Power-related Appeal Task Allocation Method

Abstract

The present invention discloses a method for allocating power-related appeal tasks, specifically related to the field of power grid management, and is used to solve the problems of resource optimization and balance in the allocation of power-related appeal tasks. First of all, the segmented adaptive filtering algorithm effectively removes seasonal interference in historical task data, making the data more stable, thereby clearly revealing the long-term trend of resource allocation and laying a scientific analysis foundation. Subsequently, the transient pressure absorption potential index is used to evaluate the resource adaptability and elasticity of each grid in coping with sudden tasks, ensuring the rapid response and stability of resource allocation in a high-pressure environment. The structural resource allocation bias index further identifies and quantifies potential biases and imbalances in resource allocation, providing a basis for optimization. Finally, through the generation of the spiral resonance index, the rationality and coordination of resource allocation in each grid are comprehensively evaluated, significantly improving the efficiency of power-related appeal task allocation and the stability of the system.

Description

Technical Field

[0001] The present invention relates to the field of power grid management, and more specifically, to a method for allocating power-related demand tasks. Background Art

[0002] In power grid management, the resource allocation of each grid needs to accurately reflect the actual demand and pressure to ensure the stability and efficiency of the system. Generally, the allocation of grid resources involves comprehensive consideration of multiple parameters, such as task load, response time, resource availability, etc. However, the existing resource allocation methods have deficiencies in the analysis of the correlation between multiple parameters. Especially when facing power demand fluctuations, it is difficult to adjust resource allocation in real time, resulting in low resource utilization of each grid and failure to fully meet the requirements of a complex power environment.

[0003] In the process of resource allocation for each grid in the prior art, static or single-dimensional parameter optimization methods are usually adopted, lacking in-depth analysis of the balance of resource allocation among grids. Especially in the case of sudden tasks, the phenomenon of local imbalance in resource allocation is more prominent. This deficiency leads to excessive or insufficient resource allocation in some grids in specific situations, unable to reasonably match the actual task requirements, thus reducing the response efficiency and service quality of the overall power system, and ultimately affecting the long-term stability and adaptability of the grid.

[0004] To solve the above problems, a technical solution is provided now. Summary of the Invention

[0005] To overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide a method for allocating power-related demand tasks, which realizes accurate evaluation and optimization of power grid resource allocation through the innovative combination of a segmented adaptive filtering algorithm, a transient pressure absorption potential index, and a structural resource allocation bias index. First, the segmented adaptive filtering algorithm effectively removes the seasonal components in the historical task data, making the data stable, thereby revealing the long-term trend of resource allocation and providing reliable basic data support. Then, the transient pressure absorption potential index is used to evaluate the resource adaptability and elasticity of each grid under sudden task pressure, ensuring the stability and efficiency of resource allocation in a high-pressure environment. At the same time, the structural resource allocation bias index is used to analyze the degree of bias in resource allocation among grids, revealing potential imbalance problems. Finally, a spiral resonance index is comprehensively generated, combined with multi-dimensional space mapping technology, to systematically evaluate the balance of resource allocation for each grid, and through classification discussion and early warning mechanisms, scientific optimization suggestions are provided to ensure the rationality, adaptability, and balance of resource allocation. Generally speaking, the present invention not only improves the operation efficiency and system stability of the power grid, but also provides a strong guarantee for long-term stable operation, promoting the overall optimization of power services to solve the problems raised in the above background art.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] S1. Collect the historical task data of each grid, apply the seasonal adjustment algorithm to remove the seasonal components in the data, and obtain the complete time series stationary data;

[0008] S2. Based on the complete time series stationary data, calculate the transient pressure absorption potential index by quantifying the absorption ability of the grid for burst tasks, and generate the spiral resonance index in combination with the structural resource allocation bias index. The spiral resonance index is obtained by comprehensive quantification of the two, and analyze the adaptability and elasticity of the grid resource allocation in coping with transient task pressure;

[0009] S3. Obtain the spiral resonance index of each grid and perform standardization processing, construct a resource balance matrix, map it to a multi-dimensional space to calculate the resource allocation balance index, analyze the resource allocation situation of each grid in combination with the spiral resonance index, and generate processing results and warning prompts based on classification discussion to identify and optimize the resource allocation.

[0010] In a preferred embodiment, step S1 includes the following contents:

[0011] S1.1. Collect the historical task data of each grid, segment the historical task data of each grid according to the time series, set the length of each time period as T, and for each segment of data Extract the key features related to resource allocation;

[0012] where represents the time index, represents the serial number of the segment;

[0013] S1.2. Construct an adaptive filter for processing each segment of data;

[0014] The filter transfer function is defined as follows: where is the angular frequency, and are adjustment parameters for controlling the amplitude and phase response of the filter;

[0015] S1.3. Apply the constructed filter to filter each segment of data to obtain the stationary data where represents the Fourier transform, is the inverse Fourier transform;

[0016] S1.4. Concatenate the filtered data of each segment to form the complete time series stationary data.

[0017] In a preferred embodiment, the acquisition logic of the transient pressure absorption potential index is as follows:

[0018] S2a-1. Extract the task pressure of each grid within a time period of length τ from the complete time series stationary data generated in step S1. Measure the transient task pressure of the grid during this period by accumulating the intensity of each task and its corresponding priority weight within the corresponding time period.

[0019] For the transient task pressure P k (τ) of each grid within the time period τ, the measurement is carried out according to the following formula: where λ k,i (t) represents the intensity of task i in the grid at time t, ω k,i (t) represents the priority weight of task i, N k is the total number of tasks in the grid within the time period τ, and τ0 and τ1 are the start and end times of the time period;

[0020] S2a-2. Establish a model of resource absorption capacity by analyzing the availability of resources within the grid and their response efficiency to task pressure.

[0021] Model the resource absorption capacity of each grid. The resource absorption capacity R k (τ) is the response ability of the available resources in the grid to task pressure within the time period τ. The formula is: where ρ k,j (t) represents the availability of resource j in the grid at time t, v k,j (t) represents the response efficiency of resource j, and M k is the total number of resources in the grid within the time period τ;

[0022] S2a-3. Compare the resource absorption capacity with the transient task pressure. Analyze that the pressure absorption potential is the response ability of the resource absorption capacity relative to the transient task pressure to measure the potential of the resource to cope with pressure. By introducing a sine function, further adjust the weight of the pressure absorption capacity at different time points, emphasize the pressure absorption capacity at different time points within the time period, and obtain the transient pressure absorption potential intermediate value through integration to reflect the adaptability of the grid to transient task pressure at different time points;

[0023] The transient pressure absorption potential intermediate value A k (τ) is expressed as follows:

[0024] S2a-4. Normalize the transient pressure absorption potential intermediate values of different grids to obtain the transient pressure absorption potential index.

[0025] In a preferred embodiment, the generation logic of the structural resource allocation bias index is as follows:

[0026] S2b-1, Select the input and output variables of each grid as the basis of the DEA model. The input variable I m,n represents the multi-dimensional structural characteristics of resource allocation, and the output variable represents the completion efficiency of various electricity-related tasks. For each grid, there is an input vector I m ={I m,1 ,I m,2 ,…,I m,N} and an output vector O m ={O m,1 ,O m,2 ,…,O m,K};

[0027] S2b-2, In the DEA model, the efficiency value E m of the grid is the ratio of its output vector to the input vector, expressed as: where, and v n are the weight coefficients of the output and input respectively;

[0028] S2b-3, Calculate the intermediate value of the structural resource allocation bias of the grid through the efficiency value obtained from the DEA model where, represents the similarity of the grid and the reference grid in terms of structural resource allocation, and δ m,q is a binary indicator function. When the grid and the reference grid have a high similarity in the resource allocation structure, δ m,q =1; otherwise δ m,q =0;

[0029] S2b-4, Normalize the calculated intermediate value of the structural resource allocation bias to obtain the structural resource allocation bias index.

[0030] In a preferred embodiment, the transient pressure absorption potential index and the structural resource allocation bias index are comprehensively calculated by the logarithmic spiral mapping method to obtain the spiral resonance index.

[0031] In a preferred embodiment, step S3 includes the following contents:

[0032] S3.1, Standardize the spiral resonance index of each grid, and the standardized spiral resonance index is marked as S;

[0033] S3.2, Construct a resource balance matrix for measuring any two grids o and The degree of resource allocation balance between them. The calculation formula of the balance degree matrix is as follows: Among them, is a regulation parameter, and ∈ is a tiny positive number to avoid division by zero;

[0034] S3.3. Map the resource balance degree matrix into a multi-dimensional space, and construct an equilibrium space model of grid resource allocation through multi-dimensional scaling technology. The coordinates after multi-dimensional mapping are represented as X o , where each grid is mapped to a point in the multi-dimensional space;

[0035] S3.4. In the multi-dimensional space, calculate the resource allocation equilibrium index RBEI for the spatial distribution of all grids. The calculation formula is as follows: Among them, represents the distance between grid o and in the multi-dimensional space, Δ and are regulation parameters, and Z is a normalization factor.

[0036] In a preferred embodiment, S3.5. Use the combination of the spiral resonance index and the resource allocation equilibrium index to analyze the handling of power-related demands for each grid, and represent different resource allocation and demand handling situations by constructing a formula for classification discussion. The specific formula is as follows:

[0037] Among them, f1 and f2 are the thresholds of the spiral resonance index and the resource allocation equilibrium index respectively;

[0038] If the classification result is efficient and balanced, it indicates no warning;

[0039] If the classification result is efficient but unbalanced, it indicates a resource concentration warning;

[0040] If the classification result is inefficient but balanced, it indicates a low resource utilization warning;

[0041] If the classification result is inefficient and unbalanced, it indicates a serious imbalance and inefficiency warning of resource allocation.

[0042] The technical effects and advantages of the power-related appeal task allocation method of the present invention:

[0043] 1. The present invention uses a segmented adaptive filtering algorithm to pre-process the historical task data of each grid, successfully removing the seasonal components in the data, making the data more stable and real. In this way, the long-term trend of resource allocation can be clearly presented, avoiding analysis errors caused by seasonal fluctuations. It not only improves the accuracy of the rationality analysis of resource allocation, but also provides reliable basic data support for optimizing resource allocation plans. Especially in a changing power demand environment, ensuring the stability of the data can help decision makers more scientifically identify potential problems in resource allocation, and then formulate more targeted and effective optimization strategies, ultimately improving the operating efficiency and service quality of the overall power supply grid.

[0044] 2. The present invention accurately evaluates the resource adaptability and elasticity of each grid under sudden task pressure through the calculation of the transient pressure absorption potential index, ensuring that resource allocation can respond quickly and maintain stability in a high-pressure environment. This enables the power grid to have stronger resilience and adaptability when responding to emergencies, avoiding system instability caused by insufficient or improper resource allocation. At the same time, the structural resource allocation bias index identifies and quantifies the degree of bias in resource allocation between grids through multi-dimensional structural analysis, reveals potential imbalances in resource allocation, and provides a scientific basis for resource optimization. By integrating these two indexes to generate a spiral resonance index, a comprehensive evaluation and optimization of grid resource allocation is achieved, the overall operational efficiency of the power grid is improved, and the stability and continuous service capabilities of the system are enhanced, thereby ensuring the rationality and efficiency of resource allocation and promoting the comprehensive optimization and long-term stable operation of power services.

[0045] 3. The present invention realizes a comprehensive evaluation of the resource allocation balance of each grid through multi-dimensional space mapping. The constructed resource balance matrix amplifies the phenomenon of uneven resource allocation, so that the resource allocation differences can be sensitively captured. By calculating the resource allocation balance index, the resource allocation differences between grids can be clearly identified, providing data support for the reasonable redistribution of resources. Combining the classification discussion of the spiral resonance index and the resource allocation balance index, it can effectively identify and warn of potential problems in resource allocation, and provide targeted optimization suggestions, thereby ensuring that the power grid system maintains the efficiency and balance of resource allocation when facing complex environments, and improves the stability and responsiveness of the overall system. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a flowchart of the method for allocating electricity-related demand tasks of the present invention;

[0047] Figure 2 This is a schematic diagram of step 3 in the method for allocating tasks related to electricity demands of the present invention. DETAILED DESCRIPTION

[0048] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0049] Embodiment 1

[0050] Figure 1 A method for allocating power-related demand tasks of the present invention is provided, including:

[0051] S1. Collect historical task data of each grid, apply a seasonal adjustment algorithm to remove the seasonal components in the data, and obtain complete time series stationary data;

[0052] S2. Based on the complete time series stationary data, by quantifying the absorption capacity of the grid for sudden tasks, calculate the transient pressure absorption potential index, combine it with the structural resource allocation bias index to generate a spiral resonance index, and comprehensively quantify the two to obtain the spiral resonance index, and analyze the adaptability and elasticity of the grid resource allocation in coping with transient task pressure;

[0053] S3. Obtain the spiral resonance index of each grid and perform standardization processing, construct a resource balance matrix, map it to a multi-dimensional space to calculate the resource allocation balance index, analyze the resource allocation situation of each grid in combination with the spiral resonance index, and generate processing results and warning prompts based on classification discussion to identify and optimize the resource allocation.

[0054] In power grid management, the rationality of resource allocation is crucial for ensuring the efficiency of power supply services. With the change of power demand, the resource usage situations among grids often show complex seasonal fluctuations and long-term trends. These changes are not only affected by external environments (such as seasons and weather), but also closely related to the fluctuations in the internal task volume of the grid and the completion time limit. However, failure to fully identify and remove these fluctuations may lead to inaccurate resource allocation analysis, thereby affecting the scientific nature of decision-making.

[0055] Therefore, before analyzing the rationality of resource allocation, it is necessary to deeply preprocess the historical task data of each grid to remove the seasonal components in the data and reveal the potential long-term trends. This process is not only a prerequisite for accurately analyzing whether the resource allocation is reasonable, but also an important guarantee for optimizing the resource allocation plan of the power supply grid and improving the overall operation efficiency.

[0056] To achieve this goal, a segmented adaptive filtering algorithm is introduced into this scenario. By finely processing the historical task data, the stationarity and authenticity of the data are ensured, laying a solid foundation for subsequent resource allocation analysis.

[0057] Step S1 includes the following contents:

[0058] S1.1, Collect historical task data of each grid, including indicators such as the number of work orders, task completion duration, resource input volume, task priority, work order completion rate, and customer satisfaction. Segment the historical task data of each grid according to the time series, and set the length of each time period as T, where the selection of T should be determined according to the periodic characteristics of task execution within the grid. For each segment of data (where represents the time index, represents the serial number of the segment), extract key features related to resource allocation, including task volume, task completion deadline, resource usage, etc. Through segment processing, the local features of resource allocation within each time period can be captured, helping to analyze the rationality of resource allocation more accurately.

[0059] S1.2, Construct an adaptive filter for processing each segment of data. The output of the filter represents the smoothed data. The transfer function of the filter is adaptively adjusted according to the characteristics of each segment of data to remove seasonal fluctuations and enhance the stability of the long-term trend. The filter transfer function is defined as follows: where, is the angular frequency, and are adjustment parameters used to control the amplitude and phase response of the filter. These parameters need to be adaptively adjusted to accurately match the seasonal characteristics in the data of different grids.

[0060] The adjustment parameters and are determined based on the characteristics of the resource allocation data within the current time period. The expression is set as follows: where, is the spectral representation of each segment of data, is the adjustment parameter. These parameters are adaptively optimized to adapt to the resource allocation characteristics of different grids, ensuring that the filter can effectively remove the seasonal components and retain the core trend of resource allocation.

[0061] S1.3, Apply the constructed filter to filter each segment of data to obtain the smoothed data where, represents the Fourier transform, is the inverse Fourier transform. After filtering, the seasonal fluctuations in the data have been removed, and the stationarity has been significantly enhanced, making the long-term trend of resource allocation clearer and providing a more reliable basis for subsequent rationality analysis.

[0062] S1.4. Concatenate the filtered data for each segment to form complete time - series stationary data. This process ensures the continuity of data in each time period and eliminates possible boundary effects. Through reconstruction, the integrated data can accurately reflect the long - term trend of resource allocation, helping to discover potential imbalance problems.

[0063] Through the implementation of step S1, the present invention pre - processes the historical task data of each grid using a segmented adaptive filtering algorithm, successfully removing the seasonal components in the data, making the data more stable and real. In this way, the long - term trend of resource allocation is clearly presented, avoiding the analysis errors caused by seasonal fluctuations. It not only improves the accuracy of the rationality analysis of resource allocation but also provides reliable basic data support for optimizing the resource allocation plan. Especially in a changing power demand environment, ensuring the stability of data can help decision - makers more scientifically identify potential problems in resource allocation, and then formulate more targeted and effective optimization strategies, ultimately improving the overall operation efficiency and service quality of the power supply grid.

[0064] In power grid management, with the increasing uncertainty of power demand and the frequency of sudden tasks, the accuracy and dynamic adaptability of resource allocation have become key factors in ensuring the stability and efficiency of the power system. Traditional resource allocation assessment methods focus more on long - term stability and often ignore the direct impact of sudden events in the short term on grid operation, resulting in resource allocation strategies being unable to respond to real - time demands in a timely manner. In this context, how to accurately measure the resource adaptation ability of each grid in the face of sudden tasks and identify possible structural biases in resource allocation has become the core challenge in the optimized management of the power system.

[0065] The foreground of step S2 lies in that by calculating the transient pressure absorption potential index, it deeply evaluates the immediate response ability of each grid's resource allocation in a high - pressure environment, thereby revealing the elasticity and adaptability of resources. At the same time, the structural resource allocation bias index accurately locates possible biases and imbalance problems in each grid's resource allocation through multi - dimensional structural analysis, ensuring that resource allocation can meet short - term demands while maintaining overall long - term stability. In this process, the generation of the spiral resonance index further integrates the resource adaptability and structural rationality of each grid, providing a comprehensive evaluation tool for managers. It can not only monitor the effect of resource allocation in real time but also optimize resource allocation strategies in different scenarios, improving the overall operation efficiency of the power grid and the system's response ability, thus ensuring that the system can operate continuously, stably, and efficiently in the face of an increasingly complex power demand environment.

[0066] Step S2 includes the following:

[0067] In the context of power grid management, the resource allocation of each grid directly affects the ability to respond to emergency tasks. When there is a sudden increase in power demand or an emergency task suddenly arrives, how to respond to these transient task pressures quickly and efficiently becomes an important criterion for measuring the rationality of grid resource allocation. Traditional resource evaluation methods focus more on long-term stability. However, in actual operation, short-term task peaks and emergencies often have a significant impact on the operation of the grid. Therefore, it is particularly important to establish an indicator that can accurately reflect the ability of each grid to respond to emergency tasks in the short term. The transient pressure absorption potential index came into being in the context of this demand. It aims to help identify and optimize resource allocation and improve the overall resilience of the grid by quantifying the grid's ability to absorb task pressure in a short period of time.

[0068] The logic for obtaining the transient pressure absorption potential index is:

[0069] Based on the complete time series stabilized data obtained in step S1, the calculation process of the transient pressure absorption potential index is as follows. This index is intended to measure the tolerance and absorption capacity of resource allocation of each grid when facing sudden task pressure.

[0070] S2a-1, extract the task pressure of each grid within a time period of length τ from the smoothed data of the complete time series generated in step S1, which represents the pressure window of the burst task in a short time. By accumulating the intensity of each task in the corresponding time period and its corresponding priority weight, the transient task pressure of the grid during this period is measured. The task intensity reflects the urgency and workload of the task, while the priority weight indicates the importance of each task in the entire task set. This process aims to accurately capture the total pressure that the grid is subjected to in the short term.

[0071] For each grid in the time period τ, the transient task pressure P k (τ) is measured, and the formula is as follows:

[0072]

[0073] Among them, λ k,i (t) represents the intensity of task i in the grid at time t, ω k,i (t) represents the priority weight of task i, N k is the total number of tasks in the grid within the time period τ, τ0 and τ1 are the start and end times of the time period.

[0074] S2a-2. Establish a model of resource absorption capacity by analyzing the availability of resources within the grid and their response efficiency to task pressure. Availability describes the real-time state of resources within the pressure window, while response efficiency represents the effectiveness of resources in coping with task pressure. Integrate these factors to calculate the overall resource absorption capacity of the grid during this time period. This model can reflect the adaptability of resource allocation to task pressure.

[0075] Model the resource absorption capacity of each grid. The resource absorption capacity R k (τ) is the response ability of available resources in the grid to task pressure during time period τ. The formula is:

[0076] where ρ k,j (t) represents the availability of resource j in the grid at time t, and v k,j (t) represents the response efficiency of resource j, and M k is the total number of resources in the grid during time period τ.

[0077] Obtain the resource absorption capacity of the grid during a given time period by performing a time integral on the availability and response efficiency of resources. This calculation takes into account the response effects of resource availability and response efficiency on task pressure.

[0078] S2a-3. Compare the resource absorption capacity with the transient task pressure. Analyze the pressure absorption potential, which is the response ability of the resource absorption capacity relative to the transient task pressure, to measure the potential of resources to cope with pressure. By introducing a sine function, further adjust the weights of the pressure absorption capacity at different time points, emphasizing the pressure absorption capacity at different time points within the time period. Obtain the intermediate value of the transient pressure absorption potential through integration, reflecting the adaptability of the grid to transient task pressure at different time points. This adjustment ensures that within the pressure window, the resource absorption capacity can dynamically adapt to changes in task pressure and form an overall evaluation of the pressure absorption potential.

[0079] The intermediate value A k (τ) of the transient pressure absorption potential is expressed as follows:

[0080] S2a-4. Normalize the intermediate values of the transient pressure absorption potential for different grids to make the indices comparable between different grids and obtain the transient pressure absorption potential index. This normalization process ensures that the evaluation of pressure absorption potential can be compared on a unified basis by adjusting the indices of all grids to the same scale range. The normalized index is not only easy to understand and analyze but also can more effectively reveal the differences in resource allocation and potential problems between different grids.

[0081] The transient pressure absorption potential index is used to indicate the resource response capacity and adaptability of each grid under the pressure of short-term sudden tasks. This index reflects the effective absorption capacity of the existing resource configuration and utilization efficiency of the grid when facing transient high-pressure tasks. The larger the index, the stronger the absorption potential of the grid's resource configuration in dealing with sudden tasks, and the more effective and resilient the transient task pressure can be. On the contrary, the smaller the index, the weaker the grid's resource configuration is in dealing with sudden tasks, and the resource absorption capacity is insufficient. There may be unreasonable resource configuration or low resource redundancy, and the resource allocation strategy needs to be further optimized to improve the overall resilience of the grid.

[0082] The transient pressure absorption potential index provides a new evaluation dimension for the rationality of resource allocation by quantitatively evaluating the resource absorption capacity of each grid when facing short-term emergency tasks. The higher the index, the stronger the task handling capability of the grid in emergency situations, the more reasonable resource allocation and sufficient flexibility. By applying this index, not only can grids that perform poorly under pressure be identified and their resource allocation strategies be optimized in a timely manner, but also the continuity and stability of power services can be ensured during peak hours or emergencies. Ultimately, the application of this index will effectively improve the operational efficiency and service quality of the grid and enhance the overall stability and resilience of the power system.

[0083] In the grid management of electric power, the structural rationality of resource allocation is crucial to improving the overall operational efficiency. As the types of tasks and resource requirements faced by each grid continue to change, how to ensure that the structure of resource allocation can scientifically match the task requirements and avoid waste or shortage of resources has become the focus of managers. Traditional resource allocation analysis often focuses on efficiency in a single dimension, while ignoring the coordination between multi-dimensional resource allocation structures. In order to more accurately identify structural deviations in resource allocation, the structural resource allocation bias index came into being. This index helps managers accurately judge whether resource allocation is reasonable by analyzing the degree of efficiency deviation of each grid in the resource allocation structure, and provides strong support for optimizing resource allocation strategies.

[0084] The logic for generating the structural resource allocation bias index is as follows:

[0085] S2b-1, select the input and output variables of each grid as the basis of the DEA model, input variable I m,n Represents the multi-dimensional structural characteristics of resource allocation, such as manpower, equipment, and capital investment. Output variables It represents the efficiency of completing various power-related tasks, such as task response speed, task success rate, etc. The specific input and output selection needs to be defined according to the actual resource configuration and task requirements of the grid. For each grid, there is an input vector Im = {I m,1 , I m,2 , …, I m,N} and the output vector O m = {O m,1 , O m,2 , …, O m,K}.

[0086] By quantifying different dimensions of grid resource allocation, specific values of each dimension are extracted as inputs. Meanwhile, by quantifying different dimension indicators of task completion, output data is obtained. These data constitute the basic input-output dataset of the DEA model.

[0087] S2b-2. In the DEA model, the efficiency value E of the grid m is the ratio of its output vector to the input vector, expressed as:

[0088] where and v n are the weight coefficients of output and input respectively, which need to be optimized by the linear programming method. The goal of the model is to find the optimal weight coefficients to maximize the efficiency value of the grid among all grids without exceeding the efficiency value of other grids.

[0089] By the linear programming method, the weight coefficients of input-output variables are optimized to obtain the efficiency value of each grid. During the optimization process, it is necessary to ensure that the efficiency value of each grid is less than or equal to 1, so that the model can effectively evaluate the resource allocation efficiency of each grid.

[0090] S2b-3. Calculate the median value of the structural resource allocation bias of the grid based on the efficiency value obtained from the DEA model

[0091] where represents the similarity between the grid and the reference grid in terms of structural resource allocation, and δ m,q is a binary indicator function. When the grid and the reference grid have a high similarity in the resource allocation structure, δ m,q = 1; otherwise δ m,q = 0.

[0092] Quantify the degree of resource allocation bias by calculating the weighted difference between the grid efficiency value and the structural similarity. If a grid is highly similar to the reference grid in terms of resource allocation structure, its median value of structural resource allocation bias should reflect the efficiency difference between it and other similar grids. Finally, the larger the median value of structural resource allocation bias, the more biased the resource allocation structure is and further adjustment is needed.

[0093] S2b-4. Normalize the calculated median value of the structural resource allocation bias so that the median value is comparable among different grids, and obtain the structural resource allocation bias index. The normalized index enables the bias degree of each grid to be compared under the same benchmark, thus providing more targeted adjustment suggestions for decision-making.

[0094] Through normalization, the bias index is adjusted to a unified scale, making the evaluation criteria consistent among different grids. The normalized index is not only easy to understand and analyze, but also can effectively reveal the structural deviations of resource allocation in each grid and guide corresponding optimization adjustments.

[0095] The structural resource allocation bias index is used to represent and reflect the degree of deviation of each grid from the ideal or reference structure in the resource allocation structure. This index reveals whether the resource allocation is reasonable and balanced by quantifying the efficiency differences in multi-dimensional input and output of grid resource allocation. The larger the index, the greater the bias in the resource allocation structure of the grid. Compared with other similar grids, the irrationality of resource allocation is more significant, which may lead to resource waste or shortage and requires adjustment and optimization. The smaller the index, the more reasonable the resource allocation structure of the grid, maintaining consistency and coordination with the reference grid, with higher resource utilization efficiency and basically achieving the ideal allocation state.

[0096] The structural resource allocation bias index provides a new tool for identifying and optimizing the rationality of resource allocation by quantifying the structural deviations in multi-dimensional resource allocation of each grid. The higher the index, the more significant the bias in the resource allocation structure of the grid, which may lead to inefficient use or uneven distribution of resources and requires further optimization to improve the overall efficiency. By applying this index, managers can more effectively discover the irrationality in resource allocation, make timely adjustments, ensure the balance and coordination of resource allocation in each grid, ultimately improve the stability and response speed of power services, and optimize the overall grid operation efficiency.

[0097] The transient pressure absorption potential index and the structural resource allocation bias index are comprehensively calculated through the logarithmic spiral mapping method to obtain the spiral resonance index. For example, it is defined as follows:

[0098] LSRI represents the spiral resonance index.

[0099] A m : The transient pressure absorption potential index, which represents the resource response ability and its adaptability of the grid when facing sudden task pressure, and reflects the effectiveness of resources in coping with high-pressure tasks in the short term.

[0100] B m: Structural resource allocation bias index, which represents the deviation degree of grid resource allocation relative to the ideal or reference configuration, and reveals the structural rationality and balance of resource allocation.

[0101] θ m : The angle defined by the transient pressure absorption potential index and the structural resource allocation bias index, which reflects the relative relationship between the transient pressure absorption capacity and the structural bias of resource allocation, and embodies the mapping positions of the two on the spiral curve.

[0102] Through logarithmic spiral curve mapping, the transient pressure absorption potential index and the structural resource allocation bias index are integrated to form a dynamic assessment of the uneven grid resource allocation.

[0103] The spiral resonance index is used in the logarithmic spiral mapping method to represent and reflect the adaptability and elasticity of grid resource allocation in response to transient task pressure. Specifically, it reflects the balance degree between the flexibility of resource allocation and the structural rationality. The larger the spiral resonance index, the stronger the adaptability and elasticity of the grid in resource allocation, and it can effectively absorb pressure in sudden tasks and maintain the rationality of resource allocation; the smaller it is, the weaker the adaptability and elasticity of grid resource allocation, and there may be a large response deficiency or structural deviation in resource allocation when facing sudden tasks, and it is necessary to further optimize resource allocation to improve the overall strain capacity and allocation efficiency.

[0104] Through the calculation of the transient pressure absorption potential index, the present invention accurately evaluates the resource adaptability and elasticity of each grid under sudden task pressure, ensuring that resource allocation can quickly respond and maintain stability in a high-pressure environment. It enables the power grid to have stronger resilience and strain capacity when dealing with emergencies, and avoids system instability caused by insufficient or improper resource allocation. At the same time, through multi-dimensional structure analysis, the structural resource allocation bias index identifies and quantifies the bias degree of resource allocation among grids, reveals the potential imbalance in resource allocation, and provides a scientific basis for resource optimization. By integrating these two indexes to generate the spiral resonance index, a comprehensive evaluation and optimization of grid resource allocation are achieved, the overall operation efficiency of the power grid is improved, the stability and continuous service capacity of the system are enhanced, thereby ensuring the rationality and high efficiency of resource allocation, and promoting the overall optimization and long-term stable operation of power services.

[0105] In the power grid management, the balance of resource allocation directly affects the stability and response efficiency of the system. With the changes in power demand and the increase in emergencies, the resource allocation of each grid needs to be both adaptable and flexible while maintaining overall balance to ensure the efficient operation of the system. Traditional resource assessment methods often have difficulty taking these factors into account simultaneously, resulting in over-concentration or shortage of resources in some grids, affecting the long-term stability of the overall system. Step S3 calculates and analyzes the resource allocation balance index of each grid through standardizing the spiral resonance index, constructing the resource balance degree matrix, and multi-dimensional space mapping, and comprehensively evaluates and optimizes the grid resource allocation in combination with the classification discussion formula to ensure reasonable resource allocation and improve the overall operation efficiency of the system.

[0106] As Figure 2 shown, step S3 includes the following:

[0107] S3.1, Standardize the spiral resonance index of each grid to ensure that the indexes of each grid are compared within the same scale range. The standardized spiral resonance index is marked as S.

[0108] S3.2, Construct the resource balance degree matrix used to measure the resource allocation balance degree between any two grids o and . The calculation formula of the balance degree matrix is:

[0109] where is the adjustment parameter, and ∈ is a small positive number to avoid division by zero.

[0110] This formula magnifies the phenomenon of unbalanced resource allocation by introducing the logarithmic function and the product term, enabling the differences in resource allocation between adjacent grids to be sensitively captured. The larger the element in the matrix, the worse the resource allocation balance degree between these two grids.

[0111] S3.3, Map the resource balance degree matrix into a multi-dimensional space, and construct an equilibrium space model of grid resource allocation through multi-dimensional scaling technology (MDS). The coordinates after multi-dimensional mapping are represented as X o , where each grid is mapped to a point in the multi-dimensional space.

[0112] The multi-dimensional scaling technology minimizes the difference between the mapped distance and the elements of the original balance degree matrix, so that the distance between the mapped points can accurately reflect the balance of resource allocation of each grid. In this multi-dimensional space, points with closer distances correspond to grids with more balanced resource allocation, while points with farther distances indicate larger differences in resource allocation.

[0113] S3.4. Calculate the resource allocation balance index RBEI for the spatial distribution of all grids in the multi-dimensional space. The calculation formula is as follows:

[0114] where represents the distance between grid o and in the multi-dimensional space, Δ and are adjustment parameters, and Z is the normalization factor.

[0115] The resource allocation balance index is used to represent and reflect the balance degree of each grid in the overall resource allocation, and to reflect the coordination and rationality of the resource distribution among grids. The larger the index, the greater the difference in resource allocation between this grid and other grids, the poorer the balance, and the more likely the resource allocation is unreasonable; the smaller the index, the more consistent the resource allocation of this grid with other grids, the better the balance, and the more reasonable and coordinated the resource allocation.

[0116] S3.5. Use the combination of the spiral resonance index (LSRI) and the resource allocation balance index (RBEI) to analyze the handling of electricity-related demands for each grid, and represent different resource allocation and demand handling situations by constructing a formula for classification discussion. This formula can comprehensively reflect the adaptability, elasticity, and balance of resources. The specific formula is as follows:

[0117] where f1 and f2 are the thresholds of the spiral resonance index and the resource allocation balance index respectively.

[0118] If the classification result is efficient and balanced, for this grid, the resource allocation not only has strong adaptability and elasticity, but also maintains good balance with other grids, can effectively handle electricity-related demands, and the resource allocation is reasonable and efficient. For the overall resource distribution, the overall resource allocation is relatively reasonable, evenly distributed, with good cooperation among grids, and the overall system runs stably. Without warning, continue to maintain the current resource allocation, and it is recommended to monitor regularly to ensure continuous high efficiency.

[0119] If the classification result is efficient but unbalanced, for this grid, although the resource allocation has high adaptability and elasticity and can effectively handle electricity-related demands, compared with other grids, there is an unbalanced phenomenon in the resource allocation, which may lead to over-concentration of some resources. For the overall resource distribution, the overall resource distribution is unbalanced, and there may be problems of over-abundant or insufficient resource allocation in some grids, which may affect the long-term stability of the overall system. Warning prompt "Resource concentration warning", it is recommended to make appropriate adjustments to the resource allocation to prevent over-concentration of local resources and ensure the reasonable distribution and overall balance of resources.

[0120] If the classification result is inefficient but balanced, for this grid, although the resource allocation is balanced, the adaptability and elasticity are weak, and it may not be able to effectively respond to sudden power-related demands, the processing efficiency is low, and the resource allocation needs to be optimized and improved. For the overall resource distribution, the overall resource distribution is balanced, but the resource utilization efficiency of some grids is low, which may affect the overall system's resilience and service quality. The warning prompt is "inefficient resource utilization warning", and it is recommended to improve the resource adaptability and elasticity of the grid, optimize resource allocation, and enhance the ability to respond to sudden demands.

[0121] If the classification result is inefficient and unbalanced, the resource allocation for this grid is not only unbalanced, but also lacks sufficient adaptability and flexibility, and the efficiency of handling electricity-related demands is low, and there are significant problems in resource allocation. For the overall resource distribution, the overall resource distribution is unbalanced, and the resource allocation efficiency of some grids is inefficient. The overall operation risk of the system is high, which may have a negative impact on the service quality. The warning prompt is "Serious imbalance and inefficiency warning of resource allocation", and it is recommended to immediately conduct a comprehensive review and optimization of resource allocation to ensure that resources are reallocated to a reasonable and efficient state, and to improve the stability and responsiveness of the overall system.

[0122] The present invention realizes a comprehensive evaluation of the resource allocation balance of each grid through multi-dimensional space mapping. The constructed resource balance matrix amplifies the phenomenon of uneven resource allocation, so that the resource allocation differences can be sensitively captured. By calculating the resource allocation balance index, the resource allocation differences between grids can be clearly identified, providing data support for the reasonable redistribution of resources. Combining the classification discussion of the spiral resonance index and the resource allocation balance index, it is possible to effectively identify and warn of potential problems in resource allocation, and provide targeted optimization suggestions, thereby ensuring that the power grid system maintains the efficiency and balance of resource allocation when facing complex environments, and improves the stability and responsiveness of the overall system.

[0123] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0124] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

[0125] It should be noted that in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0126] As described above, the above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claimed rights.

Claims

1. The method for allocating tasks related to electricity demands is characterized by: Includes steps: S1, collect historical task data of each grid, apply seasonal adjustment algorithm, and filter the segmented historical task data by constructing an adaptive filter to remove seasonal fluctuations, retain long-term trends, and obtain complete time series stable data; S2, based on the complete time series smoothed data, by quantifying the ratio of the grid's resource absorption capacity to task pressure in a specific time period, the transient pressure absorption potential index is calculated to evaluate the grid's resource adaptability in responding to sudden tasks; at the same time, the efficiency of grid resource allocation is evaluated through the data envelopment analysis model, and combined with the similarity with the reference grid, the structural resource allocation bias index is calculated to quantify the structural bias of resource allocation; then, the transient pressure absorption potential index and the structural resource allocation bias index are integrated through the logarithmic spiral mapping method to generate the spiral resonance index to evaluate the adaptability and elasticity of grid resource allocation; the spiral resonance index is defined as follows: ; represents the helical resonance index; : Transient pressure absorption potential index; : Structural resource allocation bias index; : Angle defined by transient pressure absorption potential index and structural resource allocation bias index; S3, obtain the spiral resonance index of each grid and perform normalization processing, construct a resource balance matrix, calculate the resource allocation balance index through the resource balance matrix and multi-dimensional scaling technology, and evaluate the balance degree of resource allocation between grids; combine the spiral resonance index and the resource allocation balance index to analyze the resource allocation situation of each grid, generate processing results and early warning prompts based on classification discussion, and identify and optimize resource allocation.

2. The method for allocating tasks related to electricity demands according to claim 1 is characterized in that: Step S1 includes the following contents: S1.1, collect the historical task data of each grid, divide the historical task data of each grid into segments according to the time series, and set the length of each time period to , for each piece of data , extract key features related to resource allocation; in Represents the time index, Indicates the sequence number of the segment; S1.2, construct an adaptive filter to process each segment of data; The filter transfer function is defined as follows: ;in, is the angular frequency, and To adjust the parameters, used to control the amplitude and phase response of the filter; S1.3, apply the constructed filter to filter each segment of data to obtain the stabilized data : ,in, represents the Fourier transform, is the inverse Fourier transform; S1.4, concatenate each segment of filtered data to form a complete time series stabilized data.

3. The method for allocating tasks related to electricity demands according to claim 2 is characterized in that: The logic for obtaining the transient pressure absorption potential index is: S2a-1, extract the time period length of each grid from the complete time series smoothed data generated in step S1. The task pressure in the grid is measured by accumulating the intensity of each task and its corresponding priority weight in the corresponding time period, and measuring the transient task pressure of the grid during this period; For each grid in the time period Transient task pressure within To measure, the formula is as follows: ;in, Indicates time Tasks in the time grid The strength of Indicates the task The priority weight of For the grid in the time period The total number of tasks within and The start and end time of the time period; S2a-2, modeling resource absorption capacity by analyzing the availability of resources within the grid and their response efficiency to task pressure; Model the resource absorption capacity of each grid. For the grid in the time period The responsiveness of available resources to task pressure is: ;in, Indicates time Resources in the time grid Availability, Representation Resources The response efficiency, For the grid in the time period The total number of resources within S2a-3, compare resource absorption capacity with transient task pressure, analyze pressure absorption potential as the response capacity of resource absorption capacity relative to transient task pressure, to measure the potential of resources to cope with pressure, and further adjust the weight of pressure absorption capacity at different time points by introducing sine function, emphasizing the pressure absorption capacity at different time points in the time period, and obtain the intermediate value of transient pressure absorption potential by integration, which reflects the adaptability of the grid to transient task pressure at different time points; Median transient pressure absorption potential It is expressed as follows: ; S2a-4, normalize the intermediate values ​​of transient pressure absorption potential of different grids to obtain the transient pressure absorption potential index.

4. The method for allocating tasks related to electricity demands according to claim 3 is characterized in that: The logic for generating the structural resource allocation bias index is as follows: S2b-1, select the input and output variables of each grid as the basis of the DEA model, input variables Represents the multi-dimensional structural characteristics of resource allocation, output variables Represents the completion efficiency of various power-related tasks. For each grid, there is an input vector and the output vector ; S2b-2, in the DEA model, the efficiency value of the grid The ratio of its output vector to its input vector is expressed as: ;in, and are the weight coefficients of output and input respectively; S2b-3, efficiency value obtained by DEA model, calculation of structural resource allocation bias of grid : ;in, represents the similarity between the grid and the reference grid in terms of structural resource configuration, is a binary indicator function. When the grid and the reference grid have a high similarity in resource configuration structure, ;otherwise ; S2b-4, bias in the calculated structural resource allocation After normalization, we get the structural resource allocation bias index.

5. The method for allocating tasks related to electricity demands according to claim 4 is characterized in that: The transient pressure absorption potential index and the structural resource allocation bias index were comprehensively calculated using the logarithmic spiral mapping method to obtain the spiral resonance index.

6. The method for allocating tasks related to electricity demands according to claim 5 is characterized in that: Step S3 includes the following contents: S3.1, the spiral resonance index of each grid is normalized, and the normalized spiral resonance index is marked as ; S3.2, build resource balance matrix , used to measure any two grids and The balance degree of resource allocation between the two, the calculation formula of the balance matrix is: ;in, To adjust the parameters, To avoid division by zero for small positive numbers; S3.3, the resource balance matrix is ​​mapped into multidimensional space, and a grid resource allocation balance space model is constructed through multidimensional scaling technology. The coordinates after multidimensional mapping are expressed as , where each grid is mapped to a multidimensional space point; S3.4, in multidimensional space, calculate the resource allocation balance index for the spatial distribution of all grids , the calculation formula is: ;in, Representation Grid and Distance in multidimensional space, and To adjust the parameters, is the normalization factor.

7. The method for allocating tasks related to electricity demands according to claim 6 is characterized in that: S3.5, the spiral resonance index and resource allocation balance index are combined to analyze the power demand processing situation of each grid, and the different resource allocation and demand processing situations are represented by constructing a classification discussion formula. The specific formula is as follows: in, and They are the thresholds of spiral resonance index and resource allocation balance index respectively; If the classification result is efficient and balanced, there is no warning; If the classification result is efficient but unbalanced, a warning of resource concentration will be prompted; If the classification result is inefficient but balanced, an early warning of inefficient resource utilization is prompted; If the classification result is inefficient and unbalanced, it will prompt a warning of serious imbalance and inefficiency in resource allocation.

Images