A power dispatching processing method and system based on multi-task collaboration

Abstract

This invention belongs to the field of power dispatching technology and provides a power dispatching processing method and system based on multi-task collaboration. To address the problem of low accuracy in traditional power dispatching technologies, the method determines several preset power service tasks, then identifies the target power network corresponding to each preset power service task, and determines whether the target power network contains faulty nodes. If the target power network does not contain faulty nodes, the method determines the historical power load data and power load benchmark of the target power network, and determines the adjustment range of the target power network's power load. Then, based on the power load benchmark and adjustment range, the method predicts the expected load corresponding to the target power network. Finally, based on all the expected loads corresponding to the several preset power service tasks, the method determines the target load corresponding to power dispatching. This improves the accuracy of power dispatching and enhances the overall utilization rate of power resources in complex and ever-changing power demand environments.

Description

Technical Field

[0001] This invention relates to the field of power dispatching technology, and in particular to a power dispatching processing method and system based on multi-task collaboration. Background Technology

[0002] Power dispatching involves monitoring the power grid's operation in real time through a monitoring system, including the generation status of generator units, load demand, and the operational status of transmission lines. Based on the actual situation of the power grid, it promptly adjusts the output of generator units or operates equipment within the grid to ensure system stability. The main tasks of power dispatching include generation planning and optimized dispatching, load forecasting and management, real-time operation control, safety analysis and fault handling, coordination of new energy sources and energy storage, emergency and disaster response, and balancing environmental protection and economic interests. Power dispatching's multiple tasks cover the entire power business chain, from forecasting and planning to real-time control, requiring dynamic trade-offs among multiple objectives to ensure the continuous, safe, and stable operation of the power grid. Power dispatching is a core link in power system operation, and its multi-task collaborative work ensures the safe, stable, economical, and environmentally friendly operation of the power grid.

[0003] With the continuous increase in electricity demand and the growing complexity of power systems, power dispatch and management have become increasingly important. Traditional technologies typically rely on historical data from the user side to predict electricity demand and then dispatch power based on this prediction. However, directly using historical data for demand prediction often lacks effective dispatch strategies. For example, residential electricity demand changes during holidays compared to normal times, as does commercial demand. Furthermore, long-term and short-term faults in the power network also affect demand; for instance, long-term faults caused by construction have different impacts on demand than short-term faults caused by unexpected circuits. These complex and variable electricity demands can easily lead to wasted power resources or insufficient supply, hindering efficient coordination and dispatch, and resulting in low power resource utilization. Therefore, traditional power dispatch methods are often ill-suited to addressing complex and changing electricity demand and load conditions.

[0004] Therefore, improving the accuracy of power dispatch has become an urgent problem to be solved. Summary of the Invention

[0005] The technical problem solved by this invention is: how to improve the accuracy of power dispatching.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: determining a plurality of preset power service tasks; determining the target power network corresponding to the preset power service tasks, and determining whether the target power network contains faulty nodes; if the above determination is negative, determining a historical power load data sequence of the target power network, wherein the historical power load data sequence contains historical power load data; determining a power load benchmark for the target power network, and determining an adjustment range for the power load of the target power network based on the historical power load data and the power load benchmark; predicting the load of the target power network based on the power load benchmark and the adjustment range to obtain the expected load of the preset power service tasks; determining the target load for power dispatch based on all the expected loads; and performing power dispatch based on the target load.

[0007] As a preferred embodiment of the multi-task collaborative power dispatching processing described in this invention, determining the adjustment range of the power load of the target power network based on the historical power load data and the power load benchmark includes: determining the change ratio of the historical power load of the target power network relative to the power load benchmark based on the historical power load data and the power load benchmark; determining the minimum and maximum ratio values ​​included in all the change ratios, and forming a ratio range by combining the minimum and maximum ratio values ​​to obtain an adjustment ratio range; and determining the adjustment range of the power load of the target power network based on the adjustment ratio range.

[0008] The beneficial effects of this invention are as follows: By determining several preset power business tasks, then determining the target power network corresponding to the preset power business tasks, and judging whether the target power network contains faulty nodes, if the target power network does not contain faulty nodes, determining several historical power load data corresponding to the target power network, and determining the power load benchmark corresponding to the target power network, and determining the adjustment range of the power load corresponding to the target power network based on the historical power load data and the power load benchmark, then predicting the expected load corresponding to the target power network based on the power load benchmark and the adjustment range, and finally determining the target load corresponding to power dispatch based on all the expected loads corresponding to the several preset power business tasks, this invention not only achieves automation and accuracy of power load forecasting based on the collaboration of multiple power business tasks, but also realizes automated processing of power dispatching with multi-task collaboration of power load forecasting, grid faults, generation planning and optimized dispatching, in order to cope with complex and ever-changing power demand and load conditions, improve the accuracy of power dispatching, and achieve reasonable dispatching and optimization of power resources in a complex and ever-changing power demand environment, thereby improving the overall utilization rate of power resources. Attached Figure Description

[0009] Figure 1 A flowchart illustrating the power dispatching processing method based on multi-task collaboration provided in an embodiment of the present invention;

[0010] Figure 2 A schematic diagram of the multi-task collaboration relationship in the power dispatching processing method based on multi-task collaboration provided in an embodiment of the present invention;

[0011] Figure 3 This is a schematic diagram of the first sub-process of the power dispatching method based on multi-task collaboration provided in an embodiment of the present invention;

[0012] Figure 4 This is a schematic diagram of the second sub-process of the power dispatching processing method based on multi-task collaboration provided in an embodiment of the present invention;

[0013] Figure 5 This is a schematic diagram of the third sub-process of the power dispatching method based on multi-task collaboration provided in an embodiment of the present invention;

[0014] Figure 6 This is a schematic block diagram of a power dispatching and processing system based on multi-task collaboration, provided in an embodiment of the present invention. Detailed Implementation

[0015] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0016] This invention provides a power dispatching processing method based on multi-task collaboration. The method can be applied to devices including but not limited to computers, and is used in power dispatching, among other things.

[0017] To address the issue of low accuracy in traditional power dispatching technologies, the inventors propose a multi-task collaborative power dispatching method according to embodiments of the present invention. The core idea of ​​this invention is as follows: Power business tasks are pre-divided to determine n power business tasks, where n is a natural number and n≥2. When there are no grid faults within the business area covered by each power business task, the expected load corresponding to each power business task is predicted based on historical load data. Then, a power dispatching plan is generated based on all expected loads. This not only achieves automated power load prediction based on the collaboration of multiple power business tasks, but also realizes automated power dispatching processing through multi-task collaboration of power load prediction, grid fault detection, generation planning, and optimized dispatching. This addresses complex and ever-changing power demand and load conditions, improves the accuracy of power dispatching, and enables rational dispatching and optimization of power resources in complex and ever-changing power demand environments, thereby improving the overall utilization rate of power resources.

[0018] Example 1, please refer to Figure 1 and Figure 2 , Figure 1 This is a flowchart illustrating the power dispatching processing method based on multi-task collaboration provided in an embodiment of the present invention. Figure 2 This diagram illustrates the multi-task collaboration relationship in the power dispatching processing method based on multi-task collaboration provided in an embodiment of the present invention. Figure 1 As shown, in this embodiment, the method includes, but is not limited to, the following steps S11-S18:

[0019] S11. Determine several pre-set power business tasks.

[0020] Interpretationally, a number of preset power service tasks are determined by responding to the preset start command corresponding to the power dispatch. Thus, the start command is usually set in advance, that is, the preset start command. The preset start command represents the command of the relevant computer system of power dispatch to start power dispatch. The preset start command can be the command corresponding to the user's start operation, or it can be the command corresponding to the timer or event trigger.

[0021] Based on the granularity of electricity service attributes, including but not limited to geographical location, commercial, residential, and public facility electricity consumption, the electricity usage attributes within a preset scope are pre-divided into different electricity service tasks, resulting in several preset electricity service tasks, which can be represented as n preset electricity service tasks, where n is a natural number and n≥2. A preset electricity service task represents a pre-divided electricity task involving the use of electricity for a specific business activity. For example, within a certain urban area, the electricity usage of that area can be divided into three preset electricity service tasks: industrial production electricity consumption, residential electricity consumption, and public facility electricity consumption. These three preset electricity service tasks represent different types or natures of electricity consumption activities.

[0022] Based on the above concept and setup, when conducting power dispatch, several preset power service tasks are generally determined in response to the preset start command corresponding to the power dispatch.

[0023] S12. Determine the target power network corresponding to the preset power service task, and determine whether the target power network contains faulty nodes.

[0024] For an explanatory explanation, please refer to [link / reference]. Figure 2 As mentioned above, different preset power service tasks correspond to different target power networks. That is, each preset power service task is associated with a corresponding target power network, forming a corresponding relationship pair. For example, preset power service task 1 corresponds to target power network 1, preset power service task 2 corresponds to target power network 2, and preset power service task 3 corresponds to target power network 3.

[0025] As described above, when conducting power dispatch, several preset power service tasks are first determined. Generally, n preset power service tasks are determined first. Then, the target power network corresponding to each preset power service task is determined, and it is determined whether the target power network contains fault nodes. Fault nodes refer to nodes in the power network that have experienced faults. Fault nodes generally affect power consumption within a certain range. Correspondingly, depending on the duration of the fault node's fault, it will also have a corresponding impact on the power load of the target power network. For example, fault nodes caused by construction may affect power consumption for several days to more than ten days or even longer, while sudden accidental fault nodes may affect power consumption for several hours to a day.

[0026] S13. If the target power network contains faulty nodes, the historical power load data sequence corresponding to the target power network is uncertain.

[0027] S14. If the target power network does not contain faulty nodes, determine the historical power load data sequence of the target power network, wherein the historical power load data sequence contains historical power load data.

[0028] Explained, a time period is generally set in advance, that is, a preset time period. The preset time period represents the time period for power dispatch. The preset time period includes, but is not limited to, time periods with years, months, days, and hours as time units.

[0029] Based on the above settings, when the target power network contains faulty nodes (i.e., the above judgment is true, meaning the target power network is in an abnormal power consumption state), the historical power load data sequence corresponding to the target power network is uncertain. Conversely, when the target power network does not contain faulty nodes (i.e., the above judgment is false, meaning the target power network is in normal power consumption state), the power load of the target power network exhibits normal regularity and stability. Therefore, the historical power load data sequence corresponding to the target power network is determined. This historical power load data sequence contains several historical power load data points based on the same preset time period, and represents a sequence composed of these historical power load data points arranged in a corresponding chronological order. For example, assuming the preset time period is December of the current year, the historical power load data sequence includes the historical power load data for December of each year from year 1, year 2, ..., year m in the history of electricity consumption. The historical power load data represents the actual power load of the target power network in December of each year from year 1, year 2, ..., year m, and the historical power load data sequence represents the temporal longitudinal relationship of the actual power load in December of each year from year 1, year 2, ..., year m.

[0030] S15. Determine the power load benchmark of the target power network, and determine the adjustment range of the power load of the target power network based on the historical power load data and the power load benchmark.

[0031] Explained, the power load benchmark represents the load reference standard for predicting power load, and the adjustment range represents the magnitude of the change in power load based on the power load benchmark. The adjustment range can be a proportional value or a quantitative value.

[0032] Based on the above concept and setup, the power load benchmark corresponding to the target power network is determined. This benchmark can be an artificially set power load reference standard, or it can be a power load reference standard obtained through statistical analysis of historical power load data of the target power network. Based on historical power load data and the power load benchmark, and further through statistical analysis of several historical power load data points and the power load benchmark, the adjustment range of the power load corresponding to the target power network is determined. Thus, the adjustment range is related to both historical power load data and the power load benchmark. By leveraging the stability and regularity of the power load carried by the target power network corresponding to the preset power service tasks, as reflected in several historical power load data points, the adjustment range of the power load corresponding to the target power network can be determined. Since the adjustment range reflects the regularity and stability corresponding to the magnitude of changes in historical power load data, the expected load corresponding to the preset power service tasks can be predicted relatively accurately, thereby improving the accuracy of the expected load prediction for the preset power service tasks.

[0033] S16. Based on the power load benchmark and the adjustment range, predict the load of the target power network to obtain the expected load of the preset power service task.

[0034] Explained as described above, based on the aforementioned power load benchmark, the power load benchmark is adjusted according to the aforementioned adjustment range to predict the expected load corresponding to the target power network. That is, the load corresponding to the target power network in a future preset time period is predicted to obtain the expected load corresponding to the preset power business task. By leveraging the stability and regularity of the past actual power load carried by the target power network corresponding to the preset power business task as reflected by several historical power load data, the expected load corresponding to the preset power business task can be predicted relatively accurately, thereby improving the accuracy of the expected load prediction for the preset power business task.

[0035] S17. Determine the target load for the power dispatch based on all the expected loads.

[0036] Explained, for each preset power service task, the corresponding expected load is predicted according to the above description. For the above-determined number (e.g., n) preset power service tasks, n corresponding expected loads are obtained. Therefore, based on all the expected loads corresponding to the n preset power service tasks, all expected loads are combined, for example, including but not limited to directly adding all expected loads, to determine the target load corresponding to power dispatch. The target load is the total power required for the predicted power dispatch.

[0037] S18. Perform power dispatching based on the target load.

[0038] Explained as above, power dispatching plans or programs are generally generated based on the target load, and power dispatching is carried out by executing the power dispatching plans.

[0039] In this embodiment of the invention, several preset power service tasks are determined, then the target power network corresponding to each preset power service task is determined, and it is determined whether the target power network contains faulty nodes. If the target power network does not contain faulty nodes, a historical power load data sequence corresponding to the target power network is determined, wherein the historical power load data sequence contains several historical power load data based on the same preset time period. Then, a power load benchmark corresponding to the target power network is determined, and based on the historical power load data and the power load benchmark, the adjustment range of the power load corresponding to the target power network is determined. Based on the power load benchmark and the adjustment range, the expected load corresponding to the target power network is predicted, thus obtaining the preset power service tasks. The system determines the target load for power dispatch based on the expected load corresponding to the power business tasks and all the expected loads corresponding to several preset power business tasks. Based on the target load, a power dispatch plan is generated and executed to carry out power dispatch. This not only automates and improves the accuracy of power load forecasting based on the collaboration of multiple power business tasks, but also enables automated processing of power dispatching that coordinates multiple tasks such as power load forecasting, grid faults, generation planning, and optimized dispatching. This is to cope with complex and ever-changing power demand and load conditions, improve the accuracy of power dispatching, and enable the rational dispatching and optimization of power resources in a complex and ever-changing power demand environment, thereby improving the overall utilization rate of power resources.

[0040] In one embodiment, determining the power load reference of the target power network includes:

[0041] Calculate the mean of all the historical power load data, and use the mean as the power load benchmark corresponding to the target power network;

[0042] Alternatively, all the historical power load data can be sorted in ascending order, and the historical power load data with the smallest value can be used as the power load benchmark corresponding to the target power network.

[0043] Explained, in one embodiment, the mean of all historical power load data is calculated, which is the median. This mean is used as the power load benchmark for the target power network. The historical power load data can be obtained by varying the mean upwards or downwards. Based on the stability and regularity of the past actual power load carried by the target power network corresponding to the preset power service tasks reflected by several historical power load data, the actual power load corresponding to the future target power network should also vary around the mean. Therefore, the predicted expected load fluctuating around the mean will be more consistent with the future actual power load of the target power network. Thus, the predicted expected load is accurate, thereby improving the accuracy of power dispatch.

[0044] In another embodiment, all historical power load data are sorted in ascending order, and the historical power load data with the smallest value is used as the power load benchmark corresponding to the target power network. The historical power load data can be obtained by moving the power load benchmark upward. Based on the stability and regularity of the past actual power load carried by the target power network corresponding to the preset power business tasks reflected by several historical power load data, the actual power load corresponding to the future target power network should also move upward based on the smallest historical power load data. Therefore, the predicted expected load moving upward based on the smallest historical power load data is more consistent with the future actual power load situation of the target power network. Thus, the predicted expected load has accuracy, thereby improving the accuracy of power dispatch.

[0045] In this embodiment of the invention, for each preset power service task, a corresponding power load benchmark based on historical power load data is determined. Then, based on the power load benchmark, the expected load corresponding to each preset power service task is predicted. This improves the accuracy of the expected load prediction. By improving the accuracy of the expected load prediction for each preset power service task, the accuracy of the target load corresponding to power dispatch is further improved. This addresses the power demand and load conditions under complex and ever-changing environments, thereby improving the accuracy of power dispatch. In complex and ever-changing power demand environments, this enables the rational dispatch and optimization of power resources, improving the overall utilization rate of power resources.

[0046] In one embodiment, determining the adjustment range of the power load of the target power network based on the historical power load data and the power load benchmark includes:

[0047] Based on the historical power load data and the power load benchmark, determine the percentage change in the historical power load of the target power network relative to the power load benchmark;

[0048] Determine the minimum and maximum ratio values ​​included in all the aforementioned variation ratios, and combine the minimum and maximum ratio values ​​to form a ratio range, thereby obtaining the adjustment ratio range;

[0049] Based on the aforementioned adjustment ratio range, the adjustment range of the power load of the target power network is determined.

[0050] Explained as described above, the difference between historical power load data and the power load benchmark is calculated to obtain the magnitude of the change in historical power load data relative to the power load benchmark. This difference is then divided by the power load benchmark to obtain the percentage change in historical power load data relative to the power load benchmark. This percentage change in the target power network's historical power load relative to the power load benchmark is used to determine the percentage change in the target power network's historical power load relative to the power load benchmark. Thus, for several sets of historical power load data, several sets of percentage changes are obtained. The minimum and maximum percentage values ​​included in all percentage changes are then determined, and these minimum and maximum percentage values ​​form a percentage range, resulting in an adjustment percentage range. The adjustment percentage range represents the percentage change in several historical power load data relative to the power load benchmark in proportional form. Similarly, the target power network carrying the preset power service tasks reflected by several historical power load data... Given the stability, regularity, and continuity of past actual power loads, the range of fluctuations in the actual power load corresponding to the future target power network should also fall within the aforementioned adjustment ratio range. Based on this, the adjustment range for the power load corresponding to the target power network is determined according to the aforementioned adjustment ratio range. The adjustment range can be the median of the aforementioned adjustment ratio range or a logically appropriate ratio value within it. Thus, by using historical power load data to demonstrate the stability, regularity, and continuity of the past actual power load carried by the target power network corresponding to the preset power business tasks, the predicted load based on the adjustment range will be more consistent with the future actual power load situation of the target power network. Therefore, the predicted load is accurate, thereby improving the accuracy of power dispatch.

[0051] This invention, in its embodiments, determines the percentage change in the historical power load of a target power network relative to the power load benchmark based on historical power load data and a power load benchmark. It then identifies the minimum and maximum percentage values ​​included in all percentage changes, and combines these minimum and maximum percentage values ​​to form a percentage range, thus obtaining an adjustment percentage range. Based on this adjustment percentage range, it determines the adjustment magnitude of the power load corresponding to the target power network. This allows for the determination of a unique adjustment magnitude for each power service task based on its historical actual load, i.e., the historical power load data corresponding to the power service task. Since the adjustment magnitude reflects the personalized characteristics of each power service task, it can predict the expected load corresponding to the preset power service task, improving the accuracy of expected load prediction. This, in turn, improves the accuracy of target load prediction for power dispatch, thereby enhancing the accuracy of power dispatch. This enables the rational scheduling and optimization of power resources in complex and ever-changing power demand environments, improving the overall utilization rate of power resources.

[0052] In one embodiment, please refer to Figure 3 , Figure 3 This is a schematic diagram of the first sub-process of the power dispatching method based on multi-task collaboration provided in an embodiment of the present invention. Figure 3 As shown, in this embodiment, determining the adjustment range of the power load of the target power network according to the adjustment ratio range includes:

[0053] S31. Calculate the mean of all the historical power load data;

[0054] S32. Based on all the historical power load data, count the number of historical power load data that are greater than the average value to obtain a first quantity value, and count the number of historical power load data that are less than the average value to obtain a second quantity value;

[0055] S33. Calculate the ratio of the first quantity value to the second quantity value;

[0056] S34. Calculate the median value of the ratio corresponding to the adjustment ratio range, and adjust the median value of the ratio according to the ratio to obtain the adjustment range of the power load corresponding to the target power network.

[0057] Explainingly, this method also leverages historical power load data to demonstrate the stability, regularity, and continuity of the past actual power load carried by the target power network corresponding to the preset power service task. To more fully utilize and reflect the probability of future power load occurrences based on the past actual power load carried by the target power network corresponding to the preset power service task, the mean of all historical power load data is calculated. Using the mean as a dividing line, the number of historical power load data points exceeding the mean is counted, yielding a first value. The number of historical power load data points below the mean is counted, yielding a second value. The ratio of the first value to the second value is calculated. This ratio represents the relative probability of historical power load data points exceeding or falling below the mean occurring. Based on this ratio, the likelihood of future power loads carried by the target power network corresponding to the preset power service task exceeding or falling below the mean can be predicted. The median of the adjustment range is then calculated. Based on the median, and then the ratio, i.e., based on the probability of future power loads exceeding or falling below the mean for the preset power service task, is further calculated. The adjustment range is calculated by adjusting the median ratio based on the probability of the future power load carried by the target power network being greater or less than the average. This adjustment range represents the magnitude of the change in power load based on the power load benchmark. By leveraging the stability, regularity, and continuity of the past actual power load carried by the target power network corresponding to the preset power service task, as reflected in several historical power load data, the probability of changes in the future power load carried by the target power network corresponding to the preset power service task can be predicted. By using the aforementioned average and median, different occurrence scenarios and probabilities of the past actual power load carried by the target power network corresponding to the preset power service task are fully considered. That is, the probability of the load being greater or less than the average is considered on the basis of the average. Therefore, by leveraging the stability and regularity of the power load carried by the target power network corresponding to the preset power service task, as reflected in several historical power load data, the expected load corresponding to the preset power service task can be predicted relatively accurately, reducing prediction errors and improving the accuracy of the expected load prediction for the preset power service task.

[0058] Furthermore, the median of the ratio is adjusted based on the ratio, including at least one of the following:

[0059] If the ratio is greater than 1, the median of the ratio is increased by the ratio corresponding to the ratio.

[0060] If the ratio is less than 1, the median of the ratio is reduced by the ratio corresponding to that ratio.

[0061] When the ratio is equal to 1, the magnitude of the adjustment of the median of the ratio is set to zero.

[0062] Specifically, based on the above, the median ratio is adjusted according to the proportion corresponding to the ratio, that is, the median ratio is increased or decreased by the proportion corresponding to the ratio. Specifically: 1) When the ratio is greater than 1, it indicates that the probability of the target power network corresponding to the preset power business task having a future power load "greater than the above average" is greater than the probability of it being "less than the above average". In other words, the probability of the target power network corresponding to the preset power business task having a future power load greater than the average is relatively high. Therefore, the median ratio is increased by the proportion corresponding to the ratio, that is, "adjustment range = median ratio * (1 + ratio)"; 2) Similarly, when the ratio is less than 1, it indicates that the probability of the target power network corresponding to the preset power business task having a future power load "less than the above average" is greater than the probability of it being "greater than the above average". In other words, the probability of the target power network corresponding to the preset power business task having a future power load greater than the average is relatively high. The probability that the future power load carried by the network is less than the average is relatively high. Therefore, the median of the above ratio is reduced by the corresponding ratio, that is, "adjustment range = median of ratio * (1 - ratio)"; 3) When the above ratio is equal to 1, it indicates that the probability of the future power load carried by the target power network corresponding to the preset power business task being "greater than the above average" is the same as the probability of being "less than the above average". That is, the probability that the future power load carried by the target power network corresponding to the preset power business task is equal to the average is relatively high. Therefore, the adjustment of the median of the above ratio is set to 0, that is, zero, that is, "adjustment range = median of ratio". By using the probability of the occurrence of the past actual power load carried by the target power network corresponding to the preset power business task in the future power load reflected by a number of historical power load data, the predicted expected load is constrained and converged, which can improve the accuracy of the expected load prediction.

[0063] In this embodiment of the invention, the average of all historical power load data is calculated. Based on this, the number of historical power load data points exceeding the average is counted to obtain a first value, and the number of historical power load data points below the average is counted to obtain a second value. The ratio of the first value to the second value is calculated, and the median of the adjustment range is calculated. Based on the ratio, the median is adjusted accordingly to obtain the adjustment range of the power load corresponding to the target power network. This allows for the prediction of the expected load size and average within the same preset time period by comparing several historical power load data points with their corresponding average values ​​within the same preset time period. The probability of the predicted load being greater than, less than, or equal to the mean is compared to the average. By leveraging the stability and regularity of the target power network load corresponding to the preset power service tasks reflected in several historical power load data, the expected load corresponding to the preset power service tasks can be predicted relatively accurately. This can improve the accuracy of the predicted load for the preset power service tasks and further improve the accuracy of the target load corresponding to the power dispatching as a whole. This improves the accuracy of power dispatching and enables the rational dispatching and optimization of power resources in a complex and ever-changing power demand environment, thereby improving the overall utilization rate of power resources.

[0064] In one embodiment, please refer to Figure 4 , Figure 4 This is a schematic diagram of the second sub-process of the power dispatching method based on multi-task collaboration provided in an embodiment of the present invention. For example... Figure 4 As shown, in this embodiment, the method further includes:

[0065] S41. If the target power network contains a faulty node, determine the fault occurrence time corresponding to the faulty node;

[0066] S42. Obtain the current time and calculate the time interval between the current time and the time of the fault occurrence;

[0067] S43. Determine whether the time interval is greater than or equal to a preset time interval threshold;

[0068] S44. If the time interval is less than a preset time interval threshold, perform the step of "determining the historical power load data sequence of the target power network if the target power network does not contain a fault node";

[0069] S45. When the time interval is greater than or equal to a preset time interval threshold, determine the initial historical power load data sequence corresponding to the target power network and determine the fault node historical power load data sequence corresponding to the fault node, wherein the initial historical power load data sequence includes several initial historical power load data based on the same preset time period, and the fault node historical power load data sequence includes several fault node historical power load data based on the same preset time period.

[0070] S46. Based on the initial historical power load data sequence and the fault node historical power load data sequence, remove the corresponding fault node historical power load data from the initial historical power load data to obtain the historical power load data sequence corresponding to the target power network, and execute the step of "determining the power load benchmark of the target power network".

[0071] Explained, power network faults are generally repaired promptly to ensure normal operation. Power faults that can be repaired quickly usually do not significantly impact the future load on the target power network corresponding to the pre-defined power service tasks. However, power faults that cannot be repaired promptly for various reasons, such as those caused by road construction, will significantly impact the future load on the target power network corresponding to the pre-defined power service tasks. This impact is even greater when multiple power fault nodes exist over a large area. Therefore, to improve the accuracy of expected load forecasting, it is necessary to fully consider these scenarios through technical means.

[0072] Therefore, based on the above concept, when the target power network contains faulty nodes, the fault occurrence time corresponding to the faulty node is determined. Thus, when implementing the embodiments of the present invention, it is necessary to record and store the fault occurrence time corresponding to the faulty node in a timely manner, obtain the current time, calculate the time interval between the current time and the fault occurrence time, and then determine whether the above time interval is greater than or equal to a preset time interval threshold. The preset time interval threshold represents the judgment standard and dividing line for whether the faulty node can be repaired in a timely manner.

[0073] If the aforementioned time interval is less than a preset time interval threshold, it is determined that the fault of the faulty node occurred recently and can be repaired in a timely manner. The fault of the faulty node is a short-term or temporary fault. The step of "determining the historical power load data sequence of the target power network when the target power network does not contain a faulty node" in the above embodiment is executed. That is, it is processed according to the case in the above embodiment where the target power network does not contain a faulty node, that is, it is processed according to the target power network corresponding to the preset power service task being in a normal state.

[0074] Furthermore, if the aforementioned time interval is greater than or equal to a preset time interval threshold, it is determined that the fault of the fault node has occurred for some time, and the fault node cannot be repaired in a timely manner for various reasons, thus constituting a long-term fault. In this case, when predicting the expected load of the target power network corresponding to the preset power service task, it is necessary to remove the relevant power loads involved in the fault node to ensure the accuracy of the expected load prediction. Therefore, the initial historical power load data sequence corresponding to the target power network is determined, and the historical power load data sequence of the fault node corresponding to the fault node is determined. The initial historical power load data sequence contains several initial historical power load data based on the same preset time period, and the historical power load data sequence of the fault node contains several historical power load data of the fault node based on the same preset time period. In this case, it is necessary to pre-control the target power network in a grid-based independent manner and record the historical power load data of each corresponding power grid. That is, all power users affected by the fault node constitute one power grid.

[0075] Then, based on the initial historical power load data sequence and the historical power load data sequence of the fault node, the corresponding historical power load data of the fault node is removed from the initial historical power load data to obtain the historical power load data sequence corresponding to the target power network. At this time, the historical power load data sequence corresponding to the target power network represents the historical power load of the target power network in its normal state. The step of "determining the power load benchmark of the target power network" involved in the above embodiment is executed. The remaining process is carried out according to the steps after "determining the power load benchmark of the target power network" in the above embodiment, and will not be repeated here.

[0076] This invention, in the case of a faulty node in the target power network, determines the proximity of the faulty node's occurrence time and predicts whether the faulty node can be repaired in a timely manner. If the faulty node's occurrence time is relatively recent, it is assumed that the faulty node can recover quickly; if the faulty node's occurrence time is relatively distant, it is determined that the faulty node cannot recover quickly. Therefore, when predicting the expected load corresponding to a preset power service task, the power load corresponding to the faulty node is excluded. Through the multi-task collaboration of power load prediction, grid faults, generation planning, and optimized scheduling, the accuracy of predicting the expected load corresponding to each preset power service task is further improved. This further improves the accuracy of the target load corresponding to power dispatch, enabling the response to complex and variable power demand and load conditions, thereby improving the accuracy of power dispatch. In a complex and variable power demand environment, it achieves reasonable scheduling and optimization of power resources, improving the overall utilization rate of power resources.

[0077] In one embodiment, please refer to Figure 5 , Figure 5 This is a schematic diagram of the third sub-process of the power dispatching method based on multi-task collaboration provided in an embodiment of the present invention. For example... Figure 5 As shown, in this embodiment, determining the target load for power dispatch based on all the expected loads includes:

[0078] S51. Calculate the sum of all expected loads corresponding to all the preset power service tasks to obtain the initial target load corresponding to the power dispatch;

[0079] S52. Determine a number of historical dispatched power loads corresponding to the initial target load according to the preset time period;

[0080] S53. Calculate the average of all historical dispatched power loads to obtain the average historical dispatched power load corresponding to the power dispatch.

[0081] S54. Determine whether the initial target load is equal to the historical average power load.

[0082] S55. If the initial target load is not equal to the historical average power load, the initial target load is adjusted in the direction of the historical average power load to obtain the target load corresponding to the power dispatch, and the target load is not equal to the historical average power load.

[0083] S56. If the initial target load is equal to the historical average power load, the initial target load shall be used as the target load corresponding to the power dispatch.

[0084] For an explanatory explanation, please refer to [link / reference]. Figure 2 ,like Figure 2 As shown, based on the expected load forecasting of the target power network corresponding to the preset power service task, the power dispatching processing method of this embodiment predicts the expected load as a branch forecast or local forecast of the target power network corresponding to the preset power service task. The target load corresponding to power dispatching is a positive branch forecast (i.e., backbone forecast) or an overall forecast. In addition to the above embodiment, which uses the probability of the occurrence of past actual power loads carried by the target power network corresponding to the preset power service task, as reflected by several historical power load data, to constrain and converge the predicted expected load, thereby reducing the forecast error and improving the accuracy of the corresponding expected load forecast, the target load corresponding to power dispatching can also be further constrained and converged from several historical dispatched power loads to further reduce the overall forecast error and further improve the accuracy of the target load forecasting corresponding to power dispatching, thereby improving the accuracy of power dispatching.

[0085] Based on the above concept, after separately predicting the expected load corresponding to each preset power business task to obtain the expected load corresponding to each of the n preset power business tasks, the sum of all expected loads corresponding to the above n (i.e. all) preset power business tasks is calculated to obtain the initial target load corresponding to the overall power dispatch. The initial target load represents the overall load corresponding to the preliminary predicted power dispatch. According to the preset time period, several historical dispatch power loads corresponding to the initial target load are determined. The historical dispatch power loads represent the actual overall historical load corresponding to the power dispatch. That is, the historical dispatch power loads and the initial target loads are different specific contents of the same dimension or level. This is the meaning of the above.

[0086] Based on the above, the average of all historical dispatched power loads is calculated to obtain the average historical dispatched power load corresponding to the power dispatch. It is then determined whether the initial target load is greater than or equal to the average historical dispatched power load. If the initial target load is greater than or less than the average historical dispatched power load, the initial target load is adjusted in the direction of the average historical dispatched power load to obtain the target load corresponding to the power dispatch. This brings the initial target load closer to the average historical dispatched power load, but not equal to it. Therefore, the target load is not equal to the average historical dispatched power load, but the initial target load is equal to the average historical dispatched power load. Under the condition of load average, the initial target load is taken as the target load corresponding to power dispatch. That is, when the initial target load is equal to the historical dispatch power load average, the initial target load is not adjusted in the direction of the historical dispatch power load average. In order to achieve a holistic view, several historical dispatch power loads from the overall perspective of power dispatch are used to further constrain and converge the initial target load corresponding to power dispatch, thereby further reducing the overall prediction error and minimizing the degree of power resource waste or power supply shortage. This can further improve the accuracy of the target load prediction corresponding to power dispatch, thereby improving the accuracy of power dispatch.

[0087] Further, if the initial target load is not equal to the historical average dispatched power load, the initial target load is adjusted in the direction of the historical average dispatched power load to obtain the target load corresponding to the power dispatch, including:

[0088] Calculate the difference between the initial target load and the historical average dispatched power load;

[0089] If the initial target load is greater than the historical average power load, the initial target load is reduced by half of the difference to obtain the target load corresponding to the power dispatch.

[0090] If the initial target load is less than the historical average power load, the initial target load is increased by half of the difference to obtain the target load corresponding to the power dispatch.

[0091] Specifically, based on the above description, when adjusting the initial target load towards the average historical dispatched power load, the difference between the initial target load and the average historical dispatched power load is first calculated. If the initial target load is greater than the average historical dispatched power load, the initial target load is reduced by half of the difference, i.e., the initial target load is reduced to approach the average historical dispatched power load, thus obtaining the target load corresponding to power dispatch, i.e., "target load = initial target load - ½ difference". If the initial target load is less than the average historical dispatched power load, the initial target load is increased by half of the difference, i.e., the initial target load is increased to approach the average historical dispatched power load, thus obtaining the target load corresponding to power dispatch, i.e., "target load = initial target load + ½ difference". By using the technical concept and means of compromising between the median and the mean, the overall forecast error can be further reduced, minimizing the waste of power resources or the degree of power supply shortage, thereby further improving the accuracy of the target load forecast corresponding to power dispatch and thus improving the accuracy of power dispatch.

[0092] This invention, through the historical average power load corresponding to power dispatch, converges and constrains the initial target load corresponding to power dispatch. Based on targeted predictions of all preset power service tasks, it further improves the prediction accuracy of the target load corresponding to power dispatch, avoiding problems such as power resource waste or insufficient power supply. This further enhances the accuracy of the target load corresponding to power dispatch, enabling the response to complex and variable power demand and load conditions, thereby improving the accuracy of power dispatch. In complex and variable power demand environments, it achieves rational dispatch and optimization of power resources, improving the overall utilization rate of power resources.

[0093] It should be noted that the power dispatching processing method based on multi-task collaboration described in the above embodiments can be recombined with the technical features included in different embodiments as needed to obtain a combined implementation scheme, but all of them are within the protection scope claimed by this invention.

[0094] Please see Figure 6 , Figure 6 This is a schematic block diagram of a power dispatching system based on multi-task collaboration provided in an embodiment of the present invention. Corresponding to the above-described power dispatching method based on multi-task collaboration, this embodiment of the present invention also provides a power dispatching system based on multi-task collaboration. Figure 6 As shown, the power dispatching system based on multi-task collaboration includes modules for executing the aforementioned power dispatching method based on multi-task collaboration. This power dispatching system based on multi-task collaboration can be configured in a computer device. For details, please refer to... Figure 6The power dispatching and processing system 60 based on multi-task collaboration includes an initial determination module 61, a first determination module 62, a second determination module 63, a third determination module 64, a first prediction module 65, a fourth determination module 66, and a dispatching module 67.

[0095] Among them, the initial determination module 61 is used to determine several preset power business tasks;

[0096] The first determining module 62 is used to determine the target power network corresponding to the preset power service task, and to determine whether the target power network contains faulty nodes.

[0097] The second determining module 63 is used to determine the historical power load data sequence of the target power network if the above determination is negative, wherein the historical power load data sequence includes historical power load data.

[0098] The third determining module 64 is used to determine the power load benchmark of the target power network, and to determine the adjustment range of the power load of the target power network based on the historical power load data and the power load benchmark.

[0099] The first prediction module 65 is used to predict the load of the target power network based on the power load benchmark and the adjustment range, so as to obtain the expected load of the preset power service task.

[0100] The fourth determining module 66 is used to determine the target load of the power dispatch based on all the expected loads;

[0101] The scheduling module 67 is used to perform power scheduling based on the target load.

[0102] In one embodiment, the third determining module 64 includes:

[0103] The first calculation submodule is used to calculate the average value of all the historical power load data and use the average value as the power load benchmark corresponding to the target power network;

[0104] Alternatively, a sorting submodule is used to sort all the historical power load data in ascending order, and to use the historical power load data with the smallest value as the power load benchmark corresponding to the target power network.

[0105] In one embodiment, the third determining module 64 includes:

[0106] The first determining submodule is used to determine the percentage change in the historical power load of the target power network relative to the power load benchmark based on the historical power load data and the power load benchmark.

[0107] The second determining submodule is used to determine the minimum and maximum ratio values ​​included in all the said variation ratios, and to form a ratio range by combining the minimum and maximum ratio values ​​to obtain the adjustment ratio range;

[0108] The third determining submodule is used to determine the adjustment range of the power load of the target power network based on the adjustment ratio range.

[0109] In one embodiment, the third determining submodule includes:

[0110] The second calculation submodule is used to calculate the average value of all the historical power load data;

[0111] The statistics submodule is used to count the number of historical power load data that are greater than the average value based on all the historical power load data, to obtain a first quantity value, and to count the number of historical power load data that are less than the average value, to obtain a second quantity value.

[0112] The third calculation submodule is used to calculate the ratio of the first quantity value to the second quantity value;

[0113] The first adjustment submodule is used to calculate the median of the ratio corresponding to the adjustment ratio range, and adjust the median of the ratio according to the ratio to obtain the adjustment range of the power load corresponding to the target power network.

[0114] In one embodiment, the first adjustment submodule includes at least one of the following:

[0115] The first addition submodule is used to increase the median of the ratio by the ratio corresponding to the ratio when the ratio is greater than 1.

[0116] The first reduction submodule is used to reduce the median of the ratio by the corresponding ratio when the ratio is less than 1.

[0117] The assignment submodule is used to assign the magnitude of the adjustment of the median of the ratio to zero when the ratio is equal to 1.

[0118] In one embodiment, the power dispatching processing system 60 based on multi-task collaboration further includes:

[0119] The fifth determining module is used to determine the fault occurrence time corresponding to the fault node when the target power network contains a fault node;

[0120] The acquisition module is used to acquire the current time and calculate the time interval between the current time and the time of the fault occurrence;

[0121] The first judgment module is used to determine whether the time interval is greater than or equal to a preset time interval threshold.

[0122] The sixth determining module is used to determine the initial historical power load data sequence corresponding to the target power network and the fault node historical power load data sequence corresponding to the fault node when the time interval is greater than or equal to a preset time interval threshold. The initial historical power load data sequence includes several initial historical power load data based on the same preset time period, and the fault node historical power load data sequence includes several fault node historical power load data based on the same preset time period.

[0123] The elimination module is used to eliminate the corresponding historical power load data of the fault node from the initial historical power load data based on the initial historical power load data sequence and the historical power load data sequence of the fault node, so as to obtain the historical power load data sequence corresponding to the target power network, and to perform the step of "determining the power load benchmark of the target power network".

[0124] In one embodiment, the power dispatching processing system 60 based on multi-task collaboration further includes:

[0125] The execution module is used to perform the step of "determining the historical power load data sequence of the target power network when the time interval is less than a preset time interval threshold".

[0126] In one embodiment, the fourth determining module 66 includes:

[0127] The fourth calculation submodule is used to calculate the sum of all expected loads corresponding to all the preset power service tasks, and to obtain the initial target load corresponding to the power dispatch.

[0128] The fourth determining submodule is used to determine a number of historical dispatched power loads corresponding to the initial target load according to the preset time period;

[0129] The fifth calculation submodule is used to calculate the average of all historical dispatched power loads to obtain the average historical dispatched power load corresponding to the power dispatch.

[0130] The first judgment submodule is used to determine whether the initial target load is equal to the historical average power load.

[0131] The second adjustment submodule is used to adjust the initial target load in the direction of the average value of the historical dispatched power load when the initial target load is not equal to the average value of the historical dispatched power load, so as to obtain the target load corresponding to the power dispatch, and the target load is not equal to the average value of the historical dispatched power load.

[0132] The fifth determining submodule is used to determine the initial target load as the target load corresponding to the power dispatch when the initial target load is equal to the historical average power load.

[0133] In one embodiment, the second adjustment submodule includes:

[0134] The sixth calculation submodule is used to calculate the difference between the initial target load and the historical average power load.

[0135] The second reduction submodule is used to reduce the initial target load by half of the difference when the initial target load is greater than the historical average power load, so as to obtain the target load corresponding to the power dispatch.

[0136] The second additional submodule is used to increase the initial target load by half of the difference when the initial target load is less than the historical average power load, so as to obtain the target load corresponding to the power dispatch.

[0137] It should be noted that those skilled in the art can clearly understand that the specific implementation process of the above-mentioned power dispatching and processing system based on multi-task collaboration and each module can be referred to the corresponding description in the foregoing method embodiments. For the sake of convenience and brevity, it will not be repeated here.

[0138] Meanwhile, the division and connection methods of the modules in the above-mentioned power dispatching and processing system based on multi-task collaboration are only for illustrative purposes. In other embodiments, the power dispatching and processing system based on multi-task collaboration can be divided into different modules as needed, and the modules in the power dispatching and processing system based on multi-task collaboration can be connected in different sequences and methods to complete all or part of the functions of the power dispatching and processing system based on multi-task collaboration.

[0139] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0140] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A power dispatch processing method based on multi-task cooperation, characterized in that, The method comprises: determining a plurality of preset power service tasks; determining a target power network corresponding to the preset power service tasks, and determining whether the target power network contains a fault node; if the determination result is no, determining a historical power load data sequence of the target power network, wherein the historical power load data sequence contains historical power load data; determining a power load benchmark of the target power network, and determining an adjustment range of the power load of the target power network according to the historical power load data and the power load benchmark; predicting the load of the target power network according to the power load benchmark and the adjustment range, to obtain an expected load of the preset power service task; determining a target load of the power dispatching according to all the expected loads; and performing power dispatching according to the target load. The method further comprises: if the target power network contains a fault node, determining a fault occurrence time corresponding to the fault node; obtaining a current time, and calculating a time interval between the current time and the fault occurrence time; determining whether the time interval is greater than or equal to a preset time interval threshold; if the time interval is greater than or equal to the preset time interval threshold, determining an initial historical power load data sequence corresponding to the target power network, and determining a fault node historical power load data sequence corresponding to the fault node, wherein the initial historical power load data sequence contains a plurality of initial historical power load data based on a same preset time period, and the fault node historical power load data sequence contains a plurality of fault node historical power load data based on the same preset time period; according to the initial historical power load data sequence and the fault node historical power load data sequence, removing the corresponding fault node historical power load data from the initial historical power load data to obtain a historical power load data sequence corresponding to the target power network, determining a power load benchmark of the target power network, and determining an adjustment range of the power load of the target power network according to the historical power load data and the power load benchmark; predicting the load of the target power network according to the power load benchmark and the adjustment range, to obtain an expected load of the preset power service task; determining a target load of the power dispatching according to all the expected loads; and performing power dispatching according to the target load. In a case that the time interval is less than a preset time interval threshold, historical power load data of the target power network is determined, wherein the historical power load data comprises historical power load data; a power load benchmark of the target power network is determined, and an adjustment range of power load of the target power network is determined according to the historical power load data and the power load benchmark; the load of the target power network is predicted according to the power load benchmark and the adjustment range, to obtain an expected load of the preset power service task; a target load of the power dispatch is determined according to all the expected loads; and the power dispatch is performed according to the target load.

2. The power dispatching processing method based on multi-task cooperation according to claim 1, characterized in that, The power load benchmark of the target power network is determined, comprising: a mean value of all the historical power load data is calculated, and the mean value is taken as the power load benchmark corresponding to the target power network; or, all the historical power load data are sorted in ascending order, and the historical power load data with the minimum value is taken as the power load benchmark corresponding to the target power network.

3. The multi-task cooperation-based power dispatching processing method according to claim 1 or 2, characterized in that, The adjustment range of power load of the target power network is determined according to the historical power load data and the power load benchmark, comprising: a variation proportion of the historical power load of the target power network relative to the power load benchmark is determined according to the historical power load data and the power load benchmark; a minimum proportion value and a maximum proportion value contained in all the variation proportions are determined, and the minimum proportion value and the maximum proportion value are taken as a proportion range, to obtain an adjustment proportion range; the adjustment range of power load of the target power network is determined according to the adjustment proportion range.

4. The power dispatching processing method based on multi-task cooperation according to claim 3, characterized in that, The adjustment range of power load of the target power network is determined according to the adjustment proportion range, comprising: a mean value of all the historical power load data is calculated; a first quantity value of the historical power load data greater than the mean value is counted according to all the historical power load data, and a second quantity value of the historical power load data less than the mean value is counted, to obtain a second quantity value; a ratio of the first quantity value to the second quantity value is calculated; a proportion median value corresponding to the adjustment proportion range is calculated, and the proportion median value is adjusted according to the ratio, to obtain the adjustment range of power load corresponding to the target power network.

5. The power dispatching processing method based on multi-task cooperation according to claim 4, characterized in that, The proportion median value is adjusted according to the ratio, comprising at least one of the following: in a case that the ratio is greater than 1, the proportion median value is increased by a proportion size corresponding to the ratio; in a case that the ratio is less than 1, the proportion median value is decreased by a proportion size corresponding to the ratio; in a case that the ratio is equal to 1, the size of the adjustment of the proportion median value is assigned a value of zero.

6. The power dispatching process method based on multi-task cooperation according to claim 1 or 2, characterized in that, The target load of the power dispatch is determined according to all the expected loads, comprising: a sum of all the expected loads corresponding to all the preset power service tasks is calculated, to obtain an initial target load corresponding to the power dispatch; a plurality of historical dispatch power loads corresponding to the initial target load are determined according to the preset time period; calculate a mean value of all historical dispatching power loads to obtain a historical dispatching power load mean value corresponding to the power dispatching; determine whether the initial target load is equal to the historical dispatching power load mean value; in a case where the initial target load is not equal to the historical dispatching power load mean value, adjust the initial target load to a direction of the historical dispatching power load mean value to obtain a target load corresponding to the power dispatching, and the target load is not equal to the historical dispatching power load mean value; in a case where the initial target load is equal to the historical dispatching power load mean value, take the initial target load as the target load corresponding to the power dispatching.

7. The power dispatching processing method based on multi-task cooperation according to claim 6, characterized in that, in a case where the initial target load is not equal to the historical dispatching power load mean value, adjust the initial target load to a direction of the historical dispatching power load mean value to obtain a target load corresponding to the power dispatching, comprising: calculate a difference value between the initial target load and the historical dispatching power load mean value; in a case where the initial target load is greater than the historical dispatching power load mean value, reduce the initial target load by half of the difference value to obtain the target load corresponding to the power dispatching; in a case where the initial target load is less than the historical dispatching power load mean value, increase the initial target load by half of the difference value to obtain the target load corresponding to the power dispatching.

8. A power dispatch processing system based on multi-task cooperation, characterized by, comprising: an initial determination module configured to determine a plurality of preset power service tasks; a first determination module configured to determine a target power network corresponding to the preset power service task, and determine whether the target power network contains a fault node; a second determination module configured to, if the determination result is no, determine a historical power load data sequence of the target power network, wherein the historical power load data sequence contains historical power load data; a third determination module configured to determine a power load benchmark of the target power network, and determine an adjustment range of the power load of the target power network according to the historical power load data and the power load benchmark; a first prediction module configured to predict the load of the target power network according to the power load benchmark and the adjustment range to obtain an expected load of the preset power service task; a fourth determination module configured to determine a target load of the power dispatching according to all the expected loads; a dispatching module configured to perform power dispatching according to the target load; a fifth determination module configured to, in a case where the target power network contains the fault node, determine a fault occurrence time corresponding to the fault node; an acquisition module configured to acquire a current time, and calculate a time interval between the current time and the fault occurrence time; a first determination module configured to determine whether the time interval is greater than or equal to a preset time interval threshold. a sixth determining module, configured to determine an initial historical power load data sequence corresponding to the target power network and a fault node historical power load data sequence corresponding to the fault node when the time interval is greater than or equal to a preset time interval threshold, wherein the initial historical power load data sequence comprises a plurality of initial historical power load data based on a same preset time period, and the fault node historical power load data sequence comprises a plurality of fault node historical power load data based on the same preset time period; a removing module, configured to remove corresponding fault node historical power load data from the initial historical power load data according to the initial historical power load data sequence and the fault node historical power load data sequence, to obtain a historical power load data sequence corresponding to the target power network, to determine a power load benchmark of the target power network, and to determine an adjustment amplitude of a power load of the target power network according to the historical power load data and the power load benchmark; to predict the load of the target power network according to the power load benchmark and the adjustment amplitude, to obtain an expected load of the preset power service task; to determine a target load of the power dispatching according to all the expected loads; and to perform power dispatching according to the target load; a performing module, configured to determine a historical power load data sequence of the target power network when the time interval is less than the preset time interval threshold, wherein the historical power load data sequence comprises historical power load data; to determine a power load benchmark of the target power network, and to determine an adjustment amplitude of a power load of the target power network according to the historical power load data and the power load benchmark; to predict the load of the target power network according to the power load benchmark and the adjustment amplitude, to obtain an expected load of the preset power service task; to determine a target load of the power dispatching according to all the expected loads; and to perform power dispatching according to the target load.

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