referring to the original energy dispatching area division information of the energy production end nodes to ensure that the affiliation of each production end is clear, thereby accurately dividing it into preliminary energy distribution areas. A preliminary energy distribution area may include multiple energy production end nodes and their associated consumer end nodes.

[0038] In this embodiment, for the consumer-end nodes in the initial energy distribution area, energy consumption data is collected in real time through installed energy metering devices, such as electricity meters. For the production-end nodes, their energy production data is collected, including current production power, equipment operating status, etc. The data is automatically collected and transmitted by connecting with the control system of the production-end equipment through a data interface.

[0039] In this embodiment, a reasonable preset threshold is set based on the energy management strategy and the energy supply and demand characteristics of the region. This threshold can also be adjusted according to actual needs and expert experience. For each initial energy distribution area, the difference between its second future energy consumption data and second future energy production data is calculated using a simple subtraction operation. For example, the formula is: Energy supply and demand difference = Second future energy consumption data - Second future energy production data. Further, the energy supply and demand difference is compared with the preset threshold. When the difference is greater than the preset threshold, the initial energy distribution area is determined as an energy distribution area.

[0040] S104: Obtain and determine several production-end nodes as energy distribution nodes based on the energy production data of each production-end node in each energy distribution area, and determine the final target energy distribution node based on the energy dispatch network.

[0041] As a preferred embodiment, the step of acquiring and determining several production-end nodes as energy distribution nodes based on the energy production data of each production-end node in each energy distribution area, and determining the final target energy distribution node based on the energy dispatch network, specifically includes: Based on the energy production data of each production node in each energy distribution area, the load data of each production node is calculated, and the production node whose load data is less than a preset scheduling threshold is designated as an energy distribution node. Calculate the third future energy production data of the energy distribution node at maximum operating power within a preset time period; Calculate the target energy receiving data for the energy receiving area based on the first future energy consumption data and the first future energy production data in the energy receiving area; Based on the third future energy production data and the target energy receiving data, the production-end nodes in the energy distribution area are combined to obtain several node combinations; wherein, each node combination includes at least two production-end nodes, and the sum of the energy data delivered to the energy receiving area by each production-end node in the node combination is greater than the target energy receiving data. Based on the energy dispatch network, determine the sum of the transmission distances through which each production node in a combination of nodes accesses the energy receiving area via the transmission line; The energy distribution node in the node combination with the smallest sum of transmission distances is determined as the target energy distribution node.

[0042] In this embodiment, load data is calculated based on the energy production data of each production node, reflecting its load status during energy production, i.e., its energy production intensity or pressure. By setting a preset scheduling threshold, production nodes with load data below the threshold are selected, possessing additional energy distribution capabilities and thus serving as additional energy distribution nodes. Furthermore, for the identified energy distribution nodes, third future energy production data is calculated for a preset future time period at maximum operating power. This can be based on the physical characteristics and operating conditions of the energy production equipment, considering the equipment's maximum operating power, to predict its energy production potential within a specific time period, providing a basis for subsequent energy distribution planning. Additionally, by combining the first future energy consumption data and the first future energy production data of the energy receiving area, target energy receiving data for that area is calculated, reflecting the amount of energy the energy receiving area needs to receive from external sources in the future time period to meet the difference between its energy consumption demand and local production. Finally, production nodes are combined based on the third future energy production data of the energy distribution nodes and the target energy receiving data of the energy receiving area. By optimizing the algorithm, we find node combinations that can meet the needs of the energy receiving area, with each combination containing at least two production-end nodes. This ensures that the total energy delivery volume of the combination is greater than the target energy receiving data. Furthermore, based on the energy dispatch network, we determine the sum of the transmission distances of each production-end node in each node combination to access the energy receiving area through the transmission line. Finally, we select the node combination with the smallest sum of transmission distances to reduce losses and costs during energy transmission and improve energy delivery efficiency.

[0043] In this embodiment, energy production data of production nodes in each energy distribution area are collected in real time, including but not limited to power generation, power output, and equipment operating status. These data can be obtained through an energy management system (EMS) or a monitoring system of production equipment and stored in a database. In order to ensure the accuracy and integrity of the data, the collected data is periodically verified and cleaned to remove abnormal data.

[0044] In this embodiment, load data for each production node is calculated based on energy production data. For example, for a power generation node, load data can be calculated based on the ratio of power generation to the rated power of the equipment, using the formula: Load data = (Power generation / Rated power of equipment) × 100%. Simultaneously, the impact of other factors, such as equipment operating time, on the load data is considered. A reasonable preset scheduling threshold is set, which can be determined comprehensively based on historical operating data, equipment performance, and energy dispatch strategies. Typically, the threshold is set around 60%-80%, but the specific value needs to be adjusted according to actual conditions. Finally, the calculated load data is compared with the preset scheduling threshold, and production nodes with load data less than the preset scheduling threshold are selected as energy distribution nodes.

[0045] In this embodiment, the calculation of future energy production data for energy distribution nodes is primarily based on the maximum operating power of each energy production node. According to the maximum operating power of the energy distribution node, its third future energy production data for a preset future time period is calculated at that maximum operating power. For power generation nodes, the formula is: Third Future Energy Production Data = Maximum Operating Power × Preset Time Period Length × Equipment Availability Coefficient. The equipment availability coefficient considers factors such as potential equipment downtime and maintenance time. Simultaneously, the calculation results are corrected by incorporating historical energy production data and trends to improve prediction accuracy. For example, the predicted data is adjusted by analyzing the equipment's operating conditions over similar past time periods.

[0046] In this embodiment, first future energy consumption data and first future energy production data of the energy receiving area are acquired. These data can be obtained through an energy prediction model, which predicts future energy conditions based on historical energy data, meteorological data, time series, and other factors. Then, the target energy receiving data of the energy receiving area is calculated, i.e., target energy receiving data = first future energy consumption data - first future energy production data. The target energy receiving data reflects the amount of energy the energy receiving area needs to receive from external sources within a preset future time period to compensate for its own energy supply and demand gap.

[0047] In this embodiment, each combination contains at least two production-end nodes, and the sum of the energy data used by each production-end node in the combination for delivery to the energy receiving area is greater than the target energy receiving data, which ensures the reliability and stability of energy delivery and avoids insufficient energy supply due to the failure of a single node or other problems.

[0048] In this embodiment, during the combination process, the third future energy production data of each production node is used as input, combined with the target energy receiving data, to perform a large number of combination calculations and evaluations, screen out node combinations that meet the conditions, and further optimize the node combinations that have been initially screened. For example, the order or composition of the nodes in the combination is adjusted according to factors such as the connection status of energy transmission lines and the geographical location of the nodes, so as to improve the efficiency and reliability of energy distribution.

[0049] In this embodiment, based on the energy dispatch network, the transmission line connection information between each production node and the energy receiving area in each node combination is obtained. This information includes the coordinates of the start and end points of the line, the line length, and the geographical path of the line, which can be obtained through a Geographic Information System (GIS) and the energy dispatch network topology map. Then, the transmission distance for each production node to access the energy receiving area via the transmission line is calculated. Various methods can be used to calculate the transmission distance, such as the Euclidean distance (straight-line distance) formula: Transmission distance = [(x2 - x1)² + (y2 - y1)²] -2 Where (x1,y1) and (x2,y2) are the geographical coordinates of the production node and the energy receiving area, respectively; the actual transmission distance can also be calculated by considering the actual transmission line route and length through network analysis methods.

[0050] In this embodiment, among all node combinations that meet the conditions, the sum of the transmission distances of each combination is compared, and the energy distribution node in the node combination with the smallest sum of transmission distances is selected as the target energy distribution node. This helps to reduce energy loss during transmission and improve the economy and efficiency of energy distribution. Furthermore, the selected target energy distribution node needs to undergo final evaluation and verification to ensure that it can meet the energy demand of the energy receiving area and comply with the safety and stability requirements of energy dispatch. For example, this involves checking the equipment operating status and energy quality of the production-end nodes.

[0051] S105: Based on the analysis results of the energy consumption data, energy production regulation is carried out on the target energy distribution node, and energy scheduling is performed through the transmission line.

[0052] As a preferred embodiment, the step of regulating energy production at the target energy distribution node based on the analysis results of the energy consumption data, and scheduling energy through the transmission line, specifically includes: Based on the target energy receiving data, as well as the energy production data of each production node and the energy consumption data of each consumption node in the energy receiving area, determine the consumable energy data that each consumption node needs to schedule. Based on the consumable energy data, an energy reception assessment is performed on the energy receiving area. Based on the assessment results and the current energy production data of the target energy distribution node, an energy dispatch decision is generated. Based on the energy dispatch decision, energy production of the target energy distribution node is regulated, and finally, the regulated energy production is dispatched through the transmission line.

[0053] In this embodiment, considering both the target energy reception data and the energy production and consumption within the energy reception area, the available energy data that each consumer node can schedule is determined. Based on this available energy data, the energy reception capacity of the energy reception area is assessed, taking into account factors such as energy transmission efficiency and changes in energy demand at the consumer end, to determine whether the area can effectively receive and utilize the scheduled energy. For example, an energy reception assessment model can be established, which may include multiple assessment indicators, such as energy supply and demand balance, energy quality, and energy reception stability. For instance, energy supply and demand balance can be calculated by comparing available energy data with actual energy demand. Furthermore, weights are assigned to each assessment indicator to reflect its importance to the overall energy reception capacity. These weights can be determined using methods such as expert scoring or the Analytic Hierarchy Process (AHP).

[0054] In this embodiment, energy dispatching decisions are generated by combining energy reception assessment results and energy production data of target energy distribution nodes. For example, based on optimization algorithms, with the goal of minimizing energy loss, cost, or maximizing energy utilization efficiency, the decisions determine which production-end nodes will distribute how much energy to the energy receiving area. Based on these energy dispatching decisions, energy production at the target energy distribution nodes is regulated. This includes real-time control of production-end equipment, adjusting its operating parameters to change energy production levels and ensure energy supply according to the dispatching decisions. Finally, energy is dispatched through transmission lines based on the energy dispatching decisions and production regulation results. This requires management and optimization of the energy transmission network to ensure safe and stable energy transmission from the production end to the consumption end.

[0055] In this embodiment, energy production data from each production node and energy consumption data from each consumption node in the energy receiving area are collected, including historical and real-time data from devices such as energy management systems (EMS) and smart meters. Simultaneously, target energy receiving data is acquired, and based on the collected data, the consumable energy data for each consumption node is calculated. For example, this can be achieved by establishing a mathematical model that considers the relationship between energy production data, energy consumption data, and target energy receiving data. For instance, consumable energy data = target energy receiving data - energy production data of production nodes + energy allocated to consumption nodes. The specific calculation formula will be adjusted according to actual conditions, and may require the introduction of weighting factors to balance the needs of different nodes. Furthermore, losses during energy transmission are considered, and the consumable energy data is corrected. For example, the energy transmission loss rate is calculated based on factors such as the length and resistance of the transmission line and incorporated into the calculation of consumable energy data.

[0056] In this embodiment, consumable energy data is input into the evaluation model to calculate the comprehensive evaluation result of the energy receiving area. For example, the comprehensive evaluation result = Σ (evaluation index value × weight). The evaluation result is usually presented in the form of a score or grade, representing the receiving capacity of the energy receiving area. Based on the evaluation result, it is determined whether the energy receiving area can effectively receive energy. For example, an evaluation threshold is set; when the comprehensive evaluation result is higher than this threshold, the energy receiving area is considered to have good receiving capacity; otherwise, the energy dispatch plan needs to be adjusted.

[0057] As another preferred approach, the optimization objective of energy dispatch can be determined, such as minimizing energy transmission losses, minimizing energy dispatch costs, and maximizing energy utilization efficiency. For example, with the objective of minimizing energy transmission losses, the optimization objective function can be expressed as: min Σ(energy transmission amount × transmission loss rate). Based on the optimization objective, a corresponding mathematical model is established, including the objective function and constraints. Constraints may include the maximum production capacity of production nodes, the maximum consumption capacity of consumption nodes, and the maximum transmission capacity of transmission lines. Then, optimization algorithms are used to solve the energy dispatch model, such as linear programming (LP), mixed integer programming (MIP), and genetic algorithms (GA). By running the optimization algorithm, an energy dispatch decision is obtained. For example, the dispatch decision may show that production node A distributes 1000 kWh of energy, and production node B distributes 800 kWh of energy.

[0058] In this embodiment, control commands for the production-end equipment of the target energy distribution node are generated based on energy dispatch decisions. For example, for a power generation production node, control commands may include adjusting the output power of the generator set, starting or stopping standby power generation equipment, etc. Furthermore, control commands need to consider the operating characteristics of the equipment, such as start-up time, stop time, and ramp rate (i.e., the rate of change of the equipment's output power). For example, for a gas turbine generator set, its ramp rate is limited, and the output power cannot be changed instantaneously; therefore, reasonable control commands need to be generated based on the ramp rate.

[0059] In this embodiment, the transmission lines are scheduled according to energy dispatch decisions, including adjusting the switching status and power flow distribution of the lines, to ensure that energy can be transmitted to the energy receiving area according to the dispatch decisions. Simultaneously, the operating status of the transmission lines is monitored in real time, including parameters such as voltage, current, and power, to ensure safe and stable operation. For example, by installing online monitoring equipment, information such as line temperature and sag can be obtained in real time to prevent line overload or faults.

[0060] Implementing the above embodiments has the following effects: The technical solution of this invention determines each energy dispatching region by acquiring energy dispatching network data. This allows for the identification of energy dispatching regions to be dispatched by collecting energy consumption data from consumer nodes. These regions serve as energy receiving regions. The transmission lines of these receiving regions then identify energy dispatching regions connected to them, avoiding the increased computational burden of unconnected or indirectly connected regions. Furthermore, energy dispatching through directly connected regions ensures dispatching efficiency and improves response speed. Energy production data from each production node in each energy distribution region is used to determine the final target energy distribution node. Based on the analysis of energy consumption data, energy production at the target energy distribution node is regulated, and energy dispatching is performed through transmission lines. This improves response efficiency, reduces the impact of large-scale computation on timely response, and avoids the need for complex intelligent algorithm optimization. This approach is effectively applicable to scenarios such as grid faults and sudden changes in peak electricity demand. Example 2

[0061] Please see Figure 2 The present invention provides an intelligent energy dispatching system based on information processing, comprising: The region module 201 is used to acquire the energy dispatch network and determine each energy dispatch region in the energy dispatch network; wherein, the energy dispatch network includes a number of consumer nodes, a number of production nodes and a transmission line connecting the consumer nodes and the production nodes, and each energy dispatch region includes at least one consumer node and at least one production node. Analysis module 202 is used to collect energy consumption data from consumer nodes, analyze the energy consumption data, and determine the energy dispatch area to be dispatched as the energy receiving area. The connection module 203 is used to determine, based on the transmission line of the energy receiving area, the energy regulation area that is connected to the energy receiving area, and to designate it as the energy distribution area. The delivery module 204 is used to acquire and determine several production-end nodes as energy delivery nodes based on the energy production data of each production-end node in each energy delivery area, and determine the final target energy delivery node based on the energy dispatch network. The scheduling module 205 is used to regulate energy production at the target energy distribution node based on the analysis results of the energy consumption data, and to schedule energy through the transmission line.

[0062] As a preferred embodiment, the energy dispatch network, and the determination of each energy dispatch area within the energy dispatch network, specifically includes: An energy dispatch network is obtained; wherein the energy dispatch network is constructed based on the geographic location information of each consumer node and each production node, and by connecting the consumer nodes and production nodes that have energy transmission connection relationships through transmission lines; Based on the geographic location information of each consumer node and each production node, the region to which each consumer node and each production node belong is determined, and each region is designated as a controllable energy region.

[0063] As a preferred embodiment, the step of collecting energy consumption data from consumer nodes and analyzing the energy consumption data to determine the energy dispatchable area as the energy receiving area specifically includes: Collect energy consumption data from each consumer node; The energy consumption data is input into the energy prediction model, and the first future energy consumption data of each energy regulation area in the future preset time period is output. Obtain the energy production data of the production node in the energy regulation zone where the current consumer node is located, and calculate the first future energy production data of the production node at maximum operating power in a future preset time period based on the energy production data of the production node. If the first future energy consumption data in an energy regulation region is greater than the first future energy production data, then that energy regulation region is designated as an energy receiving region.

[0064] As a preferred embodiment, determining the energy distribution area as an energy delivery area based on the transmission line of the energy receiving area specifically includes: Based on the transmission lines connected to each consumer node in the energy receiving area, determine the production node that has a transmission line connection with each consumer node in the energy receiving area. Remove the production-end nodes located in the energy receiving area, and use the energy regulation areas corresponding to the removed production-end nodes as energy distribution areas.

[0065] As a preferred embodiment, the step of removing production-end nodes located in the energy receiving area and designating the energy regulation areas corresponding to the removed production-end nodes as energy distribution areas specifically includes: Production nodes located in the energy receiving area are removed, and the energy regulation areas corresponding to the removed production nodes are determined as preliminary energy distribution areas; wherein, the number of preliminary energy distribution areas is at least one. Acquire energy consumption data of consumer nodes and energy production data of production nodes in each initial energy distribution area, and calculate the second future energy consumption data and the second future energy production data of each initial energy distribution area within a preset time period based on the energy consumption data of consumer nodes and the energy production data of production nodes in each initial energy distribution area. If the difference between the second future energy consumption data and the second future energy production data of the initial energy distribution area is greater than a preset threshold, then the initial energy distribution area is designated as the energy distribution area.

[0066] As a preferred embodiment, the step of acquiring and determining several production-end nodes as energy distribution nodes based on energy production data of each production-end node in each energy distribution area, and determining the final target energy distribution node based on the energy dispatch network, specifically includes: Based on the energy production data of each production node in each energy distribution area, the load data of each production node is calculated, and the production node whose load data is less than a preset scheduling threshold is designated as an energy distribution node. Calculate the third future energy production data of the energy distribution node at maximum operating power within a preset time period; Calculate the target energy receiving data for the energy receiving area based on the first future energy consumption data and the first future energy production data in the energy receiving area; Based on the third future energy production data and the target energy receiving data, the production-end nodes in the energy distribution area are combined to obtain several node combinations; wherein, each node combination includes at least two production-end nodes, and the sum of the energy data delivered to the energy receiving area by each production-end node in the node combination is greater than the target energy receiving data. Based on the energy dispatch network, determine the sum of the transmission distances through which each production node in a combination of nodes accesses the energy receiving area via the transmission line; The energy distribution node in the node combination with the smallest sum of transmission distances is determined as the target energy distribution node.

[0067] As a preferred embodiment, the step of regulating energy production at the target energy distribution node based on the analysis results of the energy consumption data, and scheduling energy through the transmission line, specifically includes: Based on the target energy receiving data, as well as the energy production data of each production node and the energy consumption data of each consumption node in the energy receiving area, determine the consumable energy data that each consumption node needs to schedule. Based on the consumable energy data, an energy reception assessment is performed on the energy receiving area. Based on the assessment results and the current energy production data of the target energy distribution node, an energy dispatch decision is generated. Based on the energy dispatch decision, energy production of the target energy distribution node is regulated, and finally, the regulated energy production is dispatched through the transmission line.

[0068] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the device described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0069] Implementing the above embodiments has the following effects: The technical solution of this invention determines each energy dispatching region by acquiring energy dispatching network data. This allows for the identification of energy dispatching regions to be dispatched by collecting energy consumption data from consumer nodes. These regions serve as energy receiving regions. The transmission lines of these receiving regions then identify energy dispatching regions connected to them, avoiding the increased computational burden of unconnected or indirectly connected regions. Furthermore, energy dispatching through directly connected regions ensures dispatching efficiency and improves response speed. Energy production data from each production node in each energy distribution region is used to determine the final target energy distribution node. Based on the analysis of energy consumption data, energy production at the target energy distribution node is regulated, and energy dispatching is performed through transmission lines. This improves response efficiency, reduces the impact of large-scale computation on timely response, and avoids the need for complex intelligent algorithm optimization. This approach is effectively applicable to scenarios such as grid faults and sudden changes in peak electricity demand. Example 3

[0070] Accordingly, the present invention also provides a terminal device, comprising: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the intelligent energy scheduling method based on information processing as described in any of the above embodiments.

[0071] The terminal device in this embodiment includes a processor, a memory, and a computer program and computer instructions stored in the memory and executable on the processor. When the processor executes the computer program, it implements the various steps described in Embodiment 1 above, for example... Figure 1 The steps S101 to S105 are shown. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in the above-described device embodiment, such as the scheduling module 205.

[0072] For example, the computer program can be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the terminal device. For example, the scheduling module 205 is used to regulate energy production at the target energy distribution node based on the analysis results of the energy consumption data, and to perform energy scheduling through the transmission line.

[0073] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that the schematic diagram is merely an example of a terminal device and does not constitute a limitation on the terminal device. It may include more or fewer components than illustrated, or combine certain components, or different components. For example, the terminal device may also include input / output devices, network access devices, buses, etc.

[0074] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0075] The memory can be used to store the computer programs and / or modules. The processor implements various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory and by calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, applications required for at least one function, etc.; the data storage area may store data created based on the use of the mobile terminal, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital card (SD card), flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0076] Wherein, if the modules / units integrated in the terminal device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by a processor, it can implement the steps of the various method embodiments described above. Wherein, the computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content contained in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals. Example 4

[0077] Accordingly, the present invention also provides a computer-readable storage medium comprising a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the intelligent energy scheduling method based on information processing as described in any of the above embodiments.

[0078] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A smart energy dispatching method based on information processing, characterized in that, include: An energy dispatch network is acquired, and each energy dispatch area is determined within the energy dispatch network; wherein the energy dispatch network includes a number of consumer nodes, a number of production nodes, and transmission lines connecting the consumer nodes and the production nodes, and each energy dispatch area includes at least one consumer node and at least one production node. Collect energy consumption data from consumer nodes, analyze the energy consumption data, and determine the energy dispatch area to be dispatched, which is then designated as the energy receiving area. Based on the transmission line of the energy receiving area, determine the energy regulation area that is connected to the energy receiving area, and designate it as the energy distribution area. Acquire and, based on the energy production data of each production node in each energy distribution area, determine several production nodes as energy distribution nodes, and, based on the energy dispatch network, determine the final target energy distribution node. Based on the analysis results of the energy consumption data, energy production is regulated at the target energy distribution node, and energy scheduling is carried out through the transmission line.

2. The intelligent energy dispatching method based on information processing as described in claim 1, characterized in that, The acquisition of the energy dispatch network and the determination of each energy dispatch area within the energy dispatch network specifically include: An energy dispatch network is obtained; wherein the energy dispatch network is constructed based on the geographic location information of each consumer node and each production node, and by connecting the consumer nodes and production nodes that have energy transmission connection relationships through transmission lines; Based on the geographic location information of each consumer node and each production node, the region to which each consumer node and each production node belong is determined, and each region is designated as a controllable energy region.

3. The intelligent energy dispatching method based on information processing as described in claim 2, characterized in that, The process of collecting energy consumption data from consumer nodes and analyzing this data to determine energy dispatchable areas as energy receiving areas specifically includes: Collect energy consumption data from each consumer node; The energy consumption data is input into the energy prediction model, and the first future energy consumption data of each energy regulation area in the future preset time period is output. Obtain the energy production data of the production node in the energy regulation zone where the current consumer node is located, and calculate the first future energy production data of the production node at maximum operating power in a future preset time period based on the energy production data of the production node. If the first future energy consumption data in an energy regulation region is greater than the first future energy production data, then that energy regulation region is designated as an energy receiving region.

4. The intelligent energy dispatching method based on information processing as described in any one of claims 3, characterized in that, The step of determining the energy distribution area as an energy delivery area based on the transmission line of the energy receiving area specifically includes: Based on the transmission lines connected to each consumer node in the energy receiving area, determine the production node that has a transmission line connection with each consumer node in the energy receiving area. Remove the production-end nodes located in the energy receiving area, and use the energy regulation areas corresponding to the removed production-end nodes as energy distribution areas.

5. The intelligent energy dispatching method based on information processing as described in claim 4, characterized in that, The process of removing production-end nodes located in the energy receiving area, and designating the energy regulation areas corresponding to the removed production-end nodes as energy distribution areas, specifically includes: Production nodes located in the energy receiving area are removed, and the energy regulation areas corresponding to the removed production nodes are determined as preliminary energy distribution areas; wherein, the number of preliminary energy distribution areas is at least one. Acquire energy consumption data of consumer nodes and energy production data of production nodes in each initial energy distribution area, and calculate the second future energy consumption data and the second future energy production data of each initial energy distribution area within a preset time period based on the energy consumption data of consumer nodes and the energy production data of production nodes in each initial energy distribution area. If the difference between the second future energy consumption data and the second future energy production data of the initial energy distribution area is greater than a preset threshold, then the initial energy distribution area is designated as the energy distribution area.

6. The intelligent energy dispatching method based on information processing as described in claim 5, characterized in that, The process of acquiring and determining several production-end nodes as energy distribution nodes based on energy production data from each production-end node in each energy distribution area, and determining the final target energy distribution node based on the energy dispatch network, specifically includes: Based on the energy production data of each production node in each energy distribution area, the load data of each production node is calculated, and the production node whose load data is less than a preset scheduling threshold is designated as an energy distribution node. Calculate the third future energy production data of the energy distribution node at maximum operating power within a preset time period; Calculate the target energy receiving data for the energy receiving area based on the first future energy consumption data and the first future energy production data in the energy receiving area; Based on the third future energy production data and the target energy receiving data, the production-end nodes in the energy distribution area are combined to obtain several node combinations; wherein, each node combination includes at least two production-end nodes, and the sum of the energy data delivered to the energy receiving area by each production-end node in the node combination is greater than the target energy receiving data. Based on the energy dispatch network, determine the sum of the transmission distances through which each production node in a combination of nodes accesses the energy receiving area via the transmission line; The energy distribution node in the node combination with the smallest sum of transmission distances is determined as the target energy distribution node.

7. The intelligent energy dispatching method based on information processing as described in claim 6, characterized in that, The step of regulating energy production at the target energy distribution node based on the analysis results of the energy consumption data, and scheduling energy through the transmission line, specifically includes: Based on the target energy receiving data, as well as the energy production data of each production node and the energy consumption data of each consumption node in the energy receiving area, determine the consumable energy data that each consumption node needs to schedule. Based on the consumable energy data, an energy reception assessment is performed on the energy receiving area. Based on the assessment results and the current energy production data of the target energy distribution node, an energy dispatch decision is generated. Based on the energy dispatch decision, energy production of the target energy distribution node is regulated, and finally, the regulated energy production is dispatched through the transmission line.

8. An intelligent energy dispatching system based on information processing, characterized in that, include: A regional module is used to acquire an energy dispatch network and determine each energy dispatch region within the energy dispatch network; wherein, the energy dispatch network includes a number of consumer nodes, a number of production nodes, and transmission lines connecting the consumer nodes and the production nodes, and each energy dispatch region includes at least one consumer node and at least one production node. The analysis module is used to collect energy consumption data from consumer nodes, analyze the energy consumption data, and determine the energy dispatch area to be dispatched as the energy receiving area. The connection module is used to determine the energy regulation area that is connected to the energy receiving area, based on the transmission line of the energy receiving area, as the energy distribution area; The delivery module is used to acquire and determine several production-end nodes as energy delivery nodes based on the energy production data of each production-end node in each energy delivery area, and determine the final target energy delivery node based on the energy dispatch network. The scheduling module is used to regulate energy production at the target energy distribution node based on the analysis results of the energy consumption data, and to schedule energy through the transmission line.

9. A terminal device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the information processing-based intelligent energy scheduling method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the intelligent energy scheduling method based on information processing as described in any one of claims 1 to 7.

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