Power management optimization method and system for temporary construction areas based on artificial intelligence

Abstract

The present invention discloses a temporary construction area power management optimization method and system based on artificial intelligence, which realizes the intelligent optimization of global power management by integrating equipment energy consumption management, power load management and power supply path optimization modules. The present invention relates to the field of temporary construction area power management technology, specifically refers to a temporary construction area power management optimization method and system based on artificial intelligence. Equipment energy efficiency management adopts an improved dynamic power consumption modeling method, combined with transient characteristics and hybrid models, to accurately identify high-energy-consuming equipment and improve the accuracy of energy efficiency evaluation; power load management utilizes improved weighted clustering and multi-power supply node intelligent agent reinforcement learning technology to optimize load distribution and enhance the system's adaptability; power supply path optimization realizes dynamic adjustment of power supply paths by combining topological graph structure and Dijkstra algorithm, reduces line losses, and ensures stable power supply.

Description

Technical Field

[0001] The present invention relates to the technical field of temporary construction area power management, and specifically to an artificial intelligence-based temporary construction area power management optimization method and system. Background Art

[0002] The AI-based power management optimization method and system for temporary construction areas is a comprehensive technical solution that integrates IoT sensing, big data analysis, and intelligent decision-making. The system deploys a network of intelligent sensors to collect real-time data on current, voltage, and device status, and applies machine learning algorithms to achieve dynamic optimization management. This technology is particularly suitable for temporary power use scenarios such as construction sites and post-disaster resettlement areas, effectively addressing the slow response and low energy efficiency of traditional manual management.

[0003] However, existing power management methods for temporary construction areas often employ a hierarchical management approach, in which equipment energy efficiency management, load management, and power supply path optimization are independent of each other and lack an effective coordination mechanism. This decentralized management approach can easily lead to uneven resource allocation and power system instability. Equipment energy efficiency information is often not promptly integrated with load demand forecasts, resulting in equipment overload during peak load periods and wasted power during low demand periods.

[0004] Existing methods for managing device energy consumption primarily rely on traditional periodic device inspections and static power consumption modeling. This approach often overlooks the impact of transient device behavior and complex load fluctuations, leading to technical issues such as the inability to accurately identify high-energy-consuming devices and difficulty analyzing abnormal device behavior and transient characteristics.

[0005] Existing power load management methods are often based on static load forecasting and conventional load distribution algorithms, which are unable to cope with sudden changes in power demand or the complex requirements of dynamic load distribution. Consequently, traditional methods are unable to cope with load fluctuations and lack intelligent scheduling capabilities.

[0006] In the existing power supply path optimization method, there are technical problems such as untimely path optimization and slow response speed. Summary of the Invention

[0007] In view of the above situation, in order to overcome the defects of the prior art, the present invention provides an artificial intelligence-based temporary construction area power management optimization method and system. In view of the existing temporary construction area power management method, the existing temporary construction area power management system usually adopts a hierarchical management method, in which equipment energy efficiency management, load management and power supply path optimization are independent of each other and lack an effective coordination mechanism. This decentralized management method easily leads to uneven resource allocation and unstable power system. The energy efficiency information of the equipment is usually not combined with the load demand forecast in a timely manner, resulting in the technical problem that the equipment may be overloaded during peak load periods and waste electricity during low demand periods. This solution creatively adopts the integrated intelligent functions of equipment energy consumption management, power load management and power supply path optimization to achieve intelligent optimization of global power management and improve the overall coordination efficiency of the power management system. The system can not only monitor the energy efficiency and load status of the equipment in real time, but also dynamically optimize the power supply path to ensure that the power resources can be optimally configured and scheduled when the equipment load changes and the power demand in the temporary construction area fluctuates, thereby greatly improving the intelligence level of power management and the stability of system operation. In view of the existing equipment energy consumption management method, the existing method mainly relies on traditional equipment periodic inspection and static power consumption modeling. This method The method often ignores the impact of transient behavior of equipment and complex load fluctuations, which leads to technical problems such as the inability to accurately identify high-energy-consuming equipment and difficulty in analyzing abnormal behavior and transient characteristics of equipment. This solution creatively adopts a dynamic power consumption modeling method that combines hybrid models and transient characteristics to manage equipment energy consumption, solving the problem of insufficient accuracy in equipment energy efficiency evaluation. Through the improved transient characteristic loss function, the accuracy in equipment power consumption prediction is improved, making equipment energy efficiency management more accurate and intelligent, thereby reducing energy consumption and failure rate, and improving equipment operation efficiency and stability; in the existing power load management method, there are existing Traditional methods are usually based on static load forecasting and conventional load distribution algorithms, which cannot cope with sudden changes in power demand or the complex needs of dynamic load distribution. Therefore, traditional methods cannot cope with technical problems such as load fluctuations and lack of intelligent scheduling capabilities. This solution creatively adopts a multi-power node intelligent agent reinforcement learning method with improved weighted clustering to achieve dynamic balancing and intelligent scheduling of power loads. By combining multi-dimensional data such as power load forecasting, energy storage status, and equipment priority, power load management has adaptive capabilities and can optimize loads according to real-time power demand, equipment status, and environmental changes, thereby improving the self-recovery capability and overall operating efficiency of the power system.To address the technical issues of delayed optimization and slow response in existing power supply path optimization methods, this solution creatively employs an optimization algorithm that combines standard topology structure power supply path reconstruction to optimize the power supply path, achieving dynamic adaptive adjustment of the power supply path. Furthermore, the use of intelligent switches for power supply circuit switching and the combination of line loss heat maps for visual analysis further reduce power transmission losses and ensure the efficient and stable operation of the power supply system.

[0008] The technical solution adopted by the present invention is as follows: The present invention provides an artificial intelligence-based temporary construction area power management optimization method, which includes the following steps:

[0009] Step S1: data scheduling;

[0010] Step S2: equipment energy consumption management;

[0011] Step S3: Power load management;

[0012] Step S4: power supply path optimization;

[0013] Step S5: Power management in temporary construction areas.

[0014] Furthermore, in step S1, the data scheduling is used to collect, optimize and schedule the data required for power management in the temporary construction area. Specifically, the raw power data is obtained through data collection, and through data processing and partition transmission, the intelligent functions of equipment energy consumption management, power load management and power supply path optimization are supported to obtain the optimized data set for power management in the temporary construction area.

[0015] The data acquisition specifically reads raw data from sensors and device controllers through industrial communication protocols, and collects power data by high-frequency sampling of power parameters;

[0016] The raw power data includes power sensor data, equipment control data, environmental monitoring data and management system data;

[0017] The data processing includes the following steps: anomaly detection, time synchronization, missing value patching and feature extraction; the feature extraction includes harmonic component extraction and transient feature calculation;

[0018] By performing data processing operations on the raw power data, a temporary construction area power management optimization data set is obtained;

[0019] The temporary construction area power management optimization data set includes time-series power parameter data, real-time data stream data, abnormal record data, current harmonic data, load prediction data and cable impedance data;

[0020] By performing partition transmission operations on the temporary construction area power management optimization data set, the time-series power parameter data, abnormal record data and current harmonic data are used for equipment energy consumption management tasks, the time-series power parameter data and system load prediction data are used for power load management tasks, and the time-series power parameter data, real-time data stream data and cable impedance data are used for power supply path optimization tasks.

[0021] Furthermore, in step S2, the device energy consumption management is used to identify high-energy-consuming devices and analyze abnormal behaviors of temporary construction area power equipment. Specifically, based on the temporary construction area power management optimization data set, a dynamic power consumption modeling method combining a hybrid model and transient characteristics is used to perform device energy consumption management and obtain equipment energy efficiency evaluation data, including the following steps:

[0022] Step S21: Enhanced preprocessing, specifically extracting current waveforms, voltage waveforms, and equipment metadata from the time-series power parameter data, abnormal record data, and current harmonic data in the temporary construction area power management optimization data set, extracting harmonic decomposition features and transient feature data, performing data reconstruction, and obtaining equipment energy consumption management input data;

[0023] Step S22: extracting mixed device features, specifically by constructing a mixed convolutional long-term and short-term model and optimizing the model structure to obtain a mixed device feature extraction network, and constructing an improved transient feature loss function to perform mixed device feature extraction on the device energy consumption management input data to obtain mixed device feature data;

[0024] The model structure optimization specifically includes sequentially constructing a waveform improvement input layer, a convolutional module, a bidirectional long-short-term module, and a fully connected layer;

[0025] Step S23: Dynamic power consumption modeling, specifically, constructing a dynamic power consumption function to dynamically model and calculate the power consumption of each power device to obtain dynamic power consumption calculation data;

[0026] Step S24: improving device anomaly detection, specifically by performing improved device anomaly detection based on the device hybrid feature data and the dynamic power consumption calculation data through feature reconstruction and anomaly score calculation, obtaining a device anomaly estimate, and establishing a threshold trigger mechanism to mark an anomaly when the device anomaly estimate is greater than a set threshold;

[0027] The feature reconstruction is performed by calculating the current harmonic distortion rate, power factor and transient energy every 10 seconds to reconstruct the frequency feature and obtain reconstructed feature data;

[0028] The anomaly score calculation is specifically performed by constructing an improved isolation forest algorithm, adding transient feature weights to the standard algorithm, and calculating the device anomaly estimation;

[0029] Step S25: Calculating an energy efficiency score, specifically performing a comprehensive calculation of the device energy consumption score based on the device mixed feature data and the dynamic power consumption calculation data to obtain device energy consumption and energy efficiency evaluation data;

[0030] Step S26: device energy consumption management, specifically performing device energy consumption management based on the dynamic power consumption calculation data, the device abnormality estimation data and the device energy consumption and energy efficiency evaluation data to obtain device energy efficiency evaluation data.

[0031] Furthermore, in step S3, the power load management is used to dynamically balance the supply and demand of power load resources. Specifically, based on the temporary construction area power management optimization dataset, a multi-power supply node agent reinforcement learning method with improved weighted clustering is used to perform power load management and obtain reference data for the power load distribution plan, including the following steps:

[0032] Step S31: input data modeling, specifically extracting load forecast data, energy storage status data, and generator fuel quantity data from the temporary construction area power management optimization data set, and defining state space parameters by constructing device priority tags to obtain state space parameter data;

[0033] Step S32: load improved weighted clustering, specifically, performing feature vector reconstruction based on the temporary construction area power management optimization data set and the state space parameter data to obtain equipment reconstructed feature vector data, performing cluster center calculation through improved weighted calculation, and performing dynamic clustering adjustment in combination with the elbow method to obtain load clustering data;

[0034] Step S33: Multi-agent reinforcement learning, specifically, defining a main grid agent, a generator agent, and an energy storage agent to perform multi-agent definition, constructing a standard deep Q network as the agent decision network, defining action space parameters and a power load improvement reward function, and performing multi-agent reinforcement learning based on the state space parameter data to obtain power load optimization strategy data;

[0035] The action space parameters include power-off action, frequency reduction action and full-power operation action;

[0036] The calculation formula of the power load improvement reward function is:

[0037] ;

[0038] Where r t is the power load improvement reward function, is the key load power supply indicator function, used to indicate the power supply stability reward, IC is the key load power supply judgment identifier, F diesel(t) is the generator fuel quantity data, which is used to represent the fuel consumption reward, and SOC(t) is the energy storage state data, which is used to represent the energy storage balance reward;

[0039] Step S34: Dynamically adjust the load priority, specifically by constructing a flexible priority adjustment algorithm to dynamically adjust the load priority. By dynamically adjusting the load priority, resource reallocation after the new device is connected is optimized to obtain dynamic load priority data.

[0040] The elastic priority adjustment algorithm specifically performs dynamic priority adjustment by calculating the priority change;

[0041] Step S35: Power load management, specifically, based on the power load optimization strategy data and dynamic load priority data, by constructing intelligent body power constraint conditions, performing comprehensive power load management, and obtaining power load distribution plan reference data.

[0042] Furthermore, in step S4, the power supply path optimization is used to adaptively adjust the power supply path strategy of the temporary construction area. Specifically, based on the temporary construction area power management optimization dataset, an optimization algorithm combined with the power supply path reconstruction of the standard topology structure is used to optimize the power supply path to obtain the optimal power supply path reference data, including the following steps:

[0043] Step S41: Input data processing, specifically constructing temporary construction area power supply node and edge data to obtain temporary construction area power supply topology data; the temporary construction area power supply node specifically includes power supply points and load points; the edge data specifically uses cable impedance data as edge data;

[0044] Step S42: reconstructing the standard topology map, specifically, obtaining optimized power supply topology map data by constructing an adjacency matrix and performing dynamic weight calculation based on the temporary construction area power supply topology map data;

[0045] Step S43: Construct an optimization algorithm, specifically, starting from the substation, using the standard Dijkstra algorithm to calculate the shortest path to all load nodes, and dynamically adjust the weights by updating them every 5 minutes to obtain optimized path data;

[0046] Step S44: Optimizing the power supply path, specifically, using an intelligent switch to automatically switch the power supply circuit based on the optimized path data, and visually analyzing the line loss heat map to obtain reference data for the power load distribution plan.

[0047] Furthermore, in step S5, the temporary construction area power management is used for global monitoring and systematic optimization, specifically by combining the equipment energy efficiency evaluation data, the power load distribution scheme reference data and the optimal power supply path reference data through data aggregation, to build a temporary construction area power integrated management system based on a rule engine, and according to the abnormality detection in the equipment energy efficiency evaluation data, perform automatic control and manual confirmation, and control the temporary construction area power equipment according to the power load distribution scheme reference data and the optimal power supply path reference data, to obtain the temporary construction area power integrated management reference data;

[0048] The rule engine adopts the Drools engine and matches predefined rules;

[0049] The predefined rules include overload protection and energy storage scheduling.

[0050] The artificial intelligence-based temporary construction area power management optimization system provided by the present invention includes a data scheduling module, an equipment energy consumption management module, a power load management module, a power supply path optimization module and a temporary construction area power management module;

[0051] The data scheduling module is used for data scheduling, obtaining a temporary construction area power management optimization data set through data scheduling, and sending the temporary construction area power management optimization data set to the equipment energy consumption management module, the power load management module and the power supply path optimization module;

[0052] The equipment energy consumption management module is used for equipment energy consumption management, obtains equipment energy efficiency evaluation data through equipment energy consumption management, and sends the equipment energy efficiency evaluation data to the temporary construction area power management module;

[0053] The power load management module is used for power load management, obtains power load distribution scheme reference data through power load management, and sends the power load distribution scheme reference data to the temporary construction area power management module;

[0054] The power supply path optimization module is used to optimize the power supply path, obtain optimal power supply path reference data through power supply path optimization, and send the optimal power supply path reference data to the temporary construction area power management module;

[0055] The temporary construction area power management module is used for temporary construction area power management, and obtains temporary construction area power comprehensive management reference data through temporary construction area power management.

[0056] The beneficial effects achieved by the present invention using the above scheme are as follows:

[0057] (1) In view of the existing power management methods in temporary construction areas, the existing power management systems in temporary construction areas usually adopt a hierarchical management method, in which equipment energy efficiency management, load management and power supply path optimization are independent of each other and lack an effective coordination mechanism. This decentralized management method easily leads to uneven resource allocation and unstable power system. The energy efficiency information of the equipment is usually not combined with the load demand forecast in a timely manner, resulting in the technical problem that the equipment may be overloaded during peak load periods and power is wasted during low demand periods. This solution creatively adopts the integrated intelligent functions of equipment energy consumption management, power load management and power supply path optimization to achieve intelligent optimization of global power management and improve the overall coordination efficiency of the power management system. The system can not only monitor the energy efficiency and load status of the equipment in real time, but also dynamically optimize the power supply path to ensure that power resources can be optimally configured and dispatched when the equipment load changes and the power demand in the temporary construction area fluctuates, thereby greatly improving the intelligence level of power management and the stability of system operation.

[0058] (2) In view of the fact that the existing equipment energy consumption management methods mainly rely on traditional equipment periodic inspection and static power consumption modeling, this method often ignores the impact of equipment transient behavior and complex load fluctuations, which leads to the technical problem of being unable to accurately identify high-energy-consuming equipment and difficult to analyze abnormal behavior and transient characteristics of equipment, this solution creatively adopts a dynamic power consumption modeling method that combines hybrid models and transient characteristics to manage equipment energy consumption, solving the problem of insufficient accuracy in equipment energy efficiency evaluation. Through the improved transient characteristic loss function, the accuracy in equipment power consumption prediction is improved, making equipment energy efficiency management more accurate and intelligent, thereby reducing energy consumption and failure rate, and improving equipment operation efficiency and stability;

[0059] (3) In view of the technical problems that the existing power load management methods are usually based on static load forecasting and conventional load distribution algorithms, which cannot cope with sudden changes in power demand or complex demands of dynamic load distribution, and thus traditional methods cannot cope with load fluctuations and lack intelligent scheduling capabilities, this solution creatively adopts the multi-power node intelligent agent reinforcement learning method with improved weighted clustering to achieve dynamic balance and intelligent scheduling of power loads. By combining multi-dimensional data such as power load forecasting, energy storage status and equipment priority, power load management has adaptive capabilities and can optimize loads according to real-time power demand, equipment status and environmental changes, thereby improving the self-recovery capability and overall operation efficiency of the power system.

[0060] (4) In view of the technical problems of untimely path optimization and slow response speed in the existing power supply path optimization methods, this solution creatively adopts an optimization algorithm combined with the reconstruction of the power supply path of the standard topology structure to optimize the power supply path and realizes the adaptive adjustment of the dynamic power supply path. In addition, the intelligent switch is used to switch the power supply circuit, and the line loss thermal map is combined with the visual analysis to further reduce the loss in the power transmission process and ensure the efficient and stable operation of the power supply system. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 A flow chart of the artificial intelligence-based temporary construction area power management optimization method provided by the present invention;

[0062] Figure 2 A schematic diagram of the artificial intelligence-based temporary construction area power management optimization system provided by the present invention;

[0063] Figure 3 This is a flow chart of equipment energy consumption management in step S2;

[0064] Figure 4 This is a schematic diagram of the process of power load management in step S3;

[0065] Figure 5 A schematic diagram of the process of optimizing the power supply path in step S4.

[0066] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention. DETAILED DESCRIPTION

[0067] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0068] In the description of the present invention, it should be understood that terms such as "upper", "lower", "front", "back", "left", "right", "top", "bottom", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operated in a specific direction. Therefore, they should not be understood as limiting the present invention.

[0069] Example 1, see Figure 1The present invention provides an artificial intelligence-based temporary construction area power management optimization method, which includes the following steps:

[0070] Step S1: data scheduling;

[0071] Step S2: equipment energy consumption management;

[0072] Step S3: Power load management;

[0073] Step S4: power supply path optimization;

[0074] Step S5: Power management in temporary construction areas.

[0075] By performing the above operations, in view of the fact that in the existing temporary construction area power management methods, the existing temporary construction area power management system usually adopts a hierarchical management method, in which equipment energy efficiency management, load management and power supply path optimization are independent of each other and lack an effective coordination mechanism. This decentralized management method easily leads to uneven resource allocation and unstable power system. The energy efficiency information of the equipment is usually not combined with the load demand forecast in a timely manner, resulting in the technical problem that the equipment may be overloaded during peak load periods and power is wasted during low demand periods. This solution creatively adopts the integrated intelligent functions of equipment energy consumption management, power load management and power supply path optimization to achieve intelligent optimization of global power management and improve the overall coordination efficiency of the power management system. The system can not only monitor the energy efficiency and load status of the equipment in real time, but also dynamically optimize the power supply path to ensure that power resources can be optimally configured and dispatched when the equipment load changes and the power demand in the temporary construction area fluctuates, thereby greatly improving the intelligence level of power management and the stability of system operation.

[0076] Example 2, see Figure 1 and Figure 2 This embodiment is based on the above embodiment. In step S1, the data scheduling is used to collect, optimize and schedule the data required for power management in the temporary construction area. Specifically, the raw power data is obtained through data collection, and through data processing and partition transmission, the intelligent functions of equipment energy consumption management, power load management and power supply path optimization are supported to obtain the optimized data set for power management in the temporary construction area.

[0077] The data acquisition specifically reads raw data from sensors and device controllers through industrial communication protocols, and collects power data by high-frequency sampling of power parameters;

[0078] The raw power data includes power sensor data, equipment control data, environmental monitoring data and management system data;

[0079] The power sensing data includes current waveform data, voltage waveform data, power factor data and harmonic distortion rate data;

[0080] The device control data includes device metadata and real-time operating parameter data, wherein the device metadata includes device type, rated power and operating status parameters, and the real-time operating parameter data includes air conditioner set temperature and motor load rate data;

[0081] The environmental monitoring data includes temperature, humidity and light intensity parameters;

[0082] The management system data includes power load forecast values, energy storage status and power transmission topology information;

[0083] The data processing includes the following steps: anomaly detection, time synchronization, missing value patching and feature extraction; the feature extraction includes harmonic component extraction and transient feature calculation;

[0084] By performing data processing operations on the raw power data, a temporary construction area power management optimization data set is obtained;

[0085] The temporary construction area power management optimization data set includes time-series power parameter data, real-time data stream data, abnormal record data, current harmonic data, load prediction data and cable impedance data;

[0086] By performing partition transmission operations on the temporary construction area power management optimization data set, the time-series power parameter data, abnormal record data and current harmonic data are used for equipment energy consumption management tasks, the time-series power parameter data and system load prediction data are used for power load management tasks, and the time-series power parameter data, real-time data stream data and cable impedance data are used for power supply path optimization tasks.

[0087] Example 3, see Figure 1 、 Figure 2 and Figure 3 This embodiment is based on the above embodiment. In step S2, the device energy consumption management is used to identify high-energy-consuming devices and analyze abnormal behaviors of temporary power equipment in the construction area. Specifically, based on the temporary power management optimization data set, a dynamic power consumption modeling method combining a hybrid model and transient characteristics is used to perform device energy consumption management and obtain device energy efficiency evaluation data, including the following steps:

[0088] Step S21: Enhanced preprocessing, specifically extracting current waveforms, voltage waveforms, and equipment metadata from the time-series power parameter data, abnormal record data, and current harmonic data in the temporary construction area power management optimization data set, extracting harmonic decomposition features and transient feature data, performing data reconstruction, and obtaining equipment energy consumption management input data;

[0089] Step S22: extracting mixed device features, specifically by constructing a mixed convolutional long-term and short-term model and optimizing the model structure to obtain a mixed device feature extraction network, and constructing an improved transient feature loss function to perform mixed device feature extraction on the device energy consumption management input data to obtain mixed device feature data;

[0090] The model structure optimization specifically includes sequentially constructing a waveform improvement input layer, a convolutional module, a bidirectional long-short-term module, and a fully connected layer;

[0091] The waveform improvement input layer specifically uses a current waveform of a 200 millisecond window and 10-dimensional harmonic components as input layer dimensions;

[0092] The convolution module specifically uses a one-dimensional convolution layer, sets the convolution model parameters to (kernel=5, filters=32, stride=2), uses a ReLU activation function and sets a maximum pooling layer with a pooling dimension of 2, and outputs convolution feature data of size 32×49;

[0093] The bidirectional long-term and short-term module specifically uses a 64-dimensional hidden layer and outputs a 128-dimensional time series feature vector;

[0094] The fully connected layer receives and fuses the convolution feature data and the time series feature vector, outputs 64-dimensional device mixed feature data, and constructs the device feature mixed extraction network;

[0095] The calculation formula of the improved transient feature loss function is:

[0096] ;

[0097] Where, is the improved transient feature loss function, max(·) is the maximum value function, f(x a ) is the anchor sample feature vector, which is used to represent the feature representation of the target device to be identified at the current moment, f(x p ) is the positive sample feature vector, which is used to represent the feature representation of the same type of equipment as the anchor sample at different time periods, f(x n ) is the negative sample feature vector, which is used to represent the feature representation of devices of different types from the anchor sample, ||·||2 is the L2 norm operator, is the interval parameter, is the transient feature weight coefficient, The overall is the rate of change of the transient feature;

[0098] Preferably, the interval parameter The specific value is 0.5, the transient feature weight coefficient The value range of is set to [0.1, 0.3], and the specific value is selected through cross-validation;

[0099] Step S23: Dynamic power consumption modeling, specifically, constructing a dynamic power consumption function to dynamically model and calculate the power consumption of each power device to obtain dynamic power consumption calculation data;

[0100] The calculation formula of the dynamic power consumption function is:

[0101] ;

[0102] Where, P dev (t) is the output of the dynamic power consumption function, which is used to represent the real-time power consumption of the power equipment at time t, t is the time index, K is the total number of devices, k is the device index, and w k is the device weight coefficient, which is trained using the least squares method. The calculation formula is: ,in, is the update operator, is the learning rate parameter, P meas (t) is the actual measured power of the device, It is a nonlinear mapping function of the equipment state characteristics, specifically using the Gaussian radial basis function, and S(t) is the equipment state parameter, which is used to represent the real-time operating parameter data of the equipment;

[0103] Step S24: improving device anomaly detection, specifically by performing improved device anomaly detection based on the device hybrid feature data and the dynamic power consumption calculation data through feature reconstruction and anomaly score calculation, obtaining a device anomaly estimate, and establishing a threshold trigger mechanism to mark an anomaly when the device anomaly estimate is greater than a set threshold;

[0104] The feature reconstruction is performed by calculating the current harmonic distortion rate, power factor and transient energy every 10 seconds to reconstruct the frequency feature and obtain reconstructed feature data;

[0105] The anomaly score calculation is specifically performed by constructing an improved isolation forest algorithm, adding transient feature weights on the basis of the standard algorithm, and calculating the device anomaly estimation. The calculation formula is:

[0106] ;

[0107] Where Score(x) is the device anomaly estimate, x is the reconstructed feature data, E(·) is the averaging function, h(x) is the path length of the input sample in the isolation tree, c(N) is the normalization factor, and N is the total number of samples in the reconstructed feature data. is the transient feature weight, x trans is the transient energy in the reconstructed feature data;

[0108] Preferably, the transient feature weight The specific value of is set to 0.3;

[0109] The set threshold is specifically set to 0.75;

[0110] Step S25: Calculating an energy efficiency score, specifically performing a comprehensive calculation of the device energy consumption score based on the device mixed feature data and the dynamic power consumption calculation data to obtain device energy consumption and energy efficiency evaluation data;

[0111] The calculation formula for the comprehensive calculation of the equipment energy consumption score is:

[0112] ;

[0113] Where, EfficiencyScore is the energy efficiency evaluation data of the equipment, P dev (t) is the dynamic power consumption function output, P ideal (t) is the device baseline power consumption, P rated is the rated power consumption of the device;

[0114] Step S26: device energy consumption management, specifically performing device energy consumption management based on the dynamic power consumption calculation data, the device abnormality estimation data and the device energy consumption and energy efficiency evaluation data to obtain device energy efficiency evaluation data.

[0115] By performing the above operations, in view of the fact that in the existing equipment energy consumption management methods, the existing methods mainly rely on traditional equipment periodic inspections and static power consumption modeling. This method often ignores the impact of transient behavior of equipment and complex load fluctuations, which leads to the technical problem of being unable to accurately identify high-energy-consuming equipment and difficult to analyze abnormal behavior and transient characteristics of equipment, this solution creatively adopts a dynamic power consumption modeling method that combines hybrid models and transient characteristics to manage equipment energy consumption, solving the problem of insufficient accuracy in equipment energy efficiency evaluation. Through the improved transient characteristic loss function, the accuracy in equipment power consumption prediction is improved, making equipment energy efficiency management more accurate and intelligent, thereby reducing energy consumption and failure rate, and improving equipment operation efficiency and stability.

[0116] Example 4, see Figure 1 、 Figure 2 and Figure 4 This embodiment is based on the above embodiment. In step S3, the power load management is used to dynamically balance the supply and demand of power load resources. Specifically, based on the temporary construction area power management optimization dataset, an improved weighted clustering multi-power supply node intelligent agent reinforcement learning method is used to perform power load management and obtain power load distribution plan reference data, including the following steps:

[0117] Step S31: input data modeling, specifically extracting load forecast data, energy storage status data, and generator fuel quantity data from the temporary construction area power management optimization data set, and defining state space parameters by constructing device priority tags to obtain state space parameter data;

[0118] The calculation formula of the state space parameter data is:

[0119] ;

[0120] Where s t is the state space parameter, L pred (t) is the load forecast data, SOC(t) is the energy storage state data, F diesel (t) is the fuel quantity data of the generator, P dev is the device priority label;

[0121] The device priority labels specifically include non-critical devices (0), ordinary-level devices (1), and critical-level devices (2);

[0122] Step S32: load improved weighted clustering, specifically, performing feature vector reconstruction based on the temporary construction area power management optimization data set and the state space parameter data to obtain equipment reconstructed feature vector data, performing cluster center calculation through improved weighted calculation, and performing dynamic clustering adjustment in combination with the elbow method to obtain load clustering data;

[0123] The calculation formula for the device to reconstruct the feature vector data is:

[0124] ;

[0125] Where x i is the reconstructed feature vector data of the load device, is the average power characteristic, is the peak power characteristic, is the device weight feature, i is the load device index;

[0126] The calculation formula of the improved weighted calculation is:

[0127] ;

[0128] Where, Is to improve the weighted distance calculation output, is the cluster center, is the mean cluster center, is the peak cluster center, is the device weight cluster center;

[0129] Step S33: Multi-agent reinforcement learning, specifically, defining a main grid agent, a generator agent, and an energy storage agent to perform multi-agent definition, constructing a standard deep Q network as the agent decision network, defining action space parameters and a power load improvement reward function, and performing multi-agent reinforcement learning based on the state space parameter data to obtain power load optimization strategy data;

[0130] The action space parameters include power-off action, frequency reduction action and full-power operation action;

[0131] The calculation formula of the power load improvement reward function is:

[0132] ;

[0133] Where r t is the power load improvement reward function, is the key load power supply indicator function, used to indicate the power supply stability reward, IC is the key load power supply judgment identifier, F diesel (t) is the generator fuel quantity data, which is used to represent the fuel consumption reward, and SOC(t) is the energy storage state data, which is used to represent the energy storage balance reward;

[0134] Step S34: Dynamically adjust the load priority, specifically by constructing a flexible priority adjustment algorithm to dynamically adjust the load priority. By dynamically adjusting the load priority, resource reallocation after the new device is connected is optimized to obtain dynamic load priority data.

[0135] The elastic priority adjustment algorithm specifically performs dynamic priority adjustment by calculating the priority change. The calculation formula is:

[0136] ;

[0137] Where, is the priority change, is the power requirement of the new device. is the normalized value of the current system load state, is the enhancement coefficient;

[0138] Preferably, the specific value range of the enhancement coefficient is set to [1.0, 3.0];

[0139] Step S35: power load management, specifically, performing comprehensive power load management based on the power load optimization strategy data and dynamic load priority data by constructing intelligent agent power constraints to obtain power load distribution solution reference data;

[0140] The calculation formula of the agent power constraint condition is:

[0141] ;

[0142] Where, The overall power is the total power of all devices, which is used to express the power constraint of the intelligent agent. is the total number of load devices, i is the load device index, P i (·) is the equipment power, a i is the action decision output after multi-agent reinforcement learning, P gridmax is the maximum power supply value of the power grid, P diesel is the maximum power value of the generator, P battery is the maximum power value of the energy storage system.

[0143] By performing the above operations, in view of the fact that existing power load management methods are usually based on static load forecasting and conventional load distribution algorithms, they are unable to cope with sudden changes in power demand or the complex needs of dynamic load distribution, and thus traditional methods cannot cope with load fluctuations and lack of intelligent scheduling capabilities. This solution creatively adopts an improved weighted clustering multi-power supply node intelligent agent reinforcement learning method to achieve dynamic balancing and intelligent scheduling of power loads. By combining multi-dimensional data such as power load forecasting, energy storage status and equipment priority, power load management has adaptive capabilities and can optimize loads according to real-time power demand, equipment status and environmental changes, thereby improving the self-recovery capability and overall operating efficiency of the power system.

[0144] Example 5, see Figure 1 、 Figure 2 and Figure 5 This embodiment is based on the above embodiment. In step S4, the power supply path optimization is used to adaptively adjust the power supply path strategy of the temporary construction area. Specifically, based on the temporary construction area power management optimization dataset, an optimization algorithm combined with the power supply path reconstruction of the standard topology structure is used to optimize the power supply path to obtain the optimal power supply path reference data, including the following steps:

[0145] Step S41: Input data processing, specifically constructing temporary construction area power supply node and edge data to obtain temporary construction area power supply topology data; the temporary construction area power supply node specifically includes power supply points and load points; the edge data specifically uses cable impedance data as edge data;

[0146] Step S42: reconstructing the standard topology map, specifically, obtaining optimized power supply topology map data by constructing an adjacency matrix and performing dynamic weight calculation based on the temporary construction area power supply topology map data;

[0147] Step S43: Construct an optimization algorithm, specifically, starting from the substation, using the standard Dijkstra algorithm to calculate the shortest path to all load nodes, and dynamically adjust the weights by updating them every 5 minutes to obtain optimized path data;

[0148] Step S44: Optimizing the power supply path, specifically, using an intelligent switch to automatically switch the power supply circuit based on the optimized path data, and visually analyzing the line loss heat map to obtain reference data for the power load distribution plan.

[0149] By performing the above operations, this solution creatively adopts an optimization algorithm that combines the reconstruction of the power supply path with the standard topology structure to optimize the power supply path, addressing the technical problems of untimely path optimization and slow response speed in existing power supply path optimization methods. This achieves adaptive adjustment of the dynamic power supply path. In addition, the use of intelligent switches for power supply circuit switching and the combination of line loss heat maps for visual analysis further reduce losses during power transmission and ensure the efficient and stable operation of the power supply system.

[0150] Example 6, see Figure 1 and Figure 2 This embodiment is based on the above embodiment. In step S5, the temporary construction area power management is used for global monitoring and systematic optimization. Specifically, through data aggregation, the equipment energy efficiency evaluation data, the power load distribution plan reference data and the optimal power supply path reference data are combined to build a temporary construction area power integrated management system based on a rule engine. According to the abnormality detection in the equipment energy efficiency evaluation data, automatic control and manual confirmation are performed. In addition, the temporary construction area power equipment is controlled according to the power load distribution plan reference data and the optimal power supply path reference data to obtain the temporary construction area power integrated management reference data.

[0151] Preferably, the data aggregation is set to collect the equipment energy efficiency evaluation data, the power load distribution scheme reference data and the optimal power supply path reference data every 30 seconds and store them in a database;

[0152] The rule engine adopts the Drools engine and matches predefined rules;

[0153] The predefined rules include overload protection and energy storage scheduling;

[0154] Table 1 is a Drools syntax code example table of the predefined rules. As shown in the table, the rule parameter represents the definition rule, Node(load > 0.9*capacity) is the power node data model, specifically indicating that when the load exceeds 90% of the capacity, the overload protection condition is triggered, triggerAlarm(·) is the alarm triggering function, shedNonCriticalLoad($node) is the non-critical load cut-off function, $battery : Battery(SOC < 0.2) is the energy storage battery data model, specifically indicating that when the power is less than 20%, the energy storage scheduling condition is triggered, Time(hour >= 18 && hour <= 6) is the system time object, specifically indicating that the time range is from 18:00 to 6:00 the next morning, and startGenerator() is the power supply scheduling function.

[0155] Table 1 Drools syntax code examples for predefined rules

[0156]

[0157] Example 7, see Figure 1 and Figure 2 This embodiment is based on the above embodiment. The artificial intelligence-based temporary construction area power management optimization system provided by the present invention includes a data scheduling module, an equipment energy consumption management module, a power load management module, a power supply path optimization module and a temporary construction area power management module;

[0158] The data scheduling module is used for data scheduling, obtaining a temporary construction area power management optimization data set through data scheduling, and sending the temporary construction area power management optimization data set to the equipment energy consumption management module, the power load management module and the power supply path optimization module;

[0159] The equipment energy consumption management module is used for equipment energy consumption management, obtains equipment energy efficiency evaluation data through equipment energy consumption management, and sends the equipment energy efficiency evaluation data to the temporary construction area power management module;

[0160] The power load management module is used for power load management, obtains power load distribution scheme reference data through power load management, and sends the power load distribution scheme reference data to the temporary construction area power management module;

[0161] The power supply path optimization module is used to optimize the power supply path, obtain optimal power supply path reference data through power supply path optimization, and send the optimal power supply path reference data to the temporary construction area power management module;

[0162] The temporary construction area power management module is used for temporary construction area power management, and obtains temporary construction area power comprehensive management reference data through temporary construction area power management.

[0163] It should be noted that, in this document, relational terms such as first and second, etc., are used only 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 terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0164] While the embodiments of the present invention have been shown and described, it will be apparent to those skilled in the art that various changes, modifications, substitutions, and alterations can be made to the embodiments without departing from the principles and spirit of the invention.

[0165] The present invention and its embodiments are described above. This description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if a person skilled in the art is inspired by this and, without departing from the purpose of the present invention, designs structures and embodiments similar to this technical solution without inventiveness, they shall fall within the scope of protection of the present invention.

Claims

1. The artificial intelligence-based power management optimization method for temporary construction areas is characterized by: The method comprises the following steps: Step S1: Data scheduling to obtain the temporary construction area power management optimization data set; Step S2: Equipment energy consumption management, using a dynamic power consumption modeling method that combines a hybrid model and transient feature improvements to manage equipment energy consumption and obtain equipment energy efficiency evaluation data, including the following steps: Step S21: Enhanced preprocessing; Step S22: Equipment feature hybrid extraction, by constructing a hybrid convolution long-term and short-term model and an improved transient feature loss function to extract equipment features; Step S23: Dynamic power consumption modeling; Step S24: Improved equipment anomaly detection, by feature reconstruction and anomaly score calculation, to improve equipment anomaly detection, and by building a threshold trigger mechanism for anomaly marking; Step S25: Energy efficiency score calculation; Step S26: Equipment energy consumption management; The calculation formula of the improved transient feature loss function is: Where, is the improved transient feature loss function, max(·) is the maximum value function, f(x a ) is the anchor sample feature vector, which is used to represent the feature representation of the target device to be identified at the current moment, f(x p ) is the positive sample feature vector, which is used to represent the feature representation of the same type of equipment as the anchor sample at different time periods, f(x n ) is the negative sample feature vector, which is used to represent the feature representation of devices of different types from the anchor sample, ||·||2 is the L2 norm operator, α is the interval parameter, and λ is the transient feature weight coefficient. The overall is the rate of change of the transient feature; Step S3: Power load management, adopting the improved weighted clustering multi-power supply node agent reinforcement learning method to perform power load management and obtain power load distribution plan reference data, including the following steps: Step S31: Input data modeling; Step S32: Load improved weighted clustering, specifically based on the temporary construction area power management optimization data set and the state space parameter data, perform feature vector reconstruction to obtain equipment reconstructed feature vector data, and perform cluster center calculation through improved weighted calculation, and combine the elbow method to perform dynamic clustering adjustment to obtain load clustering data; Step S33: Multi-agent reinforcement learning, define the main grid agent, generator agent and energy storage agent, and perform multi-agent reinforcement learning by defining action space parameters and power load improvement reward function; Step S34: Dynamic adjustment of load priority; Step S35: Power load management; The calculation formula of the power load improvement reward function is: Where r t is the power load improvement reward function, is the key load power supply indicator function, used to indicate the power supply stability reward, IC is the key load power supply judgment identifier, F diesel (t) is the generator fuel quantity data, which is used to represent the fuel consumption reward, and SOC(t) is the energy storage state data, which is used to represent the energy storage balance reward; Step S4: power supply path optimization, using an optimization algorithm combined with standard topology structure power supply path reconstruction to optimize the power supply path and obtain optimal power supply path reference data; Step S5: temporary construction area power management, obtain temporary construction area power comprehensive management reference data.

2. The artificial intelligence-based temporary construction area power management optimization method according to claim 1 is characterized by: In step S1, the data scheduling is used to collect, optimize and schedule the data required for power management in the temporary construction area. Specifically, the raw power data is obtained through data collection, and through data processing and partition transmission, the intelligent functions of equipment energy consumption management, power load management and power supply path optimization are supported to obtain the optimized data set for power management in the temporary construction area. The data acquisition specifically reads raw data from sensors and device controllers through industrial communication protocols, and collects power data by high-frequency sampling of power parameters; The raw power data includes power sensor data, equipment control data, environmental monitoring data and management system data; The temporary construction area power management optimization data set includes time-series power parameter data, real-time data stream data, abnormal record data, current harmonic data, load prediction data and cable impedance data; By performing partition transmission operations on the temporary construction area power management optimization data set, the time-series power parameter data, abnormal record data and current harmonic data are used for equipment energy consumption management tasks, the time-series power parameter data and system load prediction data are used for power load management tasks, and the time-series power parameter data, real-time data stream data and cable impedance data are used for power supply path optimization tasks.

3. The artificial intelligence-based temporary construction area power management optimization method according to claim 2 is characterized by: In step S2, the device energy consumption management is used to identify high-energy-consuming devices and analyze abnormal behavior of temporary construction area power equipment. Specifically, based on the temporary construction area power management optimization data set, a dynamic power consumption modeling method combining a hybrid model and transient characteristics is used to perform device energy consumption management and obtain equipment energy efficiency evaluation data, including the following steps: Step S21: Enhanced preprocessing, specifically extracting current waveforms, voltage waveforms, and equipment metadata from the time-series power parameter data, abnormal record data, and current harmonic data in the temporary construction area power management optimization data set, extracting harmonic decomposition features and transient feature data, performing data reconstruction, and obtaining equipment energy consumption management input data; Step S22: extracting mixed device features, specifically by constructing a mixed convolutional long-term and short-term model and optimizing the model structure to obtain a mixed device feature extraction network, and constructing an improved transient feature loss function to perform mixed device feature extraction on the device energy consumption management input data to obtain mixed device feature data; The model structure optimization specifically includes sequentially constructing a waveform improvement input layer, a convolutional module, a bidirectional long-short-term module, and a fully connected layer; Step S23: Dynamic power consumption modeling, specifically, constructing a dynamic power consumption function to dynamically model and calculate the power consumption of each power device to obtain dynamic power consumption calculation data; Step S24: improving device anomaly detection, specifically by performing improved device anomaly detection based on the device hybrid feature data and the dynamic power consumption calculation data through feature reconstruction and anomaly score calculation, obtaining a device anomaly estimate, and establishing a threshold trigger mechanism to mark an anomaly when the device anomaly estimate is greater than a set threshold; The feature reconstruction is performed by calculating the current harmonic distortion rate, power factor and transient energy every 10 seconds to reconstruct the frequency feature and obtain reconstructed feature data; The anomaly score calculation is specifically performed by constructing an improved isolation forest algorithm, adding transient feature weights on the basis of the standard algorithm, and calculating the device anomaly estimation; Step S25: Calculating an energy efficiency score, specifically performing a comprehensive calculation of the device energy consumption score based on the device mixed feature data and the dynamic power consumption calculation data to obtain device energy consumption and energy efficiency evaluation data; Step S26: device energy consumption management, specifically performing device energy consumption management based on the dynamic power consumption calculation data, the device abnormality estimation data and the device energy consumption and energy efficiency evaluation data to obtain device energy efficiency evaluation data.

4. The artificial intelligence-based temporary construction area power management optimization method according to claim 3 is characterized by: In step S3, the power load management is used to dynamically balance the supply and demand of power load resources. Specifically, based on the temporary construction area power management optimization dataset, a multi-power supply node agent reinforcement learning method with improved weighted clustering is used to perform power load management and obtain reference data for the power load distribution plan, including the following steps: Step S31: input data modeling, specifically extracting load forecast data, energy storage status data, and generator fuel quantity data from the temporary construction area power management optimization data set, and defining state space parameters by constructing device priority tags to obtain state space parameter data; Step S32: load improved weighted clustering, specifically, performing feature vector reconstruction based on the temporary construction area power management optimization data set and the state space parameter data to obtain equipment reconstructed feature vector data, performing cluster center calculation through improved weighted calculation, and performing dynamic clustering adjustment in combination with the elbow method to obtain load clustering data; Step S33: Multi-agent reinforcement learning, specifically, defining a main grid agent, a generator agent, and an energy storage agent to perform multi-agent definition, constructing a standard deep Q network as the agent decision network, defining action space parameters and a power load improvement reward function, and performing multi-agent reinforcement learning based on the state space parameter data to obtain power load optimization strategy data; The action space parameters include power-off action, frequency reduction action and full-power operation action; Step S34: Dynamically adjust the load priority, specifically by constructing a flexible priority adjustment algorithm to dynamically adjust the load priority. By dynamically adjusting the load priority, resource reallocation after the new device is connected is optimized to obtain dynamic load priority data. The elastic priority adjustment algorithm specifically performs dynamic priority adjustment by calculating the priority change; Step S35: Power load management, specifically, based on the power load optimization strategy data and dynamic load priority data, by constructing intelligent body power constraint conditions, performing comprehensive power load management, and obtaining power load distribution plan reference data.

5. The artificial intelligence-based temporary construction area power management optimization method according to claim 4 is characterized by: In step S4, the power supply path optimization is used to adaptively adjust the power supply path strategy of the temporary construction area. Specifically, based on the temporary construction area power management optimization dataset, an optimization algorithm combined with the power supply path reconstruction of the standard topology structure is used to optimize the power supply path to obtain the optimal power supply path reference data, including the following steps: Step S41: Input data processing, specifically constructing temporary construction area power supply node and edge data to obtain temporary construction area power supply topology data; the temporary construction area power supply node specifically includes power supply points and load points; the edge data specifically uses cable impedance data as edge data; Step S42: reconstructing the standard topology map, specifically, obtaining optimized power supply topology map data by constructing an adjacency matrix and performing dynamic weight calculation based on the temporary construction area power supply topology map data; Step S43: Construct an optimization algorithm, specifically, starting from the substation, using the standard Dijkstra algorithm to calculate the shortest path to all load nodes, and dynamically adjust the weights by updating them every 5 minutes to obtain optimized path data; Step S44: Optimizing the power supply path, specifically, using an intelligent switch to automatically switch the power supply circuit based on the optimized path data, and visually analyzing the line loss heat map to obtain reference data for the power load distribution plan.

6. The artificial intelligence-based temporary construction area power management optimization method according to claim 5 is characterized by: In step S5, the temporary construction area power management is used for global monitoring and systematic optimization. Specifically, through data aggregation, combined with the equipment energy efficiency evaluation data, the power load distribution scheme reference data and the optimal power supply path reference data, a temporary construction area power comprehensive management system based on a rule engine is constructed. According to the abnormal detection in the equipment energy efficiency evaluation data, automatic control and manual confirmation are performed, and the temporary construction area power equipment is controlled according to the power load distribution scheme reference data and the optimal power supply path reference data to obtain the temporary construction area power comprehensive management reference data.

7. An artificial intelligence-based temporary construction area power management optimization system, configured to implement the artificial intelligence-based temporary construction area power management optimization method according to any one of claims 1 to 6, characterized in that: It includes data scheduling module, equipment energy consumption management module, power load management module, power supply path optimization module and temporary construction area power management module.

8. The artificial intelligence-based temporary construction area power management optimization system according to claim 7 is characterized by: The data scheduling module is used for data scheduling, obtaining a temporary construction area power management optimization data set through data scheduling, and sending the temporary construction area power management optimization data set to the equipment energy consumption management module, the power load management module and the power supply path optimization module; The equipment energy consumption management module is used for equipment energy consumption management, obtains equipment energy efficiency evaluation data through equipment energy consumption management, and sends the equipment energy efficiency evaluation data to the temporary construction area power management module; The power load management module is used for power load management, obtains power load distribution scheme reference data through power load management, and sends the power load distribution scheme reference data to the temporary construction area power management module; The power supply path optimization module is used to optimize the power supply path, obtain optimal power supply path reference data through power supply path optimization, and send the optimal power supply path reference data to the temporary construction area power management module; The temporary construction area power management module is used for temporary construction area power management, and obtains temporary construction area power comprehensive management reference data through temporary construction area power management.

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