An intelligent dispatching system based on internet of things and a load dispatching method thereof

Abstract

The application relates to an intelligent scheduling system based on an Internet of Things, which at least comprises a second data processing module, which is used for creating a corresponding real-time data mapping model according to real-time state data, comparing the real-time data mapping model with a pre-stored state model, and realizing real-time monitoring of the power Internet of Things; generating or calling instruction information about at least one load unit associated with the comparison result in a database through the comparison, and displaying the obtained instruction information to a host computer by using a display interface, so that an operator can perform scheduling monitoring; updating the instruction information according to feedback information input by the operator through the host computer, actively following the operation information of the operator on the host computer based on the updated instruction information, and comparing the operation information with the updated instruction information, so that human-computer bidirectional monitoring is realized.

Description

Technical Field

[0001] This invention relates to the field of smart grid technology, and in particular to an Internet of Things-based smart dispatching system and its load dispatching method. Background Technology

[0002] Intelligent dispatching of power grids encompasses numerous functions, such as real-time monitoring, regulation and control, dispatch planning, analysis, and evaluation. Real-time monitoring refers to monitoring the current operational status of the power grid, including its dynamics and stability, as well as the operational status of downstream power grids and ancillary services. It also monitors non-grid factors affecting grid operation, such as weather conditions and related functions. Furthermore, intelligent dispatching provides early warning capabilities, including alerts on reserve capacity and the impact of disasters. Based on real-time monitoring information and employing various analytical methods, such as advance or real-time analysis, the intelligent dispatching automation system can control the operation of the power grid, including emergency response plans for accidents and control of load factors. The system can develop scientifically sound plans based on actual conditions and implement enhanced management processes, focusing on fault management, inspection and maintenance, and information dissemination. In the event of a fault, it can activate emergency plans as quickly as possible. Many factors can influence intelligent dispatching of the power grid; therefore, these factors need to be considered in practical planning. Currently, while the technology for intelligent dispatching in China's power grid is advancing rapidly, the cost is relatively high, and implementation is difficult, impacting business development. Therefore, it is necessary to reduce reliance on operating systems, utilize software more efficiently, and emphasize the importance of modules. Ensuring the scalability of the architecture and guaranteeing secure and more efficient intelligent dispatching places certain demands on research, requiring specific strategies to improve technical capabilities and ensure safer and more reliable intelligent dispatching of the power grid.

[0003] Currently, intelligent scheduling systems are commonly used to conveniently and effectively control a large number of IoT devices. These systems typically consist of sensors, actuators, controllers, and a host computer. In daily applications, intelligent scheduling systems utilize preset control logic in the controller to adjust the start / stop and operating parameters of different devices accordingly. The system will shut down or start different devices at different times. For example, patent document CN111126885B proposes an IoT-based smart electricity scheduling method and system. Based on identification results and operating data of electrical equipment, it achieves scientific electricity management and scheduling to reduce energy waste and electricity accidents. The method includes at least the following steps: First, real-time acquisition of operating data of electrical equipment in each area and information uploaded by acquisition devices in each area; second, determination of personnel distribution in each area and the personnel distribution trends in other areas based on the identification results obtained from the acquisition information in each area; finally, control of the operating status of electrical equipment in each area based on the personnel distribution and operating data of the electrical equipment in that area, and scheduling of the operating status of electrical equipment in other areas based on the personnel distribution trends in other areas. In the case of using an intelligent scheduling system to automatically control various devices, the system does not provide any prompts before the devices start or stop or before the operating parameters change. This makes it impossible for operators to understand the intentions of the intelligent scheduling system in advance, or to intervene or prepare in advance. They can only discover the sudden changes when the devices start or stop or after the operating parameters change, which creates a dilemma of inefficient coordination between operators and the intelligent scheduling system.

[0004] Furthermore, on the one hand, there are differences in understanding among those skilled in the art; on the other hand, the applicant studied a large number of documents and patents when making this invention, but due to space limitations, not all details and contents were listed in detail. However, this does not mean that the present invention does not possess the features of these prior art. On the contrary, the present invention already possesses all the features of the prior art, and the applicant reserves the right to add relevant prior art to the background art. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides an intelligent dispatching system based on the Internet of Things (IoT), comprising at least: a first data processing module, used to acquire real-time status data of multiple load units connected to the intelligent dispatching system in response to operator input or through automatic acquisition; a second data processing module, used to create a corresponding real-time data mapping model based on the real-time status data, and compare the real-time data mapping model with a pre-stored status model to achieve real-time monitoring of the power IoT; the second data processing module generates or retrieves instruction information about at least one load unit associated with the comparison result from the database through the above comparison, and displays the obtained instruction information to the host computer using a display interface for operator dispatching and monitoring; the second data processing module updates the instruction information according to the feedback information received by the operator through the host computer, and actively follows the operator's operation information on the host computer based on the updated instruction information, comparing the operation information with the updated instruction information to achieve two-way human-machine supervision. Under this setup, not only are control commands executed only after confirmation by the operator, ensuring the safety and reliability of power grid dispatch from both the system and personnel perspectives; but the execution process of control commands is also tracked and monitored by the system, achieving two-way supervision between personnel and the system, which can eliminate potential operational error risks.

[0006] According to a preferred embodiment, the first data processing module collects historical and monitoring data related to multiple load units, and judges the status of multiple load units and the system status through historical data analysis, thereby realizing real-time monitoring of the operating parameters and actual working conditions of multiple load units and the system, and uses the Internet of Things to interconnect the monitoring information generated after its preprocessing.

[0007] According to a preferred embodiment, the second data processing module actively handles abnormal operating conditions existing in multiple load units and / or the system based on the monitoring information generated after the first data processing module processes the collected data, and notifies the operator through the display interface so that the operator can perform timely scheduling and monitoring.

[0008] According to a preferred embodiment, the intelligent scheduling system further includes a third data processing module, which is used to group multiple load units connected to the intelligent scheduling system into corresponding groups by assigning numbers to each unit.

[0009] According to a preferred embodiment, the intelligent scheduling system further includes a fourth data processing module, which is used to obtain primary production association information of multiple load units accessing the intelligent scheduling system by analyzing historical data and combining it with basic information about the equipment's full life cycle ledger, and to generate secondary production association information about each group based on the primary production association information of each load unit under each group.

[0010] According to a preferred embodiment, the first data processing module is used to acquire real-time status data of multiple load units accessing the intelligent scheduling system and establish a load fluctuation domain of the system based on the real-time status data through load fluctuation analysis. The second data processing module generates or retrieves instruction information about at least one load unit from the database according to the load fluctuation domain.

[0011] According to a preferred embodiment, upon receiving a power dispatching demand instruction containing specified load data, the second data processing module determines the floating load difference between the system's load floating domain and the specified load data, and generates an inspection and maintenance instruction for at least one load unit when the floating load difference triggers inspection and maintenance conditions, or generates a dispatching optimization instruction for the operating status of at least one load unit when the floating load difference triggers dispatching optimization conditions.

[0012] This application also proposes a load dispatching method based on the Internet of Things (IoT), comprising at least: acquiring real-time status data of multiple load units connected to the intelligent dispatching system using a first data processing module in response to operator input or automatic acquisition. The method comprises at least: creating a corresponding real-time data mapping model based on the real-time status data using a second data processing module. The method comprises at least: comparing the real-time data mapping model with a pre-stored status model using the second data processing module to achieve real-time monitoring of the power IoT. The method comprises at least: generating or retrieving instruction information about at least one load unit associated with the comparison result from a database through the above comparison. The method comprises at least: displaying the obtained instruction information to a host computer using a display interface for operator dispatching and monitoring. The method comprises at least: updating the instruction information using the second data processing module based on feedback information received from the operator via the host computer. The method comprises at least: actively following the operator's operation information on the host computer using the second data processing module based on the updated instruction information. The method comprises at least: comparing the operation information with the updated instruction information to achieve two-way human-machine supervision.

[0013] This application also proposes an IoT-based intelligent dispatching system, comprising at least: a first data processing module, used to acquire real-time status data of multiple load units connected to the intelligent dispatching system and establish a load fluctuation domain for the system based on the real-time status data through load fluctuation analysis. The method comprises at least: a second data processing module, used to determine the floating load difference between the system's load fluctuation domain and the specified load data when receiving a power dispatching demand instruction containing specified load data, and to generate an inspection and maintenance instruction for at least one load unit when the floating load difference triggers inspection and maintenance conditions, or to generate a dispatching optimization instruction for the operating status of at least one load unit when the floating load difference triggers dispatching optimization conditions.

[0014] This application also proposes an Internet of Things (IoT)-based intelligent dispatching system, which includes at least a load envelope calculation module configured to: determine a sliding time window based on the current time and establish a load floating domain for each load unit based on load data within the sliding time window. The load envelope calculation module is further configured to: classify and assign a specified load unit and its corresponding load floating domain to each associated load unit, so that upon receiving a power dispatching demand instruction containing specified load data, it can generate a dispatching optimization instruction regarding the operating status of at least one load unit based on the floating load difference between the specified load data and the load floating domains corresponding to load units under different categories.

[0015] According to a preferred embodiment, the intelligent scheduling system further includes: a third data processing module, used to group multiple load units accessing the intelligent scheduling system into corresponding groups by assigning numbers to each unit; and a fourth data processing module, used to obtain primary production association information of multiple load units accessing the intelligent scheduling system through historical data analysis combined with a custom method, and to generate secondary production association information for each group based on the primary production association information of each load unit under each group.

[0016] According to a preferred embodiment, the load floating domain established by the first data processing module includes the up and down envelope curves and / or load prediction curves drawn in a virtual two-dimensional coordinate system within a sliding time window determined according to the current time, wherein the up and down envelope curves can be used to indicate the prediction error fluctuation of the load prediction curve.

[0017] According to a preferred embodiment, the first data processing module determines the sliding time window corresponding to the current moment based on a preset minimum window building time.

[0018] According to a preferred embodiment, if the time interval between the current time information and the end time of the preset time window is not greater than the preset minimum window building time, the first data processing module, based on the current time information and the minimum window building time, takes the current time information as the new window start time and extends it backward by a span of the minimum window building time, thereby forming a sliding time window corresponding to the current time.

[0019] According to a preferred embodiment, the intelligent dispatching system further includes at least one power station for converting the high voltage of the high voltage module to the low voltage of the low voltage module to provide power to the load unit. The power station can monitor one or more of the load unit, the operating environment, and the secondary low voltage switches it is connected to in real time and transmit the monitoring data to at least one data processing module.

[0020] This application also proposes an Internet of Things-based load dispatching method, which includes at least: acquiring real-time status data of multiple load units connected to the intelligent dispatching system and establishing a load floating domain for the system based on the real-time status data through load floating analysis; determining the floating load difference between the load floating domain of the system and the specified load data when receiving a power dispatching demand instruction containing specified load data; generating an inspection and maintenance instruction for at least one load unit when the floating load difference triggers inspection and maintenance conditions, or generating a dispatching optimization instruction for the operating status of at least one load unit when the floating load difference triggers dispatching optimization conditions.

[0021] According to a preferred embodiment, the method further includes: grouping multiple load units connected to the intelligent scheduling system into corresponding groups by assigning numbers one by one; obtaining primary production association information of multiple load units connected to the intelligent scheduling system through historical data analysis combined with a custom method; and generating secondary production association information about each group based on the primary production association information of each load unit under each group.

[0022] This application also proposes an Internet of Things-based load dispatching method, which includes at least: determining a sliding time window based on the current time and establishing a load floating domain for each load unit based on load data of the load unit within the sliding time window; classifying a specified load unit and its corresponding load floating domain and each associated load unit; and generating a dispatching optimization instruction for the operating status of at least one load unit based on the floating load difference between the specified load data and the load floating domains corresponding to load units under different categories when a power dispatching demand instruction containing specified load data is received. Attached Figure Description

[0023] Figure 1 This is a simplified schematic diagram of the module connections of an IoT-based intelligent scheduling system according to a preferred embodiment of this application.

[0024] Figure 2 This is a simplified schematic diagram of the module connections of an IoT-based intelligent scheduling system according to another preferred embodiment of this application.

[0025] List of reference numerals

[0026] 101: First data processing module; 102: Second data processing module; 103: Third data processing module; 104: Fourth data processing module; 105: Fifth data processing module; 106: Load envelope calculation module; 107: Scheduling module. Detailed Implementation

[0027] The present application will now be described in detail with reference to the accompanying drawings.

[0028] Example 1

[0029] This embodiment proposes an intelligent scheduling system based on the Internet of Things (IoT), including several data processing modules. The modules mentioned in this embodiment can refer to hardware, software, or a combination of data processors capable of executing their related steps. A method step corresponding to a certain module can also be broken down into multiple method steps and executed separately by multiple modules. Where there is no conflict or contradiction, the whole and / or part of the preferred embodiments of other embodiments can be used as supplements to this embodiment.

[0030] The intelligent scheduling system may include a third data processing module 103, which is used to group multiple load units accessing the intelligent scheduling system into corresponding groups by assigning numbers to each unit. In this embodiment, grouping may refer to dividing multiple load units into multiple production-related links. This embodiment divides multiple load units in production operations into multiple production-related links based on the correlation between some load units.

[0031] In this embodiment, the category to which each production-related link belongs can be determined based on the number of specified load units contained in the production-related link. For example, the category to which each production-related link belongs can be determined based on the number of a certain device contained in the production-related link. Preferably, the specified load unit can be the load unit with the greatest load impact or the greatest load fluctuation in the production-related link.

[0032] For a single production-related link, among the multiple load units it contains, there is a first sub-load unit that is synchronized with the load impact of the designated load unit on the power grid, and a second sub-load unit that is asynchronous with the load impact of the designated load unit on the power grid.

[0033] The intelligent scheduling system may include a fourth data processing module 104, used to acquire primary production association information of multiple load units connected to the intelligent scheduling system. The fourth data processing module 104 is used to generate secondary production association information for each group.

[0034] Specifically, the fourth data processing module 104 obtains primary production-related information of multiple load units connected to the intelligent scheduling system through historical data analysis combined with a customized approach. The primary production-related information mentioned in this embodiment refers to the working status and / or working data of the load units. The working status of the load units refers to the operational status of multiple load units connected to the intelligent scheduling system, particularly including information on critical periods of downhole operations and / or operation time periods.

[0035] This intelligent scheduling system includes data acquisition devices corresponding to multiple load units. These devices are located at the load unit level and are used to collect real-time data from monitoring equipment such as sensors during actual production operations and / or pre-stored daily or multi-period production plans in each load unit. Examples include flow signals, speed signals, and operating times of relevant pumps.

[0036] Each data acquisition device can perform statistical analysis on the data signals it acquires. For example, statistical analysis can determine the maximum fluctuation range, median value, maximum and minimum values ​​of a certain operating parameter of the current monitoring device. Preferably, each data acquisition device can determine the primary production correlation information of the current load unit based on the statistical analysis results. The primary production correlation information can be used to characterize the operating status of the current load unit. Each data acquisition device and the fourth data processing module 104 can be wirelessly connected to transmit data such as primary production correlation information. Specifically, the intelligent scheduling system has a communication module and a storage module. The intelligent scheduling system can store the data obtained from the data acquisition devices through the communication module into the storage module for retrieval and / or modification by other data processing modules within the system.

[0037] Specifically, the fourth data processing module 104 generates secondary production association information for each group based on the primary production association information of each load unit under each group. The secondary production association information mentioned in this embodiment differs from the primary production association information in that primary production association information corresponds to different load units, while secondary production association information corresponds to a group or production association link containing at least one load unit. Preferably, the fourth data processing module 104 can determine the range of primary production association data or identify the primary production association data with the maximum value by statistically analyzing the primary production association information of each load unit under each group. The secondary production association information can be a numerical range or a single value.

[0038] The intelligent scheduling system includes a first data processing module 101, which is used to acquire real-time status data of multiple load units connected to the intelligent scheduling system. Preferably, the real-time status data can be acquired by the first data processing module 101 by retrieving stored data from the data acquisition device stored in the storage module. The real-time status data can be primary production-related information, or data different from primary production-related information. The real-time status data is mainly used to reflect the actual operating data of the load units, while the primary production-related information is mainly used to reflect the working status determined based on the actual operating data of the load units.

[0039] Preferably, the intelligent dispatching system also includes at least one power station for converting the high voltage of the high-voltage module to the low voltage of the low-voltage module to achieve intelligent power supply to the load unit. The power station can transmit data to the storage module via wired or wireless means. The power station can perform real-time monitoring of key equipment such as load units (e.g., high and low voltage switches, transformers, communication devices, etc.), operating environment (e.g., temperature and humidity), and connected secondary low-voltage switches (e.g., voltage, current, position). By acquiring the monitoring data from the power station, the actual load data can be determined.

[0040] The first data processing module acquires real-time status data from multiple load units connected to the intelligent scheduling system in response to operator input or through automatic acquisition. The second data processing module creates a corresponding real-time data mapping model based on the real-time status data.

[0041] The first data processing module uses computer equipment to establish a multi-source heterogeneous data fusion database based on pre-stored power grid infrastructure distribution information. Based on this database, the first data processing module constructs a real-time data mapping model. The operational data in the real-time data mapping model is kept synchronized with the real-time status data obtained by the first data processing module.

[0042] The first data processing module compares the real-time data mapping model with the pre-storage state model to achieve real-time monitoring of the power Internet of Things. The pre-storage state model can be obtained in the following way: The first data processing module uses computer equipment to establish a multi-source heterogeneous data fusion database based on the pre-stored historical fault information of power grid equipment. The first data processing module constructs a pre-storage state model, such as a power grid equipment fault prediction model, based on the established multi-source heterogeneous data fusion database.

[0043] The intelligent scheduling system also includes a second data processing module. This module generates or retrieves instruction information about at least one load unit from the database, which is associated with the comparison results, based on the aforementioned comparison. This instruction information is then displayed on a host computer via a screen interface, allowing operators to perform scheduling and monitoring based on the instruction information.

[0044] After the received instruction information is displayed to the host computer, the operator inputs feedback information to the second data processing module via the host computer. The second data processing module updates the instruction information based on the feedback information. Updating the instruction information can involve adjusting specified data or canceling or adding related instructions. The second data processing module actively follows the operator's actions on the host computer based on the updated instruction information. The operator performs operations on the host computer to execute the corresponding instruction information, and simultaneously, the second data processing module actively tracks the operator's actions on the host computer and compares them with the updated instruction information to ensure the operator's actions are correct or to promptly remind the operator of the operation steps. Under this setup, not only are control instructions executed only after operator confirmation, ensuring the safety and reliability of power grid dispatch from both system and personnel perspectives; but the execution process of control instructions is also tracked and monitored by the system, achieving two-way supervision between personnel and the system, and eliminating potential operational error risks.

[0045] The first data processing module collects historical and monitoring data related to multiple load units. Through historical data analysis, it assesses the status of these load units and the system as a whole, determining if any abnormal operating conditions exist. This enables real-time monitoring of the operating parameters and actual conditions of multiple load units and the system. The first data processing module also utilizes the Internet of Things (IoT) to interconnect the pre-processed monitoring information across the system.

[0046] The second data processing module proactively addresses abnormal operating conditions in multiple load units and / or the system based on the monitoring information generated after processing the data collected by the first data processing module. The second data processing module notifies the operator of the abnormal operating condition information through a display interface, enabling the operator to perform timely scheduling and monitoring.

[0047] Example 2

[0048] This embodiment may be a further improvement and / or supplement to the foregoing embodiments, and repeated content will not be described again. Where there is no conflict or contradiction, the whole and / or part of the preferred embodiments of other embodiments may be used as supplements to this embodiment.

[0049] This embodiment proposes an intelligent scheduling system based on the Internet of Things, which includes several data processing modules. The modules mentioned in this embodiment can refer to hardware, software or a combination of data processors that can execute their related steps. A method step corresponding to a certain module can also be divided into multiple method steps and executed by multiple modules respectively.

[0050] Traditional drilling site power and control equipment is generally configured with diesel engines or generator sets driving generators or self-generating equipment. While this equipment has contributed to the implementation of drilling projects, it also has the following drawbacks: numerous pieces of equipment, a large footprint, and complex operation and maintenance; in addition, it consumes a lot of energy and materials—even if only a single light is needed on-site, a diesel engine or generator must be started, resulting in high costs; furthermore, the drilling rig control system has poor stability and generates significant noise pollution; on the other hand, drilling workers experience high labor intensity; even when using grid electricity, the main load on-site is a variable frequency motor, which generates a large number of harmonics during operation, primarily the 5th, 7th, and 11th harmonics. These harmonics are a nuisance to the power grid, and the harmonics themselves also cause losses, leading to transformer temperature rise and affecting the normal operation of precision instruments such as meters. To ensure the safe and stable operation of oilfields, most existing drilling sites have integrated the aforementioned intelligent phase change technology to build intelligent power dispatching platforms for energy-saving control and monitoring. For example, patent document CN111126885B proposes an IoT-based intelligent power dispatching method and system. Based on identification results and operating data of electrical equipment, it achieves scientific power management and dispatching to reduce energy waste and electrical accidents. The method includes at least the following steps: First, real-time acquisition of operating data of electrical equipment in each area and information uploaded by acquisition devices in each area; second, determination of personnel distribution in each area and personnel distribution trends in other areas based on the identification results obtained from the acquisition information in each area; finally, control of the operating status of electrical equipment in each area based on the personnel distribution and operating data of electrical equipment in that area, and dispatching of the operating status of electrical equipment in other areas based on the personnel distribution trends in other areas.

[0051] The aforementioned technical solution mainly controls the operating status of electrical equipment in each area based on the personnel distribution and schedules the operating status of electrical equipment in other areas based on the personnel distribution trends in other areas. It is well-suited for situations where personnel distribution information is readily available. However, for oilfield-type field production operations located in remote areas with large electrical equipment coverage, on the one hand, the method of obtaining personnel distribution information through image acquisition proposed in the aforementioned technical solution requires a very large investment in equipment to achieve the accuracy of information collection; on the other hand, personnel movement between different areas is infrequent during oilfield-type field production operations, and even when personnel move, it is mostly by means of transportation. Therefore, the method of obtaining personnel distribution information through image acquisition proposed in the aforementioned technical solution cannot obtain effective personnel distribution data. Furthermore, the correlation between the operating status of electrical equipment and personnel distribution trends in actual oilfield-type field production operations is weak, making the method of scheduling electrical equipment within an area based on personnel distribution proposed in the aforementioned technical solution unreliable.

[0052] The intelligent scheduling system may include a third data processing module 103, which is used to group multiple load units connected to the intelligent scheduling system into corresponding groups by assigning numbers to each unit. The load units mentioned in this embodiment refer to oil wells, metering stations, combined stations, produced water treatment stations, water injection stations, water injection pumps, water distribution rooms, or water injection wells, etc. The grouping mentioned in this embodiment may refer to dividing multiple load units into multiple production-related links.

[0053] Since the partial load units in oilfield production operations are interconnected, this embodiment divides the multiple load units in oilfield production operations into multiple production-related links based on the correlation between the partial load units. A single production-related link may include one or more of the following: oil well, combined station, oil well, gathering and transportation station, water injection station, and water injection well. For example, oil wells, water injection pumps, and water injection wells can be associated with wastewater storage facilities through combined stations, transfer stations, or produced water treatment stations, forming a single, relatively independent production-related link.

[0054] A single production linkage may include a topological relationship based on one or more of the following: oil well, metering station, combined station, produced water treatment station, water injection station, water injection pump, water distribution room, and water injection well, as well as relevant production operation data for each load unit. Relevant production operation data for oil wells as load units should at least include well number, type, associated power line, affiliated unit, production time, power consumption, fluid production, water cut, motor power, and production status characteristics. The oil well type may be, for example, an electric pump well or a pumping unit well. Production status characteristics may include sand production, wax deposition, salt deposition, oil thickening, and / or water adulteration. Relevant production operation data for metering stations as production units may include station name, associated well number, and affiliated combined station. Relevant production operation data for combined stations as production units may include station name, associated metering station / oil well, and associated produced water treatment station. Production operation data for a produced water treatment station as a production unit may include station name, associated joint stations, number of emergency tanks, tank height, cross-sectional area, safe height, minimum liquid level, water collection pump data, water collection pump discharge rate, and associated injection stations. Production operation data for an injection station as a production unit may include station name, associated produced water treatment station, number of storage tanks, tank height, cross-sectional area, safe height, minimum liquid level, number of pumps installed, design capacity, and design pressure. Production operation data for an injection pump as a production unit may include the injection station, pump number, model, associated line, discharge rate, motor power, and pump efficiency. Production operation data for a water well as a production unit may include well number, associated injection station, daily injection volume, actual injection volume, oil pressure, mains pressure, dynamic classification, water increase, allowable injection time, and lifting method.

[0055] In this embodiment, the classification of each production-related link can be determined based on the number of specified load units included in the production-related link. The specified load units can be oil wells, water injection wells, combined stations, or produced water treatment stations. For example, the classification of each production-related link can be determined based on the number of oil wells included in the production-related link. Classifications can include single-well production-related links, multi-well production-related links, and / or planned production-related links. A single-well production-related link uses a single oil well as the core link, identifying other load units with certain production operation relevance to it and collectively forming the current single-well production-related link. These other load units can be associated pumps or water injection wells, etc. A multi-well production-related link uses two or more oil wells with related production operations as the core link, identifying other load units with certain production operation relevance to it and collectively forming the current multi-well production-related link. A planned production-related link can be a link formed by one or more load units, customized by production management personnel based on actual production operations. This ensures the adaptability of the overall system to different types of oilfield production operations.

[0056] Preferably, the designated load unit can be the load unit with the greatest load impact or the greatest load fluctuation in the production linkage. Apart from joint stations and other facilities that typically have a large workload due to being connected to multiple oil wells, the load unit with the greatest load impact or the greatest load fluctuation in the production linkage is the oil well.

[0057] For a single production-related link, among the multiple load units it contains, there is a first sub-load unit that is synchronized with the load impact of the designated load unit on the power grid, and a second sub-load unit that is asynchronous with the load impact of the designated load unit on the power grid.

[0058] The intelligent scheduling system may include a fourth data processing module 104, used to acquire primary production association information of multiple load units connected to the intelligent scheduling system. The fourth data processing module 104 is used to generate secondary production association information for each group.

[0059] Specifically, the fourth data processing module 104 obtains primary production-related information of multiple load units connected to the intelligent scheduling system through historical data analysis combined with a customized approach. The primary production-related information mentioned in this embodiment refers to the working status and / or working data of the load units. The working status of the load units refers to the operational status of multiple load units connected to the intelligent scheduling system, particularly including information on critical periods of downhole operations and / or operation time periods.

[0060] This intelligent scheduling system includes data acquisition devices corresponding to multiple load units. These devices are located at the load unit level and are used to collect real-time data from monitoring equipment such as sensors during actual production operations and / or pre-stored daily or multi-period production plans in each load unit. Examples include flow signals, speed signals, and operating times of relevant pumps.

[0061] Each data acquisition device can perform statistical analysis on the data signals it acquires. For example, statistical analysis can determine the maximum fluctuation range, median value, maximum and minimum values ​​of a certain operating parameter of the current monitoring device. Preferably, each data acquisition device can determine the primary production correlation information of the current load unit based on the statistical analysis results. The primary production correlation information can be used to characterize the operating status of the current load unit. Each data acquisition device and the fourth data processing module 104 can be wirelessly connected to transmit data such as primary production correlation information. Specifically, the intelligent scheduling system has a communication module and a storage module. The intelligent scheduling system can store the data obtained from the data acquisition devices through the communication module into the storage module for retrieval and / or modification by other data processing modules within the system.

[0062] Specifically, the fourth data processing module 104 generates secondary production association information for each group based on the primary production association information of each load unit under each group. The secondary production association information mentioned in this embodiment differs from the primary production association information in that primary production association information corresponds to different load units, while secondary production association information corresponds to a group or production association link containing at least one load unit. Preferably, the fourth data processing module 104 can determine the range of primary production association data or identify the primary production association data with the maximum value by statistically analyzing the primary production association information of each load unit under each group. The secondary production association information can be a numerical range or a single value.

[0063] The intelligent scheduling system includes a first data processing module 101, which is used to acquire real-time status data of multiple load units connected to the intelligent scheduling system. Preferably, the real-time status data can be acquired by the first data processing module 101 by retrieving stored data from the data acquisition device stored in the storage module. The real-time status data can be primary production-related information, or data different from primary production-related information. The real-time status data is mainly used to reflect the actual operating data of the load units, while the primary production-related information is mainly used to reflect the working status determined based on the actual operating data of the load units.

[0064] Preferably, the intelligent dispatching system also includes at least one power station for converting the high voltage of the high-voltage module to the low voltage of the low-voltage module to achieve intelligent power supply to the load unit. The power station can transmit data to the storage module via wired or wireless means. The power station can perform real-time monitoring of key equipment such as load units (e.g., high and low voltage switches, transformers, communication devices, etc.), operating environment (e.g., temperature and humidity), and connected secondary low-voltage switches (e.g., voltage, current, position). By acquiring the monitoring data from the power station, the actual load data can be determined.

[0065] Preferably, the power station can be, for example, a prefabricated substation. A prefabricated substation is a power transmission and distribution hub, mainly composed of a main transformer, station service transformer, feeder lines, busbars, disconnect switches, circuit breakers, voltage transformers, current transformers, grounding switches, lightning rods, etc. It encloses the high-voltage primary system (including switchgear and station service transformers) and the secondary system (including DC power supply, control, protection, and metering) and corresponding internal wiring within a prefabricated, moisture-proof, dust-proof, fire-proof, and heat-insulated steel structure enclosure, thus forming a fully enclosed, movable prefabricated switchgear system. Prefabricated substations are suitable for three-phase AC systems with a certain rated voltage and can be used for power transmission and distribution. Their working principle is to integrate high and low voltage switchgear, power metering equipment, and reactive power compensation devices into several enclosures according to specific technical wiring methods.

[0066] The first data processing module 101 establishes a load fluctuation domain for the system based on real-time status data through load fluctuation analysis. The load fluctuation analysis mentioned in this embodiment refers to the analysis process of processing historical production data and / or real-time status data using pre-stored calculation rules. Load fluctuation mainly refers to the fluctuation and uncertainty in load pressure caused by changes in key development indicators such as production, pressure, water cut, gas-oil ratio, and oil production rate during oilfield production operations.

[0067] Load fluctuation analysis can establish one or more of the following: the load fluctuation domain for the intelligent scheduling system, the load fluctuation domain for all load units, and the load fluctuation domain for all production-related links.

[0068] The load fluctuation domain established by the first data processing module 101 includes the up and down envelope curves and / or load forecast curves drawn in a virtual two-dimensional coordinate system within a sliding time window determined according to the current time. The up and down envelope curves can be used to indicate the fluctuation of the prediction error of the load forecast curve. The load fluctuation domain mentioned in this embodiment is obtained by processing in a two-dimensional graphical manner. The sliding time window mentioned in this embodiment refers to a certain time period. The time period corresponding to the sliding time window is not absolutely understood as a fixed time period, but can also be understood as a variable time period whose division length can also vary. The sliding time window can be adjusted according to different times to adapt to the load fluctuation analysis needs at different times. The sliding time window can be determined based on the current time information and pre-stored preset time windows. The time of each day is pre-divided to determine preset time windows containing different times. The preset time window into which the current time information falls can be used as the sliding time window.

[0069] If the time interval between the current time information and the end time of the preset time window is not greater than the preset minimum window building time, then based on the current time information and the minimum window building time, the current time information is used as the new window start time and extended backward by a span of the minimum window building time, thereby forming the sliding time window corresponding to the current time.

[0070] If an inspection and maintenance instruction is issued after the sliding time window is determined, a new window start time can be determined based on the actual inspection and maintenance information returned by the relevant personnel after the inspection and maintenance instruction is completed. This information is then used by the first data processing module 101 to establish a new load envelope domain.

[0071] If a scheduling optimization instruction appears after the sliding time window is determined, a new window start time can be determined based on the preset scheduling optimization duration corresponding to the scheduling optimization instruction, so that the first data processing module 101 can establish a new load envelope domain.

[0072] The virtual two-dimensional coordinate system mentioned in this embodiment has an abscissa with time, moment, or time period as the quantification index, and a ordinate with load value or load range as the quantification index. The virtual two-dimensional coordinate system can intuitively reflect data changes and the comparison between various plotted curves. Through the abscissa with the time quantification index, the first data processing module 101 can determine at least two-boundary quadrant regions in the coordinate system based on a sliding time window. The start and end times of the sliding time window correspond to two boundary lines at different positions on the abscissa, both of which are perpendicular to the abscissa. This allows for the extraction of corresponding curve segments plotted on the virtual two-dimensional coordinate system. Specifically, this refers to extracting the up and down envelope curves and / or load forecast curves plotted on the virtual two-dimensional coordinate system.

[0073] Unlike load forecast curves, which indicate the predicted load data and / or changes in the predicted load data, the up and down envelope curves can be used to indicate the fluctuations in the prediction error of the load forecast curve. Furthermore, unlike load forecast curves obtained through neural network models, the up and down envelope curves are obtained by analyzing and calculating the error between the predicted load and the corresponding actual load, and they consist of two curves: an up envelope curve and a down envelope curve.

[0074] Preferably, the load forecast curve can be obtained by the fifth data processing module 105 through predictive analysis using a neural network model based on historical data of load units connected to the intelligent dispatch system. Specifically, the fifth data processing module 105 can retrieve the historical data of the load units connected to the intelligent dispatch system to be predicted through the storage module. The fifth data processing module 105 can obtain a neural network load forecasting model by training the historical data using a neural network system. The historical data may include the historical data of the load units connected to the intelligent dispatch system and / or related historical data. The neural network load forecasting model reflects the relationship between the historical data of the load units and the related historical data.

[0075] The intelligent dispatching system also includes a second data processing module 102, used to determine the floating load difference and compare it with inspection and maintenance conditions and dispatch optimization conditions. The second data processing module 102 can generate inspection and maintenance instructions and / or dispatch optimization instructions for at least one load unit.

[0076] The first data processing module 101 transmits the load floating domain it generates or updates in real time to the second data processing module 102 for further processing and analysis.

[0077] This intelligent dispatching system allows verified users to input power dispatching demand commands containing specified load data. Verified users typically refer to load dispatching managers who input power dispatching demands into the intelligent dispatching system and receive commands through authentication.

[0078] This intelligent dispatching system can issue warnings to load dispatching managers or generate corresponding power dispatching demand instructions when the current actual load exceeds a preset load threshold.

[0079] Upon receiving a power dispatching demand instruction containing specified load data, the first data processing module 101 can determine the floating load difference between the system's load floating range and the specified load data.

[0080] Preferably, the specified load data can be a fixed value. After the specified load data is entered into the virtual two-dimensional coordinate system of the load floating domain, a dispatch demand line perpendicular to the vertical axis can be drawn on the virtual two-dimensional coordinate system to represent the specified load data, based on the uniformity of the quantification units.

[0081] Preferably, the specified load data can be multiple values ​​with a certain degree of fluctuation across different time periods. After the specified load data is entered into a virtual two-dimensional coordinate system of the load fluctuation domain, a dispatch demand curve representing the specified load data can be plotted on the virtual two-dimensional coordinate system, starting from the vertical axis, based on the uniformity of the quantification units. The specified load data is related to the stability of the power grid and the size of the power supply capacity. The load shedding index at different time periods can change successively with the power grid conditions or electricity consumption. Preferably, the specified load data can be determined by predicting and analyzing the power supply capacity that the power grid can provide for oilfield production operations. This specified load data has a certain degree of fluctuation, which is reflected in the virtual two-dimensional coordinate system as a curved dispatch demand curve, thus achieving better power efficiency.

[0082] The floating load difference mentioned in this embodiment can be obtained by inputting specified load data into a virtual two-dimensional coordinate system of the load floating domain. The sliding time window mentioned in this embodiment can be set on the virtual two-dimensional coordinate system by the first data processing module 101 or the second data processing module 102. Furthermore, the sliding time window can automatically update its corresponding position on the virtual two-dimensional coordinate system according to the passage of time.

[0083] The second data processing module 102 can synchronize the acquired actual system load data to a virtual two-dimensional coordinate system so that the second data processing module 102 can at least determine the actual system load data within the sliding time window. The second data processing module 102 calculates and processes the correlation between the load prediction curve, the uplink envelope curve, the downlink envelope curve, the actual system load curve, and the scheduling demand curve based on the virtual two-dimensional coordinate system.

[0084] The second data processing module 102 has preset inspection and maintenance conditions and / or dispatch optimization conditions. These conditions are correlated with the load forecast curve, uplink envelope curve, downlink envelope curve, actual system load curve, and dispatch demand curve calculated and processed by the second data processing module 102. When the second data processing module 102 obtains the floating load difference, it compares it with the inspection and maintenance conditions and / or dispatch optimization conditions.

[0085] When the floating load difference triggers the inspection and maintenance condition, the second data processing module 102 generates an inspection and maintenance instruction for at least one load unit to instruct the inspection and maintenance work for at least one load unit. When the floating load difference triggers the allocation optimization condition, the second data processing module 102 generates an allocation optimization instruction for the operating status of at least one load unit to instruct the adjustment of the operating status of at least one load unit.

[0086] Preferably, the load fluctuation range mentioned in this embodiment can be the load fluctuation range within the sliding time window, which is obtained by calculating the upper and lower limits of the load fluctuation range based on the load prediction curve at the current time and within the sliding time window, using the historical load data of each load unit and the relevant data of its corresponding load prediction curve segment as input.

[0087] Preferably, matrix K' is formed by standardizing the historical load data of each load unit into a sequence, and matrix K'' is formed by standardizing the relevant data of the load forecast curve segments corresponding to the historical load data of each load unit into a sequence. Based on matrix K' and matrix K'', the floating power sequence Z i (t)=K””t)-K'(t)(t=t0-T P +1,...,t0-1,t0). Where T P The time length of the historical load data for each selected load unit.

[0088] For a single load unit, the following formula can be used to comprehensively consider the floating power range / floating load range {E'(x), E""x)} of the single load unit. Wherein, the upper limit of the load floating range {E'(x), E""x)} is... Lower limit of load fluctuation range

[0089] Among them, Z i (t',x) is the floating power sequence with time scale x in the i-th load unit, and E' and E” are the upper and lower limits of the floating range of the i-th (1≤i≤1) load.

[0090] Where Pr(...) represents the probability of the event occurring.

[0091] Where X is the sliding time window and t0 is the current time.

[0092] For a single production-related link, the following calculation formula can be used to comprehensively consider the floating power range / floating load range of a single production-related link {U'(x), U""x")} (x∈X), where the upper limit of the load floating range of the range {U'(x), U""x") is U'(x) = max{Z i (t0)+E'(x),Z i (t0+x)+E'(x)}, the lower limit of the load fluctuation range U””x)=max{Z i (t0)+E””,Z i (t0+x)+E"(x)}.

[0093] Based on {U'(x), U""x)} (x∈X) or {E'(x), E""x)}, a multidimensional multicell can be obtained using the Cartesian product. Then, by summing and projecting the multidimensional multicell, {H'(t0,x), H""t0,x)} can be obtained. Based on this, the corresponding up and down envelope curves can be obtained based on a determined sliding time window.

[0094] For a single production-related link, among its multiple load units, there is a first sub-load unit whose load impact on the power grid is synchronized with that of the designated load unit. It also has a second sub-load unit whose load impact on the power grid is asynchronous with that of the designated load unit. Furthermore, it has a third sub-load unit whose load impact on the power grid is less volatile. However, because oilfield production systems involve a large number of load units, manual statistical calculations involve a significant amount of computation and are prone to errors. Moreover, there are issues with untimely information updates, making it impossible to accurately determine the sub-load units under different attributes, thus affecting the need for efficient power dispatch. In addition, during oilfield production operations, key development indicators such as production, pressure, water cut, gas-oil ratio, and oil production rate will change, meaning their load impact on the power grid cannot be represented by a fixed value. Conventional techniques typically use predictive analysis based on historical data to predict load impact; however, such predicted loads often have a certain error compared to the actual load. The magnitude of this error is difficult to assess and is subject to random load fluctuations. When the load impacts of multiple different production-related links are superimposed, the error is further amplified.

[0095] To address the inefficiency of manual statistical calculations and the high error rates of conventional forecasting methods, and to ensure reliable load dispatching during oilfield production operations to meet emergency power rationing demands, the IoT-based intelligent dispatching system proposed in this embodiment further includes a load envelope calculation module 106. The load envelope calculation module 106 can be one of the aforementioned data processing modules, or it can be other modules that cooperate with the aforementioned data processing modules to perform calculations and analyses.

[0096] The load envelope calculation module 106 determines a sliding time window based on the current time and establishes a load floating domain for each load unit within the sliding time window based on load data for that load unit. Based on a specified load unit and its corresponding load floating domain, the load envelope calculation module 106 can classify and assign it to each associated load unit. Upon receiving a power dispatching demand instruction containing specified load data, the load envelope calculation module 106 can generate a dispatching optimization instruction regarding the operating status of each load unit based on the floating load difference between the specified load data and the load floating domains corresponding to load units under different categories.

[0097] The load envelope calculation module 106 classifies and assigns the specified load unit and its associated load units. This can mean that for a single production-related link, among the multiple load units it contains, there are: a first sub-load unit that is synchronized with the load impact of the specified load unit on the power grid; a second sub-load unit that is asynchronous with the load impact of the specified load unit on the power grid; and a third sub-load unit whose load impact on the power grid has smaller fluctuations.

[0098] The load envelope calculation module 106 can classify each load unit based on a specified load unit, its corresponding load floating domain, and the load floating domains of other load units. Preferably, by matching the changing trends of the load floating domains of different load units with the specified load unit, a first sub-load unit whose load floating domain has a similar changing trend to the load floating domain of the specified load unit, a second sub-load unit whose load floating domain has a weak correlation with the load floating domain of the specified load unit, and a third sub-load unit whose load floating domain tends to be constant.

[0099] The intelligent scheduling system also includes a scheduling module 107. Upon receiving a dispatch optimization instruction for a specific production-related link, the scheduling module 107 performs tiered load scheduling on the multiple load units included in that production-related link. The tiered load scheduling includes first-level load scheduling, which instructs a designated load unit and / or at least one first sub-load unit to gradually reduce its operating power to respond to overall load scheduling needs in a timely manner. The tiered load scheduling also includes second-level load scheduling, which, after first-level load scheduling, calculates and analyzes the dispatch optimization instruction. If the allowable load under the dispatch optimization instruction is insufficient to support the designated load unit and the first sub-load unit in maintaining basic operational production, the system reduces the load to the point of shutting down the designated load unit and / or at least one first sub-load unit. Based on the allowable load under the current sliding time window, while ensuring the load corresponding to the operational production of the third sub-load unit, the system instructs at least one second sub-load unit to perform load migration to move production tasks after the current sliding time window to the current sliding time window for operation. The third sub-load unit can be equipment such as a security system or an anti-theft system. If the allowable load under the allocation optimization instruction is sufficient to support the basic operation production of the specified load unit and the first sub-load unit, the operation production of the specified load unit and / or at least one first sub-load unit shall be gradually restored, and at least one second sub-load unit shall be instructed to perform load migration to postpone the production operation tasks of the current sliding time window to be performed after the current sliding time window.

[0100] It should be noted that the specific embodiments described above are exemplary. Those skilled in the art can devise various solutions inspired by the disclosure of this invention, and these solutions all fall within the scope of this invention and its protection. Those skilled in the art should understand that this specification and its accompanying drawings are illustrative and do not constitute a limitation on the claims. The scope of protection of this invention is defined by the claims and their equivalents. This specification contains multiple inventive concepts; terms such as "preferredly," "according to a preferred embodiment," or "optionally" indicate that the corresponding paragraph discloses an independent concept. The applicant reserves the right to file divisional applications based on each inventive concept. Throughout the text, features introduced by "preferredly" are merely optional and should not be construed as mandatory. Therefore, the applicant reserves the right to abandon or delete relevant preferred features at any time.

Claims

1. An intelligent scheduling system based on the Internet of Things, characterized in that, At least including: The first data processing module is used to acquire real-time status data of multiple load units connected to the intelligent scheduling system in response to operator input or automatic acquisition. The second data processing module is used to create a corresponding real-time data mapping model based on real-time status data, and compare the real-time data mapping model with the pre-stored status model to realize real-time monitoring of the power Internet of Things. The second data processing module generates or retrieves instruction information related to at least one load unit from the database based on the comparison results, and displays this instruction information to the host computer via a display interface for operator scheduling and monitoring. The second data processing module updates the instruction information based on the feedback information received from the operator via the host computer. It then actively follows the operator's actions on the host computer based on the updated instruction information and compares the updated information with the new instructions, thus achieving two-way human-machine supervision. The first data processing module is used to acquire real-time status data of multiple load units connected to the intelligent scheduling system and establish a load fluctuation domain for the system based on the real-time status data through load fluctuation analysis. The second data processing module generates or retrieves instruction information about at least one load unit from the database according to the load fluctuation domain. The load fluctuation domain established by the first data processing module is included in the up and down envelope curves and / or load prediction curves drawn in the virtual two-dimensional coordinate system within a sliding time window determined according to the current time. The up and down envelope curves are used to indicate the prediction error fluctuation of the load prediction curve.

2. The intelligent scheduling system according to claim 1, characterized in that, The first data processing module collects historical and monitoring data related to multiple load units, and judges the status of multiple load units and the system status through historical data analysis, so as to realize real-time monitoring of the operating parameters and actual working conditions of multiple load units and the system, and uses the Internet of Things to interconnect the monitoring information generated after its preprocessing.

3. The intelligent scheduling system according to claim 2, characterized in that, The second data processing module actively handles abnormal operating conditions of multiple load units and / or the system based on the monitoring information generated after the first data processing module processes the collected data, and notifies the operator through the display interface so that the operator can perform timely scheduling and monitoring.

4. The intelligent scheduling system according to claim 3, characterized in that, The intelligent scheduling system also includes a third data processing module, which is used to group multiple load units connected to the intelligent scheduling system into corresponding groups by assigning numbers to each unit.

5. The intelligent scheduling system according to claim 4, characterized in that, The intelligent scheduling system also includes a fourth data processing module, which is used to obtain primary production association information of multiple load units connected to the intelligent scheduling system by analyzing historical data and combining it with the basic information of the equipment's full life cycle ledger, and to generate secondary production association information of each group based on the primary production association information of each load unit under each group.

6. The intelligent scheduling system according to claim 1, characterized in that, Upon receiving a power dispatching demand instruction containing specified load data, the second data processing module determines the floating load difference between the system's load floating domain and the specified load data, and generates an inspection and maintenance instruction for at least one load unit when the floating load difference triggers inspection and maintenance conditions, or generates a dispatching optimization instruction for the operating status of at least one load unit when the floating load difference triggers dispatching optimization conditions.

7. A load scheduling method using the Internet of Things-based intelligent scheduling system as described in any one of claims 1 to 6, characterized in that, At least including: The real-time status data of multiple load units connected to the intelligent scheduling system are acquired using the first data processing module in response to operator input or automatic acquisition. The second data processing module is used to create a corresponding real-time data mapping model based on real-time status data. The second data processing module is used to compare the real-time data mapping model with the pre-storage status model to achieve real-time monitoring of the power Internet of Things. Through the above comparison, instruction information about at least one load unit associated with the comparison result is generated or retrieved from the database; The received instruction information is displayed on the host computer via a display interface for the operator to schedule and monitor. Based on the feedback information received from the operator via the host computer, the instruction information is updated using the second data processing module; Based on the updated instruction information, the second data processing module actively follows the operator's operation information on the host computer. By comparing the operational information with the updated instruction information, two-way supervision between humans and machines can be achieved.

8. An intelligent scheduling system based on the Internet of Things, characterized in that, At least including: The first data processing module is used to acquire real-time status data of multiple load units connected to the intelligent scheduling system and establish a load fluctuation domain of the system based on the real-time status data through load fluctuation analysis. The second data processing module is used to determine the floating load difference between the system's load floating domain and the specified load data when receiving a power dispatch demand instruction containing specified load data, and to generate an inspection and maintenance instruction for at least one load unit when the floating load difference triggers an inspection and maintenance condition, or to generate a dispatch optimization instruction for the operating status of at least one load unit when the floating load difference triggers a dispatch optimization condition. The first data processing module is used to acquire real-time status data of multiple load units connected to the intelligent scheduling system and establish a load fluctuation domain for the system based on the real-time status data through load fluctuation analysis. The second data processing module generates or retrieves instruction information about at least one load unit from the database according to the load fluctuation domain. The load fluctuation domain established by the first data processing module is included in the up and down envelope curves and / or load prediction curves drawn in the virtual two-dimensional coordinate system within a sliding time window determined according to the current time. The up and down envelope curves are used to indicate the prediction error fluctuation of the load prediction curve.

9. An intelligent scheduling system based on the Internet of Things, characterized in that, The intelligent scheduling system includes at least a load envelope calculation module, which is configured as follows: A sliding time window is determined based on the current moment, and a load fluctuation domain for each load unit is established within the sliding time window based on load data for each load unit. Based on a specified load unit and its corresponding load floating domain, the load unit and its associated load units can be categorized and assigned. This allows for the generation of a dispatch optimization instruction regarding the operating status of at least one load unit when a power dispatch demand instruction containing specified load data is received. This instruction is based on the floating load difference between the specified load data and the load floating domains corresponding to load units under different categories. The load floating domain is the load floating domain within the sliding time window, calculated by taking the historical load data of each load unit and the relevant data of its corresponding load forecast curve segment as input, and calculating the upper and lower limits of the load fluctuation range.

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