Power dispatching processing method and system based on multi-task cooperation

Abstract

The invention belongs to the technical field of power dispatching, provides a power dispatching processing method and system based on multi-task collaboration, and aims to solve the problem of low power dispatching accuracy in the prior art. The method comprises the following steps: determining a plurality of preset power business tasks, and then determining a target power network corresponding to each preset power business task; determining whether the target power network contains a fault node, determining historical power load data and a power load reference of the target power network when the target power network does not contain the fault node, determining an adjustment amplitude of the power load of the target power network, and then determining the power load of the target power network according to the power load reference and the adjustment amplitude. According to the method, the expected load corresponding to the target power network is predicted, and the target load corresponding to power dispatching is determined according to all the expected loads corresponding to the plurality of preset power business tasks, so that the accuracy of power dispatching can be improved, and the utilization rate of overall power resources can be improved in a complex and changeable power demand environment.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power dispatching, and in particular to an electric power dispatching processing method and system based on multi-task collaboration. Background Art

[0002] Power dispatching uses a monitoring system to monitor the operation of the power grid in real time, including the power generation status of generators, load demand, and the operating status of transmission lines. Based on the actual conditions of the power grid, it promptly adjusts the output of generators or operates equipment in the power grid to ensure system stability. The main tasks of power dispatching include power generation planning and optimized scheduling, load forecasting and management, real-time operation control, safety analysis and fault handling, coordination of new energy and energy storage, emergency and disaster response, and environmental protection and economic balance. Power dispatching's multiple tasks cover the entire power business chain from forecasting, planning, and real-time control, requiring dynamic trade-offs between multiple objectives to ensure the continued safe and stable operation of the power grid. Power dispatching is the core link in the operation of the power system, and its multiple tasks work together to ensure the safe, stable, economical, and environmentally friendly operation of the power grid.

[0003] With the continuous increase in electricity demand and the increasing complexity of power systems, power dispatch and management are becoming increasingly important. Traditional technologies generally predict power demand based on historical user data and dispatch power based on the predicted power demand. However, directly using historical data to predict power demand can easily lack effective dispatch strategies. For example, residential electricity demand varies during holidays and commercial production, and demand also varies during holidays and commercial production. Furthermore, long-term and short-term faults in the power network can also affect power demand. For example, a long-term power network fault caused by construction has different impacts on power demand than a short-term fault caused by an unexpected circuit. These factors can lead to complex and variable power demand, easily wasting power resources or insufficient power supply, making it impossible to achieve efficient power coordination and dispatch, resulting in low power resource utilization. As a result, traditional power dispatch methods are generally unable to cope with complex and changing power demand and load conditions.

[0004] Therefore, how to improve the accuracy of power dispatching has become an urgent problem that needs to be solved. Summary of the Invention

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

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: determine a number of preset power business tasks; determine the target power network corresponding to the preset power business tasks, and judge whether the target power network includes a fault node; if the above judgment is no, determine the historical power load data sequence of the target power network, wherein the historical power load data sequence includes historical power load data; 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; predict 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 business tasks; determine the target load of the power dispatching based on all the expected loads; and perform power dispatching based on the target load.

[0007] As a preferred solution for the power dispatching processing based on multi-task collaboration described in the present invention, the adjustment range of the power load of the target power network is determined according to the historical power load data and the power load benchmark, including: determining the change ratio of the historical power load of the target power network relative to the power load benchmark according to the historical power load data and the power load benchmark; determining the minimum proportion value and the maximum proportion value contained in all the change ratios, and forming a proportion range with the minimum proportion value and the maximum proportion value to obtain an adjustment proportion range; determining the adjustment range of the power load of the target power network according to the adjustment proportion range.

[0008] The beneficial effects of the present invention are as follows: by determining a number of 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 a fault node, when the target power network does not contain a fault node, determining a number of 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, and then predicting the expected load corresponding to the target power network based on the power load benchmark and the adjustment range, and then determining the target load corresponding to the power dispatching based on all the expected loads corresponding to the number of preset power business tasks, not only the automation and accuracy of power load prediction based on the collaboration of multiple power business tasks is realized, but also the automation processing of power dispatching with multi-task collaboration of power load prediction, power grid fault, power generation plan and optimized scheduling is realized to cope with complex and changeable power demand and load conditions, which can improve the accuracy of power dispatching, and can realize the reasonable dispatching and optimization of power resources in a complex and changeable power demand environment, thereby improving the overall utilization rate of power resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 A flowchart of a power dispatch processing method based on multi-task collaboration provided by an embodiment of the present invention; Figure 2 A schematic diagram of multi-task coordination relationships in a power dispatch processing method based on multi-task coordination provided by an embodiment of the present invention; Figure 3 A schematic diagram of a first sub-flow of a power dispatch processing method based on multi-task collaboration provided by an embodiment of the present invention; Figure 4 A schematic diagram of a second sub-flow of the power dispatch processing method based on multi-task collaboration provided by an embodiment of the present invention; Figure 5 A schematic diagram of a third sub-flow of the power dispatch processing method based on multi-task collaboration provided by an embodiment of the present invention; Figure 6 A schematic block diagram of a power dispatching and processing system based on multi-task collaboration provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0010] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, but not all of the embodiments.

[0011] An embodiment of the present invention provides a power dispatch processing method based on multi-task collaboration, which can be applied to devices including but not limited to computers and used in including but not limited to power dispatching.

[0012] In the face of the technical problem of low accuracy of power dispatching in traditional technologies, the inventors proposed an electric power dispatching processing method based on multi-task collaboration in an embodiment of the present invention. The core idea of ​​the embodiment of the present invention is: pre-divide the electric power business tasks to determine n electric power business tasks, where n is a natural number and n≥2, and when there is no power grid failure within the business scope covered by each electric power business task, based on the historical load data corresponding to each electric power business task, predict the expected load corresponding to the corresponding electric power business task, and then generate a power dispatching plan based on all expected loads, thereby not only realizing the automatic prediction of electric power load based on the collaboration of multiple electric power business tasks, but also realizing the automatic processing of electric power dispatching with multi-task collaboration of electric load prediction, power grid failure, power generation plan and optimized scheduling to cope with complex and changeable power demand and load conditions, which can improve the accuracy of electric power dispatching, and can realize the reasonable scheduling and optimization of electric power resources in a complex and changeable power demand environment, thereby improving the overall utilization rate of electric power resources.

[0013] Example 1, please refer to Figure 1 and Figure 2, Figure 1 A flowchart of a multi-task collaborative power dispatching method according to an embodiment of the present invention is provided. Figure 2 Schematic diagram of multi-task coordination relationship of the power dispatch processing method based on multi-task coordination provided by the 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: S11. Determine several preset power business tasks.

[0014] Explanatory speaking, a number of preset power business tasks are generally determined in response to a preset start instruction corresponding to power dispatching. Therefore, a start instruction is generally set in advance, that is, a preset start instruction. The preset start instruction indicates an instruction for the relevant computer system of power dispatching to start power dispatching. The preset start instruction can be an instruction corresponding to the user's starting operation, or an instruction corresponding to a timer or event trigger.

[0015] According to the granularity of electricity business attributes, including but not limited to regional, commercial, residential, and public utility electricity consumption, electricity usage attributes within a preset range are pre-divided into different electricity business tasks, resulting in a number of preset electricity business tasks, which can be represented as n preset electricity business tasks, where n is a natural number and n ≥ 2. A preset electricity business task represents a pre-divided electricity task for using electricity to conduct a certain business. For example, within a certain urban area, electricity use in the urban area can be divided into three preset electricity business tasks: industrial production electricity use, residential electricity use, and public utility electricity use. The three preset electricity business tasks represent electricity businesses of different nature or types.

[0016] Based on the above concepts and settings, when performing power dispatching, a number of preset power business tasks are generally determined in response to a preset start instruction corresponding to the power dispatching.

[0017] S12: Determine a target power network corresponding to the preset power service task, and judge whether the target power network includes a faulty node.

[0018] Explanatory, please continue to see Figure 2 As described above, different preset power service tasks correspond to different target power networks. That is, each preset power service task corresponds to 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.

[0019] According to the above description, when conducting power dispatch, a number of preset power business tasks are first determined. Generally, n preset power business tasks are determined first, and then the target power network corresponding to each preset power business task is determined, and it is judged whether the target power network contains a fault node. The fault node represents the node of the power network where the fault occurs. The fault node generally affects a certain range of electricity consumption. Accordingly, according to the duration of the fault of the fault node, it will also have a corresponding impact on the power load of the target power network. For example, the fault node caused by construction may affect from several days to more than ten days or even longer, and the sudden accidental fault node may affect the time ranging from several hours to one day.

[0020] S13: when the target power network includes a faulty node, determining a historical power load data sequence corresponding to the target power network; S14. When the target power network does not include a faulty node, determine a historical power load data sequence of the target power network, wherein the historical power load data sequence includes historical power load data.

[0021] Explanatoryally, a time period is generally set in advance, that is, a preset time period, which represents a time period for power dispatching. The preset time period includes but is not limited to a time period with years, months, days, and hours as time units.

[0022] According to the above configuration, if the target power network includes a faulty node, i.e., if the above determination is yes, i.e., if the target power network is experiencing abnormal power consumption, the historical power load data sequence corresponding to the target power network is not determined. If the target power network does not include a faulty node, i.e., if the above determination is no, i.e., if the target power network is experiencing normal power consumption, the power load of the target power network exhibits normal regularity and stability. Thus, the historical power load data sequence corresponding to the target power network is determined. The historical power load data sequence includes multiple historical power load data based on the same preset time period, and the historical power load data sequence represents a sequence composed of the multiple historical power load data in corresponding chronological order. For example, assuming that the preset time is December of the current year, the historical power load data sequence includes the historical power load data of December of each year from the 1st year, the 2nd year, ..., the mth year 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 the 1st year, the 2nd year, ..., the mth year. The historical power load data sequence represents the temporal longitudinal relationship of the actual power load in December of each year from the 1st year, the 2nd year, ..., the mth year.

[0023] S15: Determine a power load benchmark of the target power network, and determine an adjustment range of the power load of the target power network based on the historical power load data and the power load benchmark.

[0024] Explanatoryally, the power load benchmark represents a load reference standard for predicting the power load, and the adjustment range represents the magnitude of the change in the power load based on the power load benchmark. The adjustment range can be a proportional value or a quantitative value.

[0025] According to the above conception and setting, the power load benchmark corresponding to the target power network is determined. The power load benchmark can be an artificially set power load reference standard, or it can be a power load reference standard obtained through statistics and analysis of historical power load data of the target power network. Based on the historical power load data and the power load benchmark, and then on the basis of statistics and analysis of several historical power load data and power load benchmarks, the adjustment range of the power load corresponding to the target power network is determined. Therefore, the adjustment range is related to both the historical power load data and the power load benchmark. Therefore, with the help of a certain degree of stability and regularity of the power load carried by the target power network corresponding to the preset power business tasks reflected by several historical power load data, the adjustment range of the power load corresponding to the target power network is determined. Since the adjustment range reflects the regularity and stability corresponding to the change size of the historical power load data, the expected load corresponding to the preset power business task can be relatively accurately predicted with the help of the adjustment range, thereby improving the accuracy of the corresponding expected load prediction for the preset power business task.

[0026] S16. 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 business task.

[0027] Explanatory, as described above, on the basis of the above-mentioned power load benchmark, the power load benchmark is adjusted according to the above-mentioned adjustment range to predict the expected load corresponding to the target power network, that is, to predict the load corresponding to the target power network in the preset time period in the future, and obtain the expected load corresponding to the preset power business task. By making use of the stability and regularity to a certain extent of the past actual power load borne by the target power network corresponding to the preset power business task reflected in a number of historical power load data, the expected load corresponding to the preset power business task can be relatively accurately predicted, which can improve the accuracy of the corresponding expected load prediction for the preset power business task.

[0028] S17. Determine the target load for the power dispatch based on all the expected loads.

[0029] Explanatoryally, for each preset power business task, the corresponding expected load is predicted as described above. For the several (for example, n) preset power business tasks determined above, the corresponding n expected loads are obtained. Thus, based on all the expected loads corresponding to the n preset power business tasks, all the expected loads are integrated, for example, including but not limited to directly adding up all the expected loads, to determine the target load corresponding to the power dispatching. The target load is the total power required for the predicted power dispatching.

[0030] S18. Perform power dispatching according to the target load.

[0031] Explanatoryally, as described above, based on the target load, a power dispatch plan or schedule is generally generated and executed to perform power dispatch.

[0032] In an embodiment of the present invention, a plurality of preset power business tasks are determined, and then a target power network corresponding to the preset power business tasks is determined, and it is judged whether the target power network includes a fault node. If the target power network does not include a fault node, a historical power load data sequence corresponding to the target power network is determined, wherein the historical power load data sequence includes a plurality of historical power load data based on the same preset time period, and then a power load benchmark corresponding to the target power network is determined. Based on the historical power load data and the power load benchmark, an adjustment range of the power load corresponding to the target power network is determined. Based on the power load benchmark and the adjustment range, an expected load corresponding to the target power network is predicted, and the preset power network is obtained. The expected load corresponding to the power business task is determined, and then the target load corresponding to the power dispatch is determined based on all the expected loads corresponding to several preset power business tasks. According to the target load, a power dispatch plan is generated, and the power dispatch plan is executed to carry out power dispatch. It not only realizes the automation and accuracy of power load forecasting based on the collaboration of multiple power business tasks, but also realizes the automated processing of power dispatching with multi-task collaboration of power load forecasting, power grid faults, power generation planning and optimized scheduling to cope with complex and changeable power demand and load conditions, which can improve the accuracy of power dispatching, realize the reasonable scheduling and optimization of power resources in a complex and changeable power demand environment, and improve the overall utilization rate of power resources.

[0033] In one embodiment, determining the power load benchmark of the target power network includes: Calculating a mean value of all the historical power load data, and using the mean value as a power load benchmark corresponding to the target power network; Alternatively, all the 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.

[0034] Explanatoryally, in one embodiment, the mean of all historical power load data is calculated, and the mean is the median value, and the mean is used as the power load benchmark corresponding to the target power network. The upward or downward change of the mean can obtain the historical power load data. According to the stability and regularity of the past actual power load borne by the target power network corresponding to the preset power business tasks reflected in a number of historical power load data, the actual power load corresponding to the future target power network should also fluctuate around the mean. Therefore, the predicted expected load fluctuation around the mean will be more in line with the actual power load situation of the target power network in the future. Therefore, the predicted expected load is accurate, thereby improving the accuracy of power dispatching.

[0035] 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 upward change of the power load benchmark can obtain the historical power load data. According to the stability and regularity of the past actual power load borne 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 change upward based on the smallest historical power load data. Therefore, the predicted expected load is more in line with the actual power load situation of the target power network in the future when the minimum historical power load data changes upward. Therefore, the predicted expected load is accurate, thereby improving the accuracy of power dispatching.

[0036] The embodiment of the present invention determines the corresponding power load benchmark based on historical actual conditions for each preset power business task according to the corresponding historical power load data, and then predicts the expected load corresponding to each preset power business task based on the power load benchmark, thereby improving the prediction accuracy of the expected load, and by improving the prediction accuracy of the expected load corresponding to each preset power business task, further improving the accuracy of the target load corresponding to power dispatching, so as to cope with power demand and load conditions in complex and changing environments, thereby improving the accuracy of power dispatching, and realizing reasonable dispatch and optimization of power resources in a complex and changing power demand environment, thereby improving the overall utilization rate of power resources.

[0037] In one embodiment, determining the adjustment range of the power load of the target power network according to the historical power load data and the power load benchmark includes: determining, based on the historical power load data and the power load benchmark, a change ratio of the historical power load of the target power network relative to the power load benchmark; Determine a minimum ratio value and a maximum ratio value included in all the change ratios, and combine the minimum ratio value and the maximum ratio value to form a ratio range to obtain an adjustment ratio range; An adjustment range of the power load of the target power network is determined according to the adjustment ratio range.

[0038] Explanatory, as described above, the historical power load data and the power load benchmark are subtracted to obtain the change size of the historical power load data relative to the power load benchmark, and then the difference is divided by the power load benchmark to obtain the change ratio of the historical power load data relative to the power load benchmark to determine the change ratio of the historical power load of the target power network relative to the power load benchmark. Thus, for a number of historical power load data, a number of change ratio sets are obtained, and then the minimum and maximum ratio values ​​contained in all the change ratios are determined, and the minimum and maximum ratio values ​​are combined into a ratio range to obtain an adjusted ratio range. The adjusted ratio range represents the change range of a number of historical power load data relative to the power load benchmark in the form of a ratio. Similarly, with the help of a number of historical power load data, the target power network corresponding to the preset power business tasks reflected in the preset power load tasks is carried. If the actual power load in the past is stable, regular, and continuous to a certain extent, then the range of variation of the actual power load corresponding to the target power network in the future should also be within the above-mentioned adjustment ratio range. Based on this, the adjustment amplitude of the power load corresponding to the target power network is determined according to the above-mentioned adjustment ratio range. The adjustment amplitude can be the median of the above-mentioned adjustment ratio range or a suitable ratio value therein that conforms to logic and reasoning. Therefore, with the help of reasoning and analysis of the stability, regularity, and continuity of the past actual power load borne by the target power network corresponding to the preset power business tasks reflected in several historical power load data to a certain extent, the expected load predicted based on the adjustment amplitude will be more in line with the actual power load situation of the target power network in the future. Therefore, the predicted expected load is accurate, thereby improving the accuracy of power dispatching.

[0039] In an embodiment of the present invention, the change ratio of the historical power load of the target power network relative to the power load benchmark is determined based on historical power load data and a power load benchmark, and the minimum and maximum proportion values ​​contained in all the change ratios are determined. The minimum and maximum proportion values ​​are then combined into a proportion range to obtain an adjustment proportion range. Based on the adjustment proportion range, the adjustment amplitude of the power load corresponding to the target power network is determined, thereby determining the unique adjustment amplitude corresponding to each power business task based on the historical actual load of the target power network, that is, based on the historical power load data corresponding to the power business task. Since the adjustment amplitude reflects the personalized business task characteristics of each power business task, the expected load corresponding to the corresponding preset power business task is predicted based on the adjustment amplitude, which can improve the accuracy of the expected load prediction, and further improve the accuracy of the target load corresponding to the power dispatching, thereby improving the accuracy of the power dispatching, and can realize the reasonable dispatching and optimization of power resources in a complex and changeable power demand environment, thereby improving the overall utilization rate of power resources.

[0040] In one embodiment, see Figure 3 , Figure 3 This is a schematic diagram of the first sub-flow of the power dispatch processing method based on multi-task collaboration provided by 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: S31. Calculating the mean of all the historical power load data; S32. Counting the number of historical power load data greater than the mean value based on all the historical power load data to obtain a first quantity value, and counting the number of historical power load data less than the mean value to obtain a second quantity value; S33, calculating the ratio of the first quantity value to the second quantity value; 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.

[0041] Explanatory, also with the help of a certain degree of stability, regularity and continuity of the past actual power load carried by the target power network corresponding to the preset power business task reflected by a number of historical power load data, in order to more fully utilize and reflect the probability of occurrence of the past actual power load carried by the target power network corresponding to the preset power business task reflected by a number of historical power load data in the future power load, the mean of all historical power load data is calculated, and the mean is used as the dividing line. According to all historical power load data, the number of historical power load data greater than the above mean is counted to obtain a first quantity value, and the number of historical power load data less than the above mean is counted to obtain a second quantity value, and the ratio of the first quantity value to the second quantity value is calculated. The ratio represents the relative probability of occurrence of historical power load data greater than the mean and the occurrence of historical power load data less than the mean. According to the ratio, it is possible to predict the possibility of the future power load carried by the target power network corresponding to the preset power business task being greater than the mean or less than the mean, and calculate the median of the proportion corresponding to the adjustment ratio range. On the basis of the median of the proportion, according to the ratio, that is, according to the preset power business task Based on the probability of the future power load carried by the target power network being greater than or less than the mean, the median of the proportion is adjusted to obtain the adjustment amplitude of the power load corresponding to the target power network. The adjustment amplitude represents the magnitude of the change in the power load adjustment based on the power load benchmark. Thus, by leveraging the stability, regularity, and continuity of the past actual power load carried by the target power network corresponding to the preset power business task, as reflected in certain historical power load data, the probability ratio of the future power load change carried by the target power network corresponding to the preset power business task is predicted. By leveraging the above-mentioned mean and median, the different occurrence scenarios and probabilities of the past actual power load carried by the target power network corresponding to the preset power business task are fully utilized. That is, the probability of being greater than or less than the mean is taken into account on the basis of the mean. Thus, by leveraging the stability and regularity of the power load carried by the target power network corresponding to the preset power business task, as reflected in certain historical power load data, the expected load corresponding to the preset power business task can be relatively accurately predicted, thereby reducing prediction errors and improving the accuracy of the expected load forecast corresponding to the preset power business task.

[0042] Furthermore, the median of the ratio is adjusted according to the ratio, including at least one of the following: When the ratio is greater than 1, the median of the ratio is increased by the ratio corresponding to the ratio; When the ratio is less than 1, the median of the ratio is reduced by the ratio corresponding to the ratio; When the ratio is equal to 1, the magnitude of the proportional median adjustment is assigned a value of zero.

[0043] Specifically, based on the above, the median of the proportion is adjusted according to the proportion corresponding to the ratio, that is, the median of the proportion is increased or decreased by the proportion corresponding to the ratio, specifically: 1) When the above ratio is greater than 1, it indicates that the probability of the target power network corresponding to the preset power business task in the future carrying a "greater than the above average situation" is greater than the probability of "less than the above average situation", that is, the target power network corresponding to the preset power business task in the future is more likely to carry a power load greater than the average, thus, the median of the proportion is increased by the proportion corresponding to the above ratio, that is, "adjustment range = median of proportion * (1 + ratio)"; 2) Similarly, when the above ratio is less than 1, it indicates that the probability of the target power network corresponding to the preset power business task in the future carrying a "less than the above average situation" is greater than the probability of "greater than the above average situation", that is, the target power network corresponding to the preset power business task is more likely to carry a power load greater than the average. 3) When the ratio is equal to 1, it indicates that the probability of the target power network corresponding to the preset power business task carrying a power load "greater than the above average" in the future is the same as the probability of "less than the above average", that is, the probability of the target power network corresponding to the preset power business task carrying a power load equal to the average in the future is greater. Therefore, the adjustment size of the median ratio is assigned to 0, that is, zero, that is, "adjustment range = median ratio". By using the probability of the future power load of the target power network corresponding to the preset power business task reflected in a number of historical power load data, the predicted expected load is constrained and converged, which can improve the accuracy of the expected load forecast.

[0044] In an embodiment of the present invention, the mean of all historical power load data is calculated, and based on all historical power load data, the number of historical power load data greater than the mean is counted to obtain a first quantity value, and the number of historical power load data less than the mean is counted to obtain a second quantity value, and the ratio of the first quantity value to the second quantity value is calculated, and the median of the ratio corresponding to the adjustment ratio range is calculated, and the median of the ratio is adjusted accordingly according to the ratio to obtain the adjustment amplitude of the power load corresponding to the target power network, and it is possible to predict the size of the expected load in the same preset time period in the future and the average value based on the comparison of several historical power load data with the corresponding mean value in the same preset time period. The probability of occurrence compared with the average value, that is, whether the expected load is greater than the mean or less than the mean, or equal to the mean, so as to make use of the stability and regularity of the target power network carrying power load corresponding to the preset power business task reflected in a number of historical power load data to a certain extent, to relatively accurately predict the expected load corresponding to the preset power business task, which can improve the accuracy of the corresponding expected load prediction for the preset power business task, and further improve the accuracy of the target load corresponding to the power dispatching as a whole, so as to improve the accuracy of power dispatching, and can realize the reasonable dispatch and optimization of power resources in a complex and changeable power demand environment, and improve the overall utilization rate of power resources.

[0045] In one embodiment, see Figure 4 , Figure 4 This is a schematic diagram of the second sub-flow of the power dispatch processing method based on multi-task collaboration provided by an embodiment of the present invention. Figure 4 As shown, in this embodiment, the method further includes: S41. When the target power network includes a faulty node, determining a fault occurrence time corresponding to the faulty node; S42, obtaining the current time, and calculating the time interval between the current time and the time when the fault occurred; S43, determining whether the time interval is greater than or equal to a preset time interval threshold; S44, when the time interval is less than the preset time interval threshold, executing the step of “determining a historical power load data sequence of the target power network when the target power network does not include a faulty node”; S45. When the time interval is greater than or equal to a preset time interval threshold, determine an initial historical power load data sequence corresponding to the target power network, and determine a fault node historical power load data sequence corresponding to the fault node, wherein the initial historical power load data sequence includes a plurality of initial historical power load data based on the same preset time period, and the fault node historical power load data sequence includes a plurality of fault node historical power load data based on the same preset time period; S46. Based on the initial historical power load data sequence and the fault node historical power load data sequence, the corresponding fault node historical power load data is removed from the initial historical power load data to obtain the historical power load data sequence corresponding to the target power network, and the step of "determining the power load benchmark of the target power network" is executed.

[0046] Explanatoryly, when a power network fails, it will generally be repaired in a timely manner to ensure the normal operation of the power network. For power failures that can be repaired in a timely manner, it will generally not have a significant impact on the power load borne in the future by the target power network corresponding to the preset power business task. However, for power failures that cannot be repaired in a timely manner due to various reasons, such as power failures caused by road construction, it will have a significant impact on the power load borne in the future by the target power network corresponding to the preset power business task. In particular, when there are multiple power failure nodes in a large area, the impact on the power load borne in the future by the target power network corresponding to the preset power business task will be even greater. When making expected load forecasts, in order to further improve the accuracy of expected load forecasts, it is necessary to fully consider the above situations through technical means.

[0047] Therefore, according to the above concept, when the target power network includes a fault node, the fault occurrence time corresponding to the fault node is determined. Therefore, when implementing the embodiment of the present invention, it is necessary to timely record and store the fault occurrence time corresponding to the fault node, obtain the current time, and calculate the time interval between the current time and the fault occurrence time, and then judge whether the above time interval is greater than or equal to the preset time interval threshold, wherein the time interval threshold is set in advance, that is, the preset time interval threshold, which represents the judgment standard and dividing line of whether the fault of the fault node can be repaired in time.

[0048] When the above time interval is less than the preset time interval threshold, it is determined that the fault of the faulty node occurred recently, the faulty node can be repaired in time, and the fault of the faulty node is a short-term fault or a temporary fault, and the step of "determining the historical power load data sequence of the target power network when the target power network does not include the faulty node" of the above embodiment is performed, that is, processing is performed according to the situation in which the target power network does not include the faulty node in the above embodiment, that is, processing is performed according to the target power network corresponding to the preset power business task being in a normal state.

[0049] Moreover, when the above-mentioned time interval is greater than or equal to the 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 time due to various reasons. The fault of the fault node is a long-term fault. At this time, when predicting the expected load of the target power network corresponding to the preset power business task, it is necessary to eliminate the relevant power load involved in the fault node to ensure the accuracy of the expected load prediction. Thus, the initial historical power load data sequence corresponding to the target power network is determined, and the fault node historical power load data sequence corresponding to the fault node is determined, 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. In this case, it is necessary to pre-grid the target power network and independently control it and record the historical power load data of each corresponding power grid, that is, all power users affected by the fault node are one power grid.

[0050] Then, based on the initial historical power load data sequence and the fault node historical power load data sequence, the corresponding fault node historical power load data 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 normal part of the target power network, and the steps of "determining the power load benchmark of the target power network" involved in the above embodiment are 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.

[0051] The embodiment of the present invention, when the target power network contains a fault node, determines the distance of the occurrence time of the fault node, predicts whether the fault node can be repaired in time, and when the occurrence time of the fault node is relatively close, it is assumed that the fault node can return to normal as soon as possible, and when the occurrence time of the fault node is relatively far away, it is determined that the fault node cannot return to normal as soon as possible, so that when predicting the expected load corresponding to the preset power business task, the power load corresponding to the fault node is eliminated. Through the multi-task collaboration of power load prediction, power grid fault, power generation plan and optimized scheduling, the prediction accuracy of the expected load corresponding to each preset power business task can be further improved, and the accuracy of the target load corresponding to the power dispatching can be further improved, so as to cope with the power demand and load conditions in a complex and changeable environment, and improve the accuracy of power dispatching. It can realize the reasonable scheduling and optimization of power resources in a complex and changeable power demand environment, and improve the overall utilization rate of power resources.

[0052] In one embodiment, see Figure 5 , Figure 5 This is a schematic diagram of the third sub-flow of the power dispatch processing method based on multi-task collaboration provided by an embodiment of the present invention. Figure 5 As shown, in this embodiment, determining the target load of the power dispatch according to all the expected loads includes: S51. Calculate the sum of all expected loads corresponding to all the preset power business tasks to obtain an initial target load corresponding to the power dispatch; S52. Determine, based on the preset time period, a number of historical dispatched power loads corresponding to the initial target load; S53. Calculate the average of all historical dispatched power loads to obtain the average of the historical dispatched power loads corresponding to the power dispatch; S54, determining whether the initial target load is equal to the historical dispatch power load average; S55. If the initial target load is not equal to the average value of the historical dispatched power load, adjust the initial target load toward the average value of the historical dispatched power load to obtain a target load corresponding to the power dispatch, and the target load is not equal to the average value of the historical dispatched power load; S56. When the initial target load is equal to the average value of the historical dispatched power load, use the initial target load as the target load corresponding to the power dispatch.

[0053] Explanatory, please continue to see Figure 2 ,like Figure 2As shown, on the basis of the expected load forecast of the target power network corresponding to the preset power business task, for the power dispatching processing method of the embodiment of the present invention, the forecast of the expected load is the branch forecast or local forecast of the target power network corresponding to the preset power business task, and the target load corresponding to the power dispatching is the positive branch forecast (i.e., the trunk forecast) or the overall forecast. In addition to the above-mentioned embodiment described in which the predicted expected load is constrained and converged by the probability of the occurrence of the past actual power load carried by the target power network corresponding to the preset power business task reflected by several historical power load data, so as to reduce the forecast error and improve the accuracy of the corresponding expected load forecast, the target load corresponding to the power dispatching can also be further constrained and converged as a whole from several historical dispatched power loads of the power dispatching, so as to further reduce the overall forecast error and further improve the accuracy of the target load forecast corresponding to the power dispatching, thereby improving the accuracy of the power dispatching.

[0054] Based on the above concept, after making a separate prediction of the expected load corresponding to each preset power business task and obtaining the expected load corresponding to each of the n preset power business tasks, the sum of all the 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 power dispatch from the overall perspective. The initial target load represents the overall load corresponding to the preliminary predicted power dispatch, and according to the preset time period, several historical dispatch power loads corresponding to the initial target load are determined. The historical dispatch power load represents the actual overall historical load corresponding to the power dispatch, that is, the historical dispatch power load and the initial target load are different specific contents of the same dimension or level. This is the corresponding meaning above.

[0055] According to the above, the average of all historical dispatched power loads is calculated to obtain the average of the historical dispatched power loads corresponding to the power dispatch, and it is determined whether the above initial target load is greater than or equal to the above historical dispatched power load average. When the above initial target load is greater than or less than the above historical dispatched power load average, the initial target load is adjusted in the direction of the size of the historical dispatched power load average to obtain the target load corresponding to the power dispatch, so that the initial target load is close to the historical dispatched power load average, but not equal to the historical dispatched power load average, so that the target load is not equal to the historical dispatched power load average, but when the initial target load is equal to the historical dispatched power load average In the case of the average load, the initial target load is used as the target load corresponding to the power dispatch, that is, when the initial target load is equal to the average of the historical dispatch power load, the initial target load is not adjusted in the direction of the size of the average of the historical dispatch power load, so as to achieve from an overall perspective, using several historical dispatch power loads from the overall perspective of power dispatch to further constrain and converge the initial target load corresponding to the power dispatch, to further reduce the overall prediction error, to minimize the waste of power resources or the degree of insufficient power supply, which can further improve the accuracy of the target load prediction corresponding to the power dispatch, thereby improving the accuracy of power dispatch.

[0056] Furthermore, when the initial target load is not equal to the average value of the historical dispatched power load, the initial target load is adjusted toward the average value of the historical dispatched power load to obtain the target load corresponding to the power dispatch, including: Calculating the difference between the initial target load and the historical dispatch power load average; When the initial target load is greater than the average value of the historical dispatched power load, reducing the initial target load by half of the difference to obtain the target load corresponding to the power dispatch; In the case where the initial target load is less than the average value of the historical dispatched power load, the initial target load is increased by half of the difference to obtain the target load corresponding to the power dispatch.

[0057] Specifically, based on the above description, when the initial target load is adjusted toward the mean value of the historical dispatching power load, the difference between the initial target load and the mean value of the historical dispatching power load is first calculated, and when the initial target load is greater than the mean value of the historical dispatching power load, the initial target load is reduced by half of the difference, that is, the initial target load is reduced to approach the mean value of the historical dispatching power load, and the target load corresponding to the power dispatch is obtained, that is, "target load = initial target load - ½ difference", and when the initial target load is less than the mean value of the historical dispatching power load, the initial target load is increased by half of the difference, that is, the initial target load is increased to approach the mean value of the historical dispatching power load, and the target load corresponding to the power dispatch is obtained, that is, "target load = initial target load + ½ difference", thereby using the technical concept and means of taking a compromise between the median and the mean to further reduce the overall prediction error, minimize the degree of waste of power resources or insufficient power supply, and further improve the accuracy of the target load prediction corresponding to the power dispatch, thereby improving the accuracy of power dispatch.

[0058] The embodiment of the present invention converges and constrains the initial target load corresponding to the power dispatch through the average value of the historical dispatched power load corresponding to the power dispatch, and can further improve the prediction accuracy of the target load corresponding to the power dispatch on the basis of targeted prediction based on all preset power business tasks, avoid the problem of waste of power resources or insufficient power supply, thereby further improving the accuracy of the target load corresponding to the power dispatch, so as to cope with the power demand and load conditions in a complex and changeable environment, and improve the accuracy of power dispatch. It can realize the reasonable scheduling and optimization of power resources in a complex and changeable power demand environment, and improve the overall utilization rate of power resources.

[0059] It should be noted that the power dispatching and processing methods based on multi-task collaboration described in the above embodiments can recombine the technical features contained in different embodiments as needed to obtain a combined implementation plan, but they are all within the scope of protection required by the present invention.

[0060] See also Figure 6 , Figure 6 A schematic block diagram of a power dispatch processing system based on multi-task collaboration provided by an embodiment of the present invention. Corresponding to the above-mentioned power dispatch processing method based on multi-task collaboration, an embodiment of the present invention also provides a power dispatch processing system based on multi-task collaboration. Figure 6 As shown, the power dispatch processing system based on multi-task collaboration includes a module for executing the above-mentioned power dispatch processing method based on multi-task collaboration. The power dispatch processing system based on multi-task collaboration can be configured in a computer device. Specifically, please refer to Figure 6The power dispatching 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.

[0061] The initial determination module 61 is used to determine a number of preset power business tasks; A first determining module 62 is configured to determine a target power network corresponding to the preset power service task and determine whether the target power network includes a faulty node; A second determining module 63 is configured to determine a 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; a third determining module 64, 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 based on the historical power load data and the power load benchmark; A first prediction module 65 is 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 determining module 66 is configured to determine a target load for the power dispatching based on all the expected loads; The scheduling module 67 is used to perform power scheduling according to the target load.

[0062] In one embodiment, the third determining module 64 includes: A first calculation submodule is configured to calculate a mean value of all the historical power load data and use the mean value as a power load benchmark corresponding to the target power network; Alternatively, the sorting submodule is configured to sort all the historical power load data in ascending order, and use the historical power load data with the smallest value as the power load benchmark corresponding to the target power network.

[0063] In one embodiment, the third determining module 64 includes: A first determining submodule is configured to determine a 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; A second determining submodule is configured to determine a minimum ratio value and a maximum ratio value included in all the change ratios, and to combine the minimum ratio value and the maximum ratio value into a ratio range to obtain an adjustment ratio range; The third determining submodule is configured to determine an adjustment range of the power load of the target power network according to the adjustment ratio range.

[0064] In one embodiment, the third determining submodule includes: A second calculation submodule is used to calculate the mean of all the historical power load data; a statistical submodule, configured to count the number of historical power load data greater than the mean 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 less than the mean value to obtain a second quantity value; a third calculation submodule, configured to calculate a ratio of the first quantity value to the second quantity value; The first adjustment submodule is configured to calculate a 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 an adjustment amplitude of the power load corresponding to the target power network.

[0065] In one embodiment, the first adjustment submodule includes at least one of the following: A first increasing submodule is configured to increase the median of the ratio by a ratio corresponding to the ratio when the ratio is greater than 1; A first reducing submodule is configured to reduce the median of the ratio by a ratio corresponding to the ratio when the ratio is less than 1; The assignment submodule is used to assign the adjusted size of the median ratio to zero when the ratio is equal to 1.

[0066] In one embodiment, the multi-task coordination-based power dispatch processing system 60 further includes: A fifth determining module, configured to determine a fault occurrence time corresponding to a fault node when the target power network includes a fault node; An acquisition module, configured to acquire the current time and calculate the time interval between the current time and the time when the fault occurred; A first determining module is configured to determine whether the time interval is greater than or equal to a preset time interval threshold; a sixth determination module, configured to determine, when the time interval is greater than or equal to a preset time interval threshold, 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, wherein the initial historical power load data sequence includes a plurality of initial historical power load data based on the same preset time period, and the fault node historical power load data sequence includes a plurality of fault node historical power load data based on the same preset time period; 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, 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".

[0067] In one embodiment, the multi-task coordination-based power dispatch processing system 60 further includes: The execution module is used to execute the step of "determining the historical power load data sequence of the target power network when the target power network does not include a fault node" when the time interval is less than a preset time interval threshold.

[0068] In one embodiment, the fourth determining module 66 includes: A fourth calculation submodule is configured to calculate the sum of all expected loads corresponding to all the preset power business tasks to obtain an initial target load corresponding to the power dispatch; a fourth determining submodule, configured to determine, based on the preset time period, a number of historical dispatched power loads corresponding to the initial target load; A fifth calculation submodule is used to calculate the average of all historical dispatched power loads to obtain the average of the historical dispatched power loads corresponding to the power dispatch; A first judgment submodule is configured to judge whether the initial target load is equal to the historical dispatch power load average; A second adjustment submodule is configured to adjust the initial target load toward the mean value of the historical dispatch power load when the initial target load is not equal to the mean value of the historical dispatch power load, so as to obtain a target load corresponding to the power dispatch, and the target load is not equal to the mean value of the historical dispatch power load; The fifth determining submodule is configured to use the initial target load as the target load corresponding to the power dispatching when the initial target load is equal to the average value of the historical dispatching power load.

[0069] In one embodiment, the second adjustment submodule includes: a sixth calculation submodule, configured to calculate a difference between the initial target load and the historical dispatch power load average; A second reduction submodule is configured to reduce the initial target load by half of the difference when the initial target load is greater than the average value of the historical dispatched power load, so as to obtain a target load corresponding to the power dispatch; The second increasing submodule is configured to increase the initial target load by half of the difference when the initial target load is less than the average value of the historical dispatched power load, so as to obtain the target load corresponding to the power dispatch. It should be noted that technical personnel in the relevant field 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 refer to the corresponding description in the aforementioned method embodiment. For the convenience and conciseness of the description, it will not be repeated here.

[0070] At the same time, the division and connection methods of the various modules in the above-mentioned power dispatching and processing system based on multi-task collaboration are only used as examples. 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 also be connected in different sequences and methods to complete all or part of the functions of the above-mentioned power dispatching and processing system based on multi-task collaboration.

[0071] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may 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 may 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 read-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 memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0072] 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 the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A power dispatch processing method based on multi-task collaboration, characterized in that: include: Determine a number of preset power business tasks; Determining a target power network corresponding to the preset power service task, and judging whether the target power network includes a faulty node; If the above judgment is no, determining a historical power load data sequence of the target power network, wherein the historical power load data sequence includes 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 based on 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 the expected load of the preset power business task; Determining a target load for power dispatching based on all the expected loads; Power dispatch is performed according to the target load.

2. The power dispatch processing method based on multi-task collaboration according to claim 1, characterized in that: Determining a power load benchmark of the target power network includes: Calculating a mean value of all the historical power load data, and using the mean value as a power load benchmark corresponding to the target power network; Alternatively, all the 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.

3. The power dispatch processing method based on multi-task collaboration according to claim 1 or 2, characterized in that: 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 includes: determining, based on the historical power load data and the power load benchmark, a change ratio of the historical power load of the target power network relative to the power load benchmark; Determine a minimum ratio value and a maximum ratio value included in all the change ratios, and combine the minimum ratio value and the maximum ratio value to form a ratio range to obtain an adjustment ratio range; An adjustment range of the power load of the target power network is determined according to the adjustment ratio range.

4. The power dispatch processing method based on multi-task collaboration according to claim 3, characterized in that: Determining an adjustment range of the power load of the target power network according to the adjustment ratio range includes: Calculating a mean of all the historical power load data; According to all the historical power load data, the number of historical power load data greater than the average value is counted to obtain a first quantity value, and the number of historical power load data less than the average value is counted to obtain a second quantity value; calculating a ratio of the first quantity value to the second quantity value; The median value of the ratio corresponding to the adjustment ratio range is calculated, and the median value of the ratio is adjusted according to the ratio to obtain the adjustment range of the power load corresponding to the target power network.

5. The power dispatch processing method based on multi-task collaboration according to claim 4, characterized in that: According to the ratio, the median ratio is adjusted, including at least one of the following: When the ratio is greater than 1, the median of the ratio is increased by the ratio corresponding to the ratio; When the ratio is less than 1, the median of the ratio is reduced by the ratio corresponding to the ratio; When the ratio is equal to 1, the magnitude of the proportional median adjustment is assigned a value of zero.

6. The power dispatch processing method based on multi-task collaboration according to claim 1 or 2, characterized in that: The method further comprises: In a case where the target power network includes a fault node, determining a fault occurrence time corresponding to the fault node; Obtaining the current time and calculating the time interval between the current time and the time when the fault occurred; Determining whether the time interval is greater than or equal to a preset time interval threshold; When the time interval is greater than or equal to a 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 includes a plurality of initial historical power load data based on the same preset time period, and the fault node historical power load data sequence includes a plurality of fault node historical power load data based on the same preset time period; Based on the initial historical power load data sequence and the fault node historical power load data sequence, the corresponding fault node historical power load data is removed from the initial historical power load data to obtain the historical power load data sequence corresponding to the target power network, and the step of "determining the power load benchmark of the target power network" is performed.

7. The power dispatch processing method based on multi-task collaboration according to claim 6, characterized in that: The method further comprises: When the time interval is less than the preset time interval threshold, the step of "determining a historical power load data sequence of the target power network when the target power network does not include a faulty node" is performed.

8. The power dispatch processing method based on multi-task collaboration according to claim 1 or 2, characterized in that: Determining the target load of the power dispatch according to all the expected loads includes: Calculating the sum of all expected loads corresponding to all the preset power business tasks to obtain the initial target load corresponding to the power dispatch; determining, according to the preset time period, a number of historical dispatched power loads corresponding to the initial target load; Calculate the average of all historical dispatch power loads to obtain the average of the historical dispatch power loads corresponding to the power dispatch; Determining whether the initial target load is equal to the historical dispatch power load average; When the initial target load is not equal to the average value of the historical dispatched power load, the initial target load is adjusted toward the average value of the historical dispatched power load 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; When the initial target load is equal to the average value of the historical dispatched power load, the initial target load is used as the target load corresponding to the power dispatch.

9. The power dispatch processing method based on multi-task collaboration according to claim 8, characterized in that: When the initial target load is not equal to the average value of the historical dispatched power load, adjusting the initial target load toward the average value of the historical dispatched power load to obtain the target load corresponding to the power dispatch, including: Calculating the difference between the initial target load and the historical dispatch power load average; When the initial target load is greater than the average value of the historical dispatched power load, reducing the initial target load by half of the difference to obtain the target load corresponding to the power dispatch; In the case where the initial target load is less than the average value of the historical dispatched power load, the initial target load is increased by half of the difference to obtain the target load corresponding to the power dispatch.

10. A power dispatching and processing system based on multi-task collaboration, characterized in that: include: An initial determination module, used to determine a number of preset power business tasks; A first determining module is configured to determine a target power network corresponding to the preset power service task and determine whether the target power network includes a faulty node; A second determining module is configured to determine a 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; a third determining 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 based on the historical power load data and the power load benchmark; A first prediction module is 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 determining module, configured to determine a target load for the power dispatching according to all the expected loads; The scheduling module is used to perform power scheduling according to the target load.

Images