Energy medium scheduling method and device, electronic equipment and storage medium

Abstract

The application provides an energy medium scheduling method and device, an electronic device and a storage medium, and belongs to the technical field of energy scheduling. The method comprises the following steps: collecting medium parameters corresponding to multiple types of energy media respectively, and device operation state data corresponding to each type of energy medium in real time; preprocessing each type of medium parameter and the corresponding device operation state data to obtain standardized time series data corresponding to the energy medium; and performing scheduling control operations multiple times until the actual pipe network state feedback data of all energy media enters a target interval. The energy medium scheduling method and device, the electronic device and the storage medium provided by the application can realize the collaborative scheduling of multiple energy media and improve the accuracy of scheduling.

Description

Technical Field

[0001] This application belongs to the field of energy dispatching technology, and more specifically, relates to energy medium dispatching methods and devices, electronic devices, and storage media. Background Technology

[0002] Steel enterprises utilize multiple energy media, including coal gas, oxygen, nitrogen, argon, water, and electricity. These media have extensive pipeline networks, diverse user groups, and fluctuating production and consumption. Furthermore, strong coupling relationships exist between different media (e.g., coal gas power generation affects electricity supply, and oxygen generators' electricity consumption affects the power load). Currently, most enterprises still employ a management model where each media is dispatched independently. Dispatchers issue control commands separately to the coal gas, oxygen, nitrogen, argon, water, and electricity systems based on real-time monitoring data and their personal experience. However, in actual production, the actual production and consumption of each media often deviate from the predicted values ​​upon which the dispatch plan is based. Moreover, a single dispatch adjustment can trigger a chain reaction in other media. The lack of an effective closed-loop adjustment mechanism when the actual pipeline state differs from expectations makes it difficult for the dispatch plan to consistently align with actual operating conditions, resulting in insufficient overall system dispatch accuracy. Therefore, how to achieve coordinated dispatch of multiple energy media and improve dispatch accuracy is a pressing technical problem that needs to be solved. Summary of the Invention

[0003] The purpose of this application is to provide an energy medium scheduling method, apparatus, electronic device, and storage medium capable of achieving coordinated scheduling of multiple energy media and improving scheduling accuracy. To achieve the above objective, the technical solutions provided by this application are as follows: Firstly, it provides energy medium dispatching methods, including: Real-time acquisition of medium parameters corresponding to multiple types of energy media, as well as equipment operating status data corresponding to each type of energy media; Preprocessing is performed on the parameters of each type of medium and the corresponding equipment operating status data to obtain standardized time-series data for that energy medium; Multiple scheduling and control operations are performed until the actual pipeline status feedback data of all energy media enters the target range; The steps of the scheduling and control operation include: For each energy medium, predictions are made based on standardized time-series data to obtain the predicted production and consumption for that energy medium. Based on the predicted production and consumption of multiple energy media, a multi-objective optimization solution is performed under cross-media coupling constraints to obtain a scheduling scheme, which includes scheduling instructions for each energy medium. Dispatch instructions corresponding to each energy medium are sent to each execution unit, and feedback data on the actual pipeline status after each execution unit executes the dispatch instructions are continuously collected. The actual pipeline status feedback data is compared with the predicted production and consumption corresponding to the scheduling scheme in real time, and the prediction deviation of each energy medium is calculated. If the prediction deviation of any energy medium is greater than or equal to the preset deviation threshold, the predicted production and consumption of each energy medium will be corrected based on the currently collected actual pipeline status feedback data, and the scheduling and control operation will continue.

[0004] Secondly, an energy medium dispatching device is provided, including: The data acquisition module is used to collect in real time the medium parameters corresponding to various types of energy media, as well as the equipment operation status data corresponding to each type of energy media. The preprocessing module is used to preprocess the parameters of each type of medium and the corresponding equipment operating status data to obtain the standardized time-series data corresponding to the energy medium. The scheduling and control module is used to execute scheduling and control operations multiple times until the actual pipeline status feedback data of all energy media enters the target range. Specifically, the scheduling and control module, when performing scheduling and control operations, is used for: For each energy medium, predictions are made based on standardized time-series data to obtain the predicted production and consumption for that energy medium. Based on the predicted production and consumption of multiple energy media, a multi-objective optimization solution is performed under cross-media coupling constraints to obtain a scheduling scheme, which includes scheduling instructions for each energy medium. Dispatch instructions corresponding to each energy medium are sent to each execution unit, and feedback data on the actual pipeline status after each execution unit executes the dispatch instructions are continuously collected. The actual pipeline status feedback data is compared with the predicted production and consumption corresponding to the scheduling scheme in real time, and the prediction deviation of each energy medium is calculated. If the prediction deviation of any energy medium is greater than or equal to the preset deviation threshold, the predicted production and consumption of each energy medium will be corrected based on the currently collected actual pipeline status feedback data, and the scheduling and control operation will continue.

[0005] Thirdly, embodiments of this application also provide an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the energy medium dispatching method provided in any possible implementation of the first aspect.

[0006] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the energy medium scheduling method provided by any possible implementation of the first aspect.

[0007] The beneficial effects of the technical solution provided in this application are as follows: Compared with related technologies, the energy medium scheduling method, apparatus, electronic device, and storage medium provided in this application embodiment predict each type of energy medium separately and perform multi-objective optimization based on the predicted production and consumption of multiple energy media under cross-media coupling constraints. This allows the scheduling scheme to simultaneously consider the mutual influence relationships between various media, overcoming the shortcomings of traditional independent scheduling of each medium leading to global imbalance. Secondly, this application embodiment compares the actual pipeline status feedback data after the scheduling command is issued and executed with the predicted production and consumption in real time. When the prediction deviation of any energy medium reaches a preset threshold, the prediction production and consumption is corrected and the scheduling and control operation continues to be executed cyclically. This ensures that the determination of the predicted production and consumption in each cycle incorporates the information of the previous actual execution result. The predicted production and consumption are updated rollingly with changes in operating conditions, and the generation of the scheduling scheme is always based on the predicted production and consumption that is closest to the current actual operating conditions. This effectively solves the problem of the inability to correct the predicted value in time and the failure of the scheduling scheme when it deviates from the actual value in traditional open-loop scheduling. With repeated cycles of control operations, the actual network state of each energy medium gradually approaches and eventually stabilizes within the preset safety target range, realizing a shift from "passive response to deviation" to "actively approaching the target," thus comprehensively improving the accuracy and effectiveness of multi-energy medium coordinated scheduling. Therefore, the embodiments of this application can achieve coordinated scheduling of multiple energy media and improve scheduling accuracy. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments of this application will be briefly introduced below.

[0009] Figure 1 A flowchart illustrating the energy medium scheduling method provided in this application embodiment; Figure 2 A structural block diagram of the energy medium dispatching device provided in the embodiments of this application; Figure 3 A schematic block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0010] The embodiments of this application are described below with reference to the accompanying drawings. It should be understood that the embodiments described below with reference to the accompanying drawings are exemplary descriptions for explaining the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions of the embodiments of this application.

[0011] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the terms “comprising” and “including” as used in embodiments of this application mean that the corresponding feature can be implemented as the presented feature, information, data, step, operation, element, and / or component, but do not exclude implementation as other features, information, data, step, operation, element, component, and / or combinations thereof supported by the art. It should be understood that when we say that an element is “connected” or “coupled” to another element, the one element can be directly connected or coupled to the other element, or it can mean that the one element and the other element establish a connection relationship through an intermediate element. Furthermore, “connected” or “coupled” as used herein can include wireless connection or wireless coupling. The term “and / or” as used herein indicates at least one of the items defined by the term; for example, “A and / or B” can be implemented as “A,” or as “B,” or as “A and B.” When describing multiple (two or more) items, if the relationship between the multiple items is not explicitly defined, the multiple items can refer to one, several or all of the multiple items. For example, the description of "parameter A includes A1, A2, A3" can be implemented as parameter A includes A1 or A2 or A3, or it can be implemented as parameter A includes at least two of the three items A1, A2 and A3.

[0012] It is understood that in the embodiments of this application, data such as user information are involved. When the embodiments of this application are applied to specific products or technologies, user permission or consent is required, and the collection, use and processing of related data must comply with relevant laws, regulations and standards.

[0013] To make the objectives, technical solutions, and advantages of this application clearer, the following description will be provided in conjunction with the accompanying drawings and specific embodiments.

[0014] This application provides an energy medium scheduling method, which can be executed by an electronic device, such as... Figure 1 As shown, the method may include: S101: Real-time acquisition of medium parameters corresponding to multiple types of energy media, as well as equipment operating status data corresponding to each type of energy media.

[0015] In this embodiment, the various energy media are coal gas, oxygen, nitrogen, argon, water, and electricity involved in the steel production process. The media parameters are physical quantities reflecting the operational status of the pipeline network for each type of energy medium. For example, coal gas parameters include blast furnace gas flow rate, blast furnace gas pressure, blast furnace gas temperature, and gas holder level; oxygen, nitrogen, and argon parameters include oxygen flow rate, oxygen pressure, nitrogen flow rate, nitrogen pressure, argon flow rate, and argon pressure; water parameters include water supply flow rate, water supply pressure, water tank level, and circulating water temperature; and electricity parameters include power load, generator output, and grid frequency. The equipment operation status data corresponding to each type of energy medium is the operational status information of key equipment affecting the production and consumption of that type of medium, such as blast furnace operation status, converter operation status, oxygen generator operation status, generator set operation status, and water pump operation status. The above data is collected at a sampling frequency of no less than 1 Hz by interfacing with PLC, DCS, data platform, and MES system.

[0016] S102: Preprocess the parameters of each type of medium and the corresponding equipment operating status data to obtain the standardized time series data corresponding to the energy medium.

[0017] In this embodiment, the preprocessing of each type of medium parameter and the corresponding equipment operating status data to obtain standardized time-series data corresponding to the energy medium includes: For each type of media parameter, missing values ​​are imputed to obtain the imputed data corresponding to that media parameter; for each type of equipment operating status data, missing values ​​are imputed to obtain the imputed data corresponding to that equipment operating status data. Outlier removal is performed on the interpolated data corresponding to each medium parameter and the interpolated data corresponding to each device operating status data to obtain the removed data corresponding to each medium parameter and the removed data corresponding to each device operating status data. The data after removing the parameters of each medium and the data after removing the operating status data of each device are aligned according to a unified timestamp reference to obtain standardized time-series data for each energy medium.

[0018] In this embodiment, for each type of medium parameter, the timestamp in its corresponding standardized time-series data is used as the horizontal axis, and the value of the medium parameter is used as the vertical axis. When data is missing at a certain moment, the line connecting the two most recent valid data points before and after the missing moment is used as the interpolation line, and the value corresponding to the line at the missing moment is calculated as the interpolated value. For example, when blast furnace gas pressure data is missing at a certain moment, the valid pressure values ​​one second before and one second after that moment are taken, and the pressure estimate at that moment is calculated using a linear interpolation formula. For the case of missing data at multiple consecutive moments, linear interpolation is performed using the nearest valid data points before and after the missing segment as endpoints. The same method is used to interpolate missing values ​​for each type of equipment operating status data. For discrete data in the equipment operating status data, such as equipment start-up and shutdown status, a forward filling method is used for interpolation, that is, the nearest valid status value before the missing moment is used for filling.

[0019] Outlier removal employs a physical threshold-based discrimination method. Reasonable value ranges are pre-defined for each medium parameter and equipment operating status parameter, determined based on equipment design parameters and process operating procedures. For each data point, it is determined whether the value falls within the corresponding reasonable value range. If it exceeds the range, the data point is identified as an outlier and deleted. For example, the reasonable range for blast furnace gas pressure is 8 to 15 kPa. When the blast furnace gas pressure data collected at a certain moment exceeds this range, it is identified as an outlier and removed from the dataset. For discrete quantities in the equipment operating status data, if an undefined illegal status code appears, it is identified as an outlier and removed.

[0020] Timestamp alignment is based on a unified reference clock source, which is the system clock of the PLC system. First, a common time axis is defined, with a fixed time interval, e.g., one second, covering the time range of all data. Then, for each type of media parameter and equipment operating status parameter, the data is removed and mapped onto the common time axis according to its timestamp. For each moment on the common time axis, if data for the parameter exists at that moment, it is used directly; otherwise, a nearest neighbor matching method is used, taking the values ​​of the nearest valid data points before and after that moment, and calculating a weighted average using the reciprocal of the time distance as the weight to obtain the data value for that moment. After the above processing, all media parameters and equipment operating status data have one-to-one corresponding data points on the same time axis, forming standardized time-series data corresponding to various energy media.

[0021] This embodiment fills in data gaps caused by abnormal acquisition by imputation of missing values, preserving complete sample information for subsequent prediction function fitting; outlier removal eliminates interference from erroneous data deviating from the normal range, ensuring that the fitting results reflect the true production and consumption patterns; and timestamp alignment eliminates time deviations between multi-source data, ensuring an accurate time correspondence between media parameters and equipment operating status data at each moment, providing a reliable time-series sample dataset for subsequent influencing factor analysis, thereby improving prediction accuracy.

[0022] S103: Perform scheduling and control operations multiple times until the actual pipeline status feedback data of all energy media enters the target range; The scheduling and control operation includes the following steps: For each energy medium, predictions are made based on standardized time-series data to obtain the predicted production and consumption for that energy medium. Based on the predicted production and consumption of multiple energy media, a multi-objective optimization solution is performed under cross-media coupling constraints to obtain a scheduling scheme, which includes scheduling instructions for each energy medium. The scheduling instructions corresponding to each energy medium are sent to each execution unit, and the actual pipeline status feedback data after each execution unit executes the scheduling instructions is continuously collected. The actual pipeline status feedback data is compared in real time with the predicted production and consumption corresponding to the scheduling scheme to calculate the prediction deviation of each energy medium. If the prediction deviation of any energy medium is greater than or equal to the preset deviation threshold, the predicted production and consumption of each energy medium will be corrected based on the currently collected actual pipeline status feedback data, and the scheduling and control operation will continue to be executed.

[0023] In this embodiment, the step of predicting the standardized time-series data corresponding to each energy medium to obtain the predicted production and consumption for that energy medium includes: Data from past time periods are extracted from the standardized time series data corresponding to the energy medium to obtain the historical time series data corresponding to the energy medium. Based on the energy medium flow rate in the historical time series data, the production and consumption of the energy medium in each unit time period are calculated to obtain the production and consumption sequence corresponding to the energy medium. The production and consumption sequence is associated and combined with the equipment operation status data at the corresponding time in the historical time series data to form a sample dataset corresponding to the energy medium. Influencing factor analysis was performed on the sample dataset to determine the influencing factors of the energy medium's production and consumption. Based on the production and consumption sequence in the sample dataset and the influencing factor data, a production and consumption prediction function corresponding to the energy medium is obtained by fitting. Substitute the data on the influencing factors of the energy medium collected at the current moment into the production and consumption prediction function to calculate the predicted production and consumption of the energy medium. By iterating through all energy media, the predicted production and consumption of each energy media can be obtained.

[0024] In this embodiment, considering that the scheduling scheme obtained from a single scheduling and control operation is generated based on the current predicted output and consumption, and that factors such as equipment response characteristics and pipeline transmission delays during actual execution can lead to deviations between the actual pipeline state and the predicted output and consumption, a single control operation often cannot simultaneously bring the pipeline states of all energy media into their respective target ranges. Therefore, after the initial control operation, the predicted output and consumption need to be corrected based on the actual execution results, and the scheduling needs to be re-optimized to make the next control operation closer to actual needs. Considering the different response speeds of different energy media—gas pipeline pressure responds quickly to generator load regulation, while water tank level responds slowly to pump regulation—by executing multiple scheduling and control operations, fast-responding energy media can enter the target range after fewer cycles, while slow-responding energy media gradually enter the target range after more cycles. The convergence times of all energy media are not completely synchronized, and the cycle stops after each reaches its target range. Considering the coupling relationship between the various energy media, a change in the state of one energy media after a single control operation may cause linked fluctuations in the states of other energy media. Multiple cycles are needed to gradually attenuate the coupled fluctuations until all energy media are stable within their target ranges. If the predicted deviation of all energy media is less than the preset deviation threshold, then the actual pipeline status feedback data of all energy media will enter the target range.

[0025] At the beginning of a scheduling and control operation, a set number of days is traced back from the current time, for example, seven days. Gas medium parameter data and corresponding equipment operating status data for all moments within these seven days are extracted from standardized time-series data. The extracted data retains its original timestamp, forming historical time-series data for that energy medium. Each unit time is a pre-set fixed time length, such as one hour. For each unit time in the historical time-series data, all instantaneous flow values ​​within that unit time are extracted, and the total flow within that unit time is calculated using an integral accumulation method as the production and consumption rate for that unit time. For example, for a certain hour, all instantaneous blast furnace gas flow values ​​collected within that hour are accumulated and summed over time intervals to obtain the blast furnace gas generation rate for that hour. The production and consumption rates for each unit time are calculated sequentially in chronological order, and the calculation results are arranged chronologically to form a production and consumption rate sequence.

[0026] For each production consumption data point in the production consumption sequence, determine the time interval covered by the unit time corresponding to that production consumption. Obtain the average value or status value of the operating status data of each device within that time interval. Associate the production consumption data with the device operating status data of the corresponding time interval to form a sample record. For example, for the production consumption data of a certain hour, obtain the average operating status of the blast furnace, the furnace changing status of the hot blast stove, and other device operating status data for that hour. Combine the production consumption data with these device operating status data to form a sample record. All sample records constitute the sample dataset.

[0027] In this embodiment, the step of performing influencing factor analysis on the sample dataset to determine the influencing factor data of the energy medium's production and consumption includes: Obtain a preset set of candidate influencing factors, wherein the set of candidate influencing factors contains multiple candidate factors; For each candidate factor, the correlation coefficient between the candidate factor and the production and consumption sequence in the sample dataset is calculated to obtain the correlation coefficient corresponding to each candidate factor; The absolute value of the correlation coefficient corresponding to each candidate factor is compared with a preset correlation coefficient threshold. Candidate factors whose absolute correlation coefficient is greater than the preset correlation coefficient threshold are identified as influencing factors on the production and consumption of the energy medium.

[0028] In this embodiment, a preset set of candidate influencing factors is obtained, which contains multiple candidate factors. Candidate factors are operating parameters that can be obtained from historical time-series data and theoretically may affect production and consumption. For example, for blast furnace gas, candidate factors include hot blast stove changeover status data, blast furnace oxygen enrichment rate data, blast furnace blast volume data, blast furnace blast temperature data, and blast furnace feed rate data. The correlation coefficient is calculated using the Pearson correlation coefficient method, specifically by calculating the covariance of each candidate factor value with its corresponding value in the production and consumption sequence, divided by the product of their standard deviations. After calculation, the correlation coefficient corresponding to each candidate factor is obtained. The absolute value of the correlation coefficient corresponding to each candidate factor is compared with a preset correlation coefficient threshold, which is pre-set, for example, to 0.3. Candidate factors with absolute correlation coefficient values ​​greater than the preset correlation coefficient threshold are identified as influencing factors affecting the production and consumption of this energy medium. For example, the absolute value of the correlation coefficient between the hot blast stove's changing state and its production and consumption is 0.7, and the absolute value of the correlation coefficient between the blast furnace's oxygen enrichment rate and its production and consumption is 0.65, both of which are greater than 0.3. These two factors are therefore identified as influencing factors. The absolute value of the correlation coefficient between the blast furnace's blast temperature and its production and consumption is 0.15, which is less than 0.3, and therefore it is not considered as an influencing factor.

[0029] In this embodiment, the step of fitting the production and consumption prediction function corresponding to the energy medium based on the production and consumption sequence in the sample dataset and the influencing factor data includes: Determine the type of functional relationship between the production and consumption of this energy medium and its influencing factors; Based on the aforementioned functional relationship type, using the production and consumption sequence in the sample dataset and the influencing factor data, the undetermined parameters in the functional relationship type are solved to obtain the production and consumption prediction function corresponding to the energy medium.

[0030] In this embodiment, the functional relationship type between the energy medium's production and consumption and its influencing factor data is first determined. For example, based on the historical data distribution characteristics of the gas medium, its production and consumption are determined to have a polynomial functional relationship with the hot blast stove's replacement state and the blast furnace's oxygen enrichment rate, with the functional relationship type being a quadratic polynomial. Then, based on this functional relationship type, the undetermined parameters in the functional relationship type are solved using the production and consumption sequence and influencing factor data in the sample dataset, obtaining the production and consumption prediction function corresponding to the energy medium. The solution process uses the least squares method, aiming to minimize the sum of squared errors between the predicted and actual values, to determine the specific values ​​of each undetermined parameter in the functional relationship type. The influencing factor data of the energy medium collected at the current moment is substituted into the production and consumption prediction function to calculate the predicted production and consumption corresponding to the energy medium. This process is repeated for all energy media, i.e., for oxygen, nitrogen, and argon media, water media, and electricity media, respectively, from extracting historical time-series data to substituting it into the predicted production and consumption calculation, to obtain the predicted production and consumption corresponding to each energy medium.

[0031] The influencing factor data collected at the current moment are the latest values ​​of the factors corresponding to the influencing factor data, collected in real time from the site. For example, the current hot blast stove switching status and the current blast furnace oxygen enrichment rate. These values ​​are substituted into the production and consumption prediction function as independent variables, and the calculated dependent variable value is the predicted production and consumption corresponding to this energy medium.

[0032] Following the steps outlined above, from extracting historical time-series data to calculating predicted production and consumption, the oxygen, nitrogen, and argon media, water media, and power media are processed sequentially. For oxygen, nitrogen, and argon media, historical data is extracted from the standardized time-series data corresponding to these media. The production and consumption sequences for each of the three gases are calculated, their respective influencing factors are determined, and prediction functions are fitted accordingly. These are then substituted into the current influencing factor data to obtain the predicted production and consumption for each gas medium. For water media, historical data is extracted from the standardized time-series data corresponding to these media. The water supply production and consumption sequence is calculated, the influencing factors for water system production and consumption are determined, and prediction functions are fitted accordingly. These are then substituted into the current influencing factor data to obtain the predicted production and consumption for water media. For power media, historical data is extracted from the standardized time-series data corresponding to these media. The power load sequence is calculated, the influencing factors for power load are determined, and prediction functions are fitted accordingly. These are then substituted into the current influencing factor data to obtain the predicted production and consumption for power media. After this process is completed, the predicted production and consumption for each energy medium are obtained: predicted production and consumption for coal gas, oxygen, nitrogen, and argon media, predicted production and consumption for water media, and predicted production and consumption for power media.

[0033] In this embodiment, the process of obtaining a scheduling scheme by performing multi-objective optimization under cross-medium coupling constraints based on predicted production and consumption corresponding to multiple energy media includes: Obtain the pre-defined cross-medium coupling constraints; Obtain a pre-defined multi-objective optimization function; Using the predicted production and consumption corresponding to each energy medium as input parameters, the cross-medium coupling constraint as constraint boundary, and the multi-objective optimization function as optimization objective, a multi-objective optimization problem is constructed. A multi-objective optimization algorithm is used to solve the multi-objective optimization problem to obtain the scheduling instructions corresponding to each energy medium. The scheduling instructions include the control parameter adjustment amount of each execution unit. The scheduling instruction data corresponding to each energy medium are categorized and combined according to the execution unit to generate the scheduling scheme.

[0034] In this embodiment, the cross-medium coupling constraints include gas-electricity coupling constraints, oxygen-nitrogen-argon-electricity coupling constraints, and production-consumption balance constraints for each energy medium. The gas-electricity coupling constraint is the adjustable output range of the gas generator set determined based on the current predicted gas production and consumption. The oxygen-nitrogen-argon-electricity coupling constraint is the adjustable load range of the oxygen generator set and its corresponding upper limit of power consumption determined based on the current predicted oxygen-nitrogen-argon production and consumption. The production-consumption balance constraints for each energy medium are the balance equations that must be satisfied between the predicted output and predicted consumption of each energy medium. The multi-objective optimization function uses minimizing pipeline pressure fluctuations, minimizing energy emissions, and minimizing equipment operating energy consumption as sub-objectives. Minimizing pipeline pressure fluctuations is quantified by minimizing the sum of squared deviations between the pipeline pressure of each medium and the set value. Minimizing energy emissions is quantified by minimizing the total amount of gas emissions. Minimizing equipment operating energy consumption is quantified by minimizing the total energy consumption of each execution unit. Using the predicted production and consumption of each energy medium as input parameters, cross-medium coupling constraints as boundary conditions, and a multi-objective optimization function as the optimization objective, a multi-objective optimization problem is constructed. A multi-objective optimization algorithm, such as particle swarm optimization (PSO), is employed to solve this problem. PSO initializes a swarm of particles, each representing a set of decision variables. By iteratively updating the position and velocity of the particles, the swarm moves towards the optimal solution of the optimization objective. Under the premise of satisfying all constraints, it searches for the optimal combination of decision variables that maximizes the effectiveness of each sub-objective, resulting in scheduling instructions for each energy medium. These instructions include adjustments to the control parameters of each execution unit. For example, for a gas system, the scheduling instructions are generator load adjustments; for a water system, they are pump control instructions and cooling tower fan control instructions; and for a power system, they are power load transfer schemes. The scheduling instruction data for each energy medium are then categorized and combined according to the execution unit to generate a scheduling scheme.

[0035] After generating the scheduling plan, scheduling instructions corresponding to each energy medium are issued to each execution unit. This issuance is conducted via industrial Ethernet or fieldbus networks, with the instructions sent to the controllers of each execution unit in the form of setpoints or control words. Execution units include controllers for gas generator sets, oxygen generators, water pump frequency converters, cooling tower fan controllers, and power switchgear. Following issuance, actual pipeline status feedback data is continuously collected from each execution unit after executing the scheduling instructions. This actual pipeline status feedback data includes the actual flow rate, actual pressure, and actual liquid level of each medium in the pipeline network, continuously collected by sensors and transmitters on each pipeline network at a sampling period of no less than one second.

[0036] The actual pipeline status feedback data is compared in real time with the predicted production and consumption corresponding to the scheduling plan to calculate the prediction deviation for each energy medium. The comparison is performed in a one-to-one correspondence manner. For each current moment, the difference between the actual production and consumption data collected at that moment and the predicted production and consumption data in the scheduling plan at that moment is calculated to obtain the prediction deviation for that moment. If the prediction deviation for any energy medium is greater than or equal to a preset deviation threshold, it indicates that the current predicted production and consumption has deviated from the actual operating conditions, requiring correction operations and continued scheduling and control operations. The preset deviation thresholds include gas pressure deviation thresholds, oxygen pressure deviation thresholds, nitrogen pressure deviation thresholds, and water tank level deviation thresholds. If the predicted gas pressure deviation is greater than or equal to the gas pressure deviation threshold, a correction operation is triggered.

[0037] In this embodiment, the step of correcting the predicted production and consumption of each energy medium based on the currently collected actual pipeline network status feedback data includes: For each energy medium, the actual production and consumption data and the actual influencing factor data at the corresponding time are extracted from the currently collected actual pipeline status feedback data to form the current sample data for that energy medium; The current sample data is added to the sample dataset corresponding to the energy medium to obtain the updated sample dataset corresponding to the energy medium. Based on the production and consumption sequence and influencing factor data in the updated sample dataset, the production and consumption prediction function corresponding to the energy medium is refitted to obtain the corrected production and consumption prediction function corresponding to the energy medium. Substitute the data on the influencing factors of the energy medium collected at the current moment into the corrected production and consumption prediction function to calculate the corrected predicted production and consumption of the energy medium. By iterating through all energy media, we obtain the corrected predicted production and consumption for each energy media.

[0038] For example, for coal gas, the current actual coal gas production and consumption, the current actual hot blast stove replacement status, and the current actual blast furnace oxygen enrichment rate are extracted from the currently collected actual pipeline status feedback data to constitute the current sample data for the coal gas. Refitting employs the same method as the aforementioned fitting process: first, the type of functional relationship is determined, and then the undetermined parameters are solved using the updated sample dataset. The influencing factor data for this energy medium collected at the current moment are substituted into the corrected production and consumption prediction function to calculate the corrected predicted production and consumption for that energy medium. This process is repeated for all energy media, i.e., coal gas, oxygen-nitrogen-argon, water, and electricity, performing all the operations from extracting the current sample data to substituting it into the corrected predicted production and consumption calculation to obtain the corrected predicted production and consumption for each energy medium. After correction, the scheduling and control operation continues, i.e., the complete process from prediction to distribution to collection to comparison to correction is re-executed.

[0039] As can be seen from the above, the embodiments of this application predict the energy medium separately and perform multi-objective optimization under cross-medium coupling constraints based on the predicted energy consumption of multiple energy media. This allows the scheduling scheme to simultaneously take into account the mutual influence between the media, overcoming the shortcomings of traditional independent scheduling of each medium leading to global imbalance. Secondly, the embodiments of this application compare the actual pipeline status feedback data after the scheduling command is issued and executed with the predicted energy consumption in real time. When the prediction deviation of any energy medium reaches a preset threshold, the prediction energy consumption is corrected and the scheduling and control operation continues to be executed in a loop. This ensures that the determination of the predicted energy consumption in each loop incorporates the information of the previous actual execution result. The predicted energy consumption is updated in a rolling manner as the operating conditions change, and the generation of the scheduling scheme is always based on the predicted energy consumption that is closest to the current actual operating conditions. This effectively solves the problem of the inability to correct the predicted value in time and the failure of the scheduling scheme when it deviates from the actual value in traditional open-loop scheduling. With repeated cycles of control operations, the actual network state of each energy medium gradually approaches and eventually stabilizes within the preset safety target range, realizing a shift from "passive response to deviation" to "actively approaching the target," thus comprehensively improving the accuracy and effectiveness of multi-energy medium coordinated scheduling. Therefore, the embodiments of this application can achieve coordinated scheduling of multiple energy media and improve scheduling accuracy.

[0040] Based on the same principle as the energy medium scheduling method provided in the embodiments of this application, the embodiments of this application also provide an energy medium scheduling device, such as... Figure 2 As shown, the energy medium scheduling device 20 may specifically include: a data acquisition module 21, a preprocessing module 22 and a scheduling and control module 23. The data acquisition module 21 is used to collect in real time the medium parameters corresponding to multiple types of energy media, as well as the equipment operation status data corresponding to each type of energy media. Preprocessing module 22 is used to preprocess the parameters of each type of medium and the corresponding equipment operating status data to obtain the standardized time series data corresponding to the energy medium. The scheduling and control module 23 is used to perform scheduling and control operations multiple times until the actual pipeline status feedback data of all energy media enters the target range; Specifically, when executing the scheduling and control operation, the scheduling and control module 23 is used for: For each energy medium, predictions are made based on standardized time-series data to obtain the predicted production and consumption for that energy medium. Based on the predicted production and consumption of multiple energy media, a multi-objective optimization solution is performed under cross-media coupling constraints to obtain a scheduling scheme, which includes scheduling instructions for each energy medium. The scheduling instructions corresponding to each energy medium are sent to each execution unit, and the actual pipeline status feedback data after each execution unit executes the scheduling instructions is continuously collected. The actual pipeline status feedback data is compared in real time with the predicted production and consumption corresponding to the scheduling scheme to calculate the prediction deviation of each energy medium. If the prediction deviation of any energy medium is greater than or equal to the preset deviation threshold, the predicted production and consumption of each energy medium will be corrected based on the currently collected actual pipeline status feedback data, and the scheduling and control operation will continue to be executed.

[0041] In one embodiment of this application, the preprocessing module 22 is specifically used for: For each type of media parameter, missing values ​​are imputed to obtain the imputed data corresponding to that media parameter; for each type of equipment operating status data, missing values ​​are imputed to obtain the imputed data corresponding to that equipment operating status data. Outlier removal is performed on the interpolated data corresponding to each medium parameter and the interpolated data corresponding to each device operating status data to obtain the removed data corresponding to each medium parameter and the removed data corresponding to each device operating status data. The data after removing the parameters of each medium and the data after removing the operating status data of each device are aligned according to a unified timestamp reference to obtain standardized time-series data for each energy medium.

[0042] In one embodiment of this application, when the scheduling and control module 23 makes predictions based on standardized time-series data corresponding to each energy medium to obtain the predicted production and consumption of that energy medium, it is specifically used for: Data from past time periods are extracted from the standardized time series data corresponding to the energy medium to obtain the historical time series data corresponding to the energy medium. Based on the energy medium flow rate in the historical time series data, the production and consumption of the energy medium in each unit time period are calculated to obtain the production and consumption sequence corresponding to the energy medium. The production and consumption sequence is associated and combined with the equipment operation status data at the corresponding time in the historical time series data to form a sample dataset corresponding to the energy medium. Influencing factor analysis was performed on the sample dataset to determine the influencing factors of the energy medium's production and consumption. Based on the production and consumption sequence in the sample dataset and the influencing factor data, a production and consumption prediction function corresponding to the energy medium is obtained by fitting. Substitute the data on the influencing factors of the energy medium collected at the current moment into the production and consumption prediction function to calculate the predicted production and consumption of the energy medium. By iterating through all energy media, the predicted production and consumption of each energy media can be obtained.

[0043] In one embodiment of this application, when the scheduling and control module 23 performs influencing factor analysis on the sample dataset to determine the influencing factor data of the energy medium's production and consumption, it is specifically used for: Obtain a preset set of candidate influencing factors, wherein the set of candidate influencing factors contains multiple candidate factors; For each candidate factor, the correlation coefficient between the candidate factor and the production and consumption sequence in the sample dataset is calculated to obtain the correlation coefficient corresponding to each candidate factor; The absolute value of the correlation coefficient corresponding to each candidate factor is compared with a preset correlation coefficient threshold. Candidate factors whose absolute correlation coefficients are greater than the preset correlation coefficient threshold are identified as influencing factors on the production and consumption of the energy medium. In one embodiment of this application, when the scheduling and control module 23 fits the production and consumption prediction function corresponding to the energy medium based on the production and consumption sequence in the sample dataset and the influencing factor data, it is specifically used for: Determine the type of functional relationship between the production and consumption of this energy medium and its influencing factors; Based on the aforementioned functional relationship type, using the production and consumption sequence in the sample dataset and the influencing factor data, the undetermined parameters in the functional relationship type are solved to obtain the production and consumption prediction function corresponding to the energy medium.

[0044] In one embodiment of this application, when the scheduling and control module 23 obtains a scheduling scheme by performing multi-objective optimization based on the predicted production and consumption of multiple energy media under cross-media coupling constraints, it is specifically used for: Obtain the pre-defined cross-medium coupling constraints; Obtain a pre-defined multi-objective optimization function; Using the predicted production and consumption corresponding to each energy medium as input parameters, the cross-medium coupling constraint as constraint boundary, and the multi-objective optimization function as optimization objective, a multi-objective optimization problem is constructed. A multi-objective optimization algorithm is used to solve the multi-objective optimization problem to obtain the scheduling instructions corresponding to each energy medium. The scheduling instructions include the control parameter adjustment amount of each execution unit. The scheduling instruction data corresponding to each energy medium are categorized and combined according to the execution unit to generate the scheduling scheme.

[0045] In one embodiment of this application, when the scheduling and control module 23 corrects the predicted production and consumption of each energy medium based on the currently collected actual pipeline status feedback data, it is specifically used for: For each energy medium, the actual production and consumption data and the actual influencing factor data at the corresponding time are extracted from the currently collected actual pipeline status feedback data to form the current sample data for that energy medium; The current sample data is added to the sample dataset corresponding to the energy medium to obtain the updated sample dataset corresponding to the energy medium. Based on the production and consumption sequence and influencing factor data in the updated sample dataset, the production and consumption prediction function corresponding to the energy medium is refitted to obtain the corrected production and consumption prediction function corresponding to the energy medium. Substitute the data on the influencing factors of the energy medium collected at the current moment into the corrected production and consumption prediction function to calculate the corrected predicted production and consumption of the energy medium. By iterating through all energy media, we obtain the corrected predicted production and consumption for each energy media.

[0046] The apparatus in this application embodiment can execute the method provided in this application embodiment, and the implementation principle is similar. The actions performed by each module in the apparatus of each embodiment of this application correspond to the steps in the method of each embodiment of this application. For detailed functional descriptions of each module of the apparatus, please refer to the descriptions in the corresponding methods shown above, which will not be repeated here.

[0047] Figure 3 A schematic diagram of the structure of an electronic device to which this application embodiment applies is shown, such as... Figure 3 As shown, the electronic device can be used to implement the methods provided in any embodiment of this application.

[0048] like Figure 3 As shown, the electronic device 300 may primarily include at least one processor 301. Figure 3 The diagram shows components such as a memory 302, a communication module 303, and an input / output interface 304. Optionally, these components can be connected and communicate with each other via a bus 305. It should be noted that... Figure 3 The structure of the electronic device 300 shown is merely illustrative and does not constitute a limitation on the electronic devices to which the methods provided in the embodiments of this application are applicable.

[0049] The memory 302 can be used to store operating systems and applications, etc. The applications can include computer programs that implement the methods shown in the embodiments of this application when invoked by the processor 301, and can also include programs for implementing other functions or services. The memory 302 can be ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, RAM (Random Access Memory) or other types of dynamic storage devices that can store information and computer programs, or it can be EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited thereto.

[0050] Processor 301 is connected to memory 302 via bus 305 and implements corresponding functions by calling the application programs stored in memory 302. Processor 301 can be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 301 can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0051] Electronic device 300 can connect to a network via communication module 303 (which may include, but is not limited to, components such as a network interface) to communicate with other devices (such as user terminals or servers) through the network and achieve data interaction, such as sending data to or receiving data from other devices. Communication module 303 may include wired network interfaces and / or wireless network interfaces, meaning the communication module may include at least one of wired or wireless communication modules.

[0052] The electronic device 300 can connect to necessary input / output devices, such as a keyboard and display device, via the input / output interface 304. The electronic device 300 itself may have a display device, and other external display devices can also be connected via the input / output interface 304. Optionally, a storage device, such as a hard drive, can also be connected via the input / output interface 304 to store data from the electronic device 300, retrieve data from the storage device, or store data from the storage device in the memory 302. It is understood that the input / output interface 304 can be a wired interface or a wireless interface. Depending on the actual application scenario, the device connected to the input / output interface 304 can be a component of the electronic device 300 or an external device connected to the electronic device 300 when needed.

[0053] The bus 305 used to connect the components may include a path for transmitting information between the components. The bus 305 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Depending on its function, the bus 305 may be divided into an address bus, a data bus, a control bus, etc.

[0054] Optionally, for the solution provided in the embodiments of this application, the memory 302 can be used to store a computer program that executes the solution of this application, and the processor 301 runs the computer program. When the processor 301 runs the computer program, it implements the operation of the method or apparatus provided in the embodiments of this application.

[0055] Based on the same principle as the method provided in the embodiments of this application, the embodiments of this application provide a computer-readable storage medium storing a computer program, which, when executed by a processor, can implement the corresponding content of the aforementioned method embodiments.

[0056] It should be noted that the terms "first," "second," "third," "fourth," "1," "2," etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in a sequence other than that shown in the figures or text.

[0057] In the embodiments of this application, the terms "module" or "unit" refer to a computer program or part of a computer program that has a predetermined function and works with other related parts to achieve a predetermined goal, and can be implemented wholly or partially using software, hardware (such as processing circuitry or memory), or a combination thereof. Similarly, a processor (or multiple processors or memory) can be used to implement one or more modules or units. Furthermore, each module or unit can be part of an overall module or unit that includes the functionality of that module or unit.

[0058] It should be understood that although arrows indicate various operation steps in the flowcharts of this application's embodiments, the order in which these steps are implemented is not limited to the order indicated by the arrows. Unless explicitly stated herein, in some implementation scenarios of this application's embodiments, the implementation steps in each flowchart can be executed in other orders as required. Furthermore, some or all steps in each flowchart, based on the actual implementation scenario, may include multiple sub-steps or multiple stages. Some or all of these sub-steps or stages can be executed at the same time, and each sub-step or stage can also be executed at different times. In scenarios where execution times differ, the execution order of these sub-steps or stages can be flexibly configured according to requirements, and this application's embodiments do not limit this.

[0059] The above description is only an optional implementation method for some implementation scenarios of this application. It should be noted that for those skilled in the art, other similar implementation methods based on the technical concept of this application without departing from the technical concept of this application also fall within the protection scope of the embodiments of this application.

Claims

1. An energy medium dispatching method, characterized in that, include: Real-time acquisition of medium parameters corresponding to multiple types of energy media, as well as equipment operating status data corresponding to each type of energy media; Preprocessing is performed on the parameters of each type of medium and the corresponding equipment operating status data to obtain standardized time-series data for that energy medium; Multiple scheduling and control operations are performed until the actual pipeline status feedback data of all energy media enters the target range; The scheduling and control operation includes the following steps: For each energy medium, predictions are made based on standardized time-series data to obtain the predicted production and consumption for that energy medium. Based on the predicted production and consumption of multiple energy media, a multi-objective optimization solution is performed under cross-media coupling constraints to obtain a scheduling scheme, which includes scheduling instructions for each energy medium. The scheduling instructions corresponding to each energy medium are sent to each execution unit, and the actual pipeline status feedback data after each execution unit executes the scheduling instructions is continuously collected. The actual pipeline status feedback data is compared in real time with the predicted production and consumption corresponding to the scheduling scheme to calculate the prediction deviation of each energy medium. If the prediction deviation of any energy medium is greater than or equal to the preset deviation threshold, the predicted production and consumption of each energy medium will be corrected based on the currently collected actual pipeline status feedback data, and the scheduling and control operation will continue to be executed.

2. The energy medium dispatching method as described in claim 1, characterized in that, The preprocessing of parameters for each type of medium and the corresponding equipment operating status data to obtain standardized time-series data for that energy medium includes: For each type of media parameter, missing values ​​are imputed to obtain the imputed data corresponding to that media parameter; for each type of equipment operating status data, missing values ​​are imputed to obtain the imputed data corresponding to that equipment operating status data. Outlier removal is performed on the interpolated data corresponding to each medium parameter and the interpolated data corresponding to each device operating status data to obtain the removed data corresponding to each medium parameter and the removed data corresponding to each device operating status data. The data after removing the parameters of each medium and the data after removing the operating status data of each device are aligned according to a unified timestamp reference to obtain standardized time-series data for each energy medium.

3. The energy medium dispatching method as described in claim 1, characterized in that, The process of predicting the energy consumption for each energy medium using standardized time-series data includes: Data from past time periods are extracted from the standardized time series data corresponding to the energy medium to obtain the historical time series data corresponding to the energy medium. Based on the energy medium flow rate in the historical time series data, the production and consumption of the energy medium in each unit time period are calculated to obtain the production and consumption sequence corresponding to the energy medium. The production and consumption sequence is associated and combined with the equipment operation status data at the corresponding time in the historical time series data to form a sample dataset corresponding to the energy medium. Influencing factor analysis was performed on the sample dataset to determine the influencing factors of the energy medium's production and consumption. Based on the production and consumption sequence in the sample dataset and the influencing factor data, a production and consumption prediction function corresponding to the energy medium is obtained by fitting. Substitute the data on the influencing factors of the energy medium collected at the current moment into the production and consumption prediction function to calculate the predicted production and consumption of the energy medium. By iterating through all energy media, the predicted production and consumption of each energy media can be obtained.

4. The energy medium dispatching method as described in claim 3, characterized in that, The step of performing influencing factor analysis on the sample dataset to determine the influencing factors on the production and consumption of the energy medium includes: Obtain a preset set of candidate influencing factors, wherein the set of candidate influencing factors contains multiple candidate factors; For each candidate factor, the correlation coefficient between the candidate factor and the production and consumption sequence in the sample dataset is calculated to obtain the correlation coefficient corresponding to each candidate factor; The absolute value of the correlation coefficient corresponding to each candidate factor is compared with a preset correlation coefficient threshold. Candidate factors whose absolute correlation coefficient is greater than the preset correlation coefficient threshold are identified as influencing factors on the production and consumption of the energy medium.

5. The energy medium dispatching method as described in claim 3, characterized in that, The step of fitting the production and consumption prediction function corresponding to the energy medium based on the production and consumption sequence in the sample dataset and the influencing factor data includes: Determine the type of functional relationship between the production and consumption of this energy medium and its influencing factors; Based on the aforementioned functional relationship type, using the production and consumption sequence in the sample dataset and the influencing factor data, the undetermined parameters in the functional relationship type are solved to obtain the production and consumption prediction function corresponding to the energy medium.

6. The energy medium dispatching method as described in claim 1, characterized in that, The method, based on predicted energy consumption corresponding to multiple energy media, performs multi-objective optimization under cross-media coupling constraints to obtain a scheduling scheme, including: Obtain the pre-defined cross-medium coupling constraints; Obtain a pre-defined multi-objective optimization function; Using the predicted production and consumption corresponding to each energy medium as input parameters, the cross-medium coupling constraint as constraint boundary, and the multi-objective optimization function as optimization objective, a multi-objective optimization problem is constructed. A multi-objective optimization algorithm is used to solve the multi-objective optimization problem to obtain the scheduling instructions corresponding to each energy medium. The scheduling instructions include the control parameter adjustment amount of each execution unit. The scheduling instruction data corresponding to each energy medium are categorized and combined according to the execution unit to generate the scheduling scheme.

7. The energy medium dispatching method as described in claim 1, characterized in that, The correction of the predicted production and consumption for each energy medium based on the currently collected actual pipeline network status feedback data includes: For each energy medium, the actual production and consumption data and the actual influencing factor data at the corresponding time are extracted from the currently collected actual pipeline status feedback data to form the current sample data for that energy medium; The current sample data is added to the sample dataset corresponding to the energy medium to obtain the updated sample dataset corresponding to the energy medium. Based on the production and consumption sequence and influencing factor data in the updated sample dataset, the production and consumption prediction function corresponding to the energy medium is refitted to obtain the corrected production and consumption prediction function corresponding to the energy medium. Substitute the data on the influencing factors of the energy medium collected at the current moment into the corrected production and consumption prediction function to calculate the corrected predicted production and consumption of the energy medium. By iterating through all energy media, we obtain the corrected predicted production and consumption for each energy media.

8. An energy medium dispatching device, characterized in that, include: The data acquisition module is used to collect in real time the medium parameters corresponding to various types of energy media, as well as the equipment operation status data corresponding to each type of energy media. The preprocessing module is used to preprocess the parameters of each type of medium and the corresponding equipment operating status data to obtain the standardized time-series data corresponding to the energy medium. The scheduling and control module is used to execute scheduling and control operations multiple times until the actual pipeline status feedback data of all energy media enters the target range. Specifically, when the scheduling and control module performs the scheduling and control operation, it is used for: For each energy medium, predictions are made based on standardized time-series data to obtain the predicted production and consumption for that energy medium. Based on the predicted production and consumption of multiple energy media, a multi-objective optimization solution is performed under cross-media coupling constraints to obtain a scheduling scheme, which includes scheduling instructions for each energy medium. The scheduling instructions corresponding to each energy medium are sent to each execution unit, and the actual pipeline status feedback data after each execution unit executes the scheduling instructions is continuously collected. The actual pipeline status feedback data is compared in real time with the predicted production and consumption corresponding to the scheduling scheme to calculate the prediction deviation of each energy medium. If the prediction deviation of any energy medium is greater than or equal to the preset deviation threshold, the predicted production and consumption of each energy medium will be corrected based on the currently collected actual pipeline status feedback data, and the scheduling and control operation will continue to be executed.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the energy medium scheduling method according to any one of claims 1 to 7 when running the computer program.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the energy medium scheduling method according to any one of claims 1 to 7.

Images