An intelligent energy operation optimization method based on AI analysis

Abstract

The application discloses a kind of based on AI analysis's wisdom energy operation optimization method, belong to wisdom energy optimization technical field, specifically include: first, each node real-time operation data and switch position signal are collected, and topology transient reconstruction trigger instruction is generated by switch signal jump;Then, the node and line of energy flow path mutation are extracted in combination with real-time data, and dynamic adjacency matrix is updated and implanted mark, and transient physical topology correlation atlas is constructed;Subsequently, atlas is input into topological adaptive perception network, and topology change is adapted by attention mask mechanism, and island node is perceived;Again, time sequence characteristics are extracted, and node correlation degree is calculated, and high-dimensional space feature atlas is generated;The real-time energy interaction margin of each node is output by network solution, and finally, according to the margin, prevent over-limit scheduling instruction is generated and issued for execution, guarantee system transient operation safety;The application quickly adapts network topology transient change, effectively improves the stability and security of wisdom energy network transient operation.

Description

Technical Field

[0001] This invention relates to the field of smart energy optimization technology, specifically to a smart energy operation optimization method based on AI analysis. Background Technology

[0002] With the construction of new power systems and the widespread adoption of IoT technology, modern power grids are rapidly evolving into smart energy networks characterized by a high proportion of distributed power generation and multi-energy complementarity. To achieve global coordinated management of massive distributed energy resources, flexible energy storage systems, and adjustable loads, artificial intelligence (AI)-based operation optimization methods have become a core technology in this field. In current engineering practice, mainstream solutions typically introduce graph neural networks or deep learning architectures based on attention mechanisms, abstracting the energy network into a spatially coupled topology composed of nodes and interconnected lines. These conventional AI analysis networks primarily rely on a predetermined initial physical network structure to establish information transmission channels between neurons and utilize preset static connection weights to extract and deduce the operating characteristics and energy flow patterns of the power grid, thereby assisting the dispatch system in formulating power allocation strategies and operational guidance under normal conditions.

[0003] However, when smart energy networks encounter multiple physical failures or perform emergency isolation switching, the underlying energy flow transmission paths and the spatial coupling topology of nodes undergo transient reconstruction. Conventional AI analysis networks employing fixed graph structures or static attention mechanisms, due to the deep binding of their neuron connection weights to the initial physical topology, cannot achieve adaptive dynamic reconstruction of the perception field when faced with sudden changes in spatial structure. This severe lag and failure of topology perception features causes optimization algorithms to continue issuing scheduling commands based on an already invalidated network model, easily inducing local cascading disconnections and secondary energy limit exceedance faults. Summary of the Invention

[0004] The purpose of this invention is to provide a smart energy operation optimization method based on AI analysis, thereby solving the problems in the background technology: The objective of this invention can be achieved through the following technical solutions: A smart energy operation optimization method based on AI analysis includes the following steps: S1: Collect real-time operating status data and switch position signals of smart energy network nodes, and generate transient reconstruction trigger commands for spatially coupled topology based on the state transitions of the switch position signals; S2: In response to the transient reconstruction trigger command, extract the set of nodes and lines where energy flow path changes occur to update the dynamic adjacency matrix, and implant line disconnection and island node markers to construct a transient physical topology association map; S3: Input the transient physical topology association map into the preset topology adaptive perception network, and based on the attention masking mechanism, shield the inherent connection weights of neurons corresponding to the line disconnection markers in the dynamic adjacency matrix, activate the information transmission channel of the new connected path, and start independent local feature perception for isolated nodes to form the reconstructed topology adaptive perception network. S4: Extract the temporal dynamic features of nodes based on the reconstructed topology adaptive sensing network, calculate the high-dimensional spatial feature correlation degree between nodes that maintain effective physical connections, and aggregate to generate a high-dimensional spatial feature map. S5: Combining high-dimensional spatial feature maps, forward propagation calculations are performed through the reconstructed topological adaptive sensing network to output the real-time energy interaction margin of each node during the transient process; S6: Generate corresponding over-limit scheduling control commands based on real-time energy interaction margin and send them to the corresponding node devices for execution.

[0005] As a further aspect of the present invention: in step S1, the process of generating the transient reconstruction trigger instruction for spatially coupled topology is as follows: The voltage and current of each node at the bottom layer of the smart energy network are read using measuring devices and converted into real-time operating status data. At the same time, the mechanical contact identifiers of physical switches are acquired and encoded into switch position signals. Extract the switch position signal of the current sampling period and perform a logical XOR operation with the historical signal of the previous period to locate the node where the Boolean flip occurs in order to confirm the state transition of the switch position signal. A physical network structure change message is constructed based on the combination of the node code and timestamp of the state transition. The change message is then encapsulated in a protocol to generate a transient reconstruction trigger instruction for the spatially coupled topology.

[0006] As a further aspect of the present invention: in step S2, the process of constructing the transient physical topology correlation map is as follows: Receive transient reconstruction trigger command and parse the state transition node code, call real-time running status data to perform power flow direction verification, and extract the set of nodes and lines at the underlying level that have undergone sudden changes in energy flow path corresponding to power flow reversal, value returning to zero, and value jumping from zero to non-zero. Based on the extracted set of nodes and lines where energy flow paths abruptly change, the coordinates of the corresponding abruptly changing lines in the basic matrix of the previous sampling period are located. The weight elements of the coordinates where disconnection occurs are set to zero, and the weight elements of the coordinates where connectivity occurs are updated to non-zero valid values. The dynamic adjacency matrix of the network is then updated globally. Traverse the updated global dynamic adjacency matrix of the network, insert line disconnection markers at the coordinates where the weights are set to zero, detect the node indices corresponding to the all-zero row vectors in the matrix, and insert island node markers at the corresponding positions. Extract the dynamic adjacency matrix of the network after implantation of tags, map the topological relationships of its internal elements to a preset spatial coordinate system to generate a physical node array, and construct a transient physical topology association map accordingly.

[0007] As a further aspect of the present invention: the process in step S3 is as follows: The transient physical topology correlation map is input into a preset topology adaptive sensing network. The underlying structure tensor of the map is analyzed, and the spatial coordinates of the line disconnection markers and the spatial coordinates of the isolated node markers embedded in the map are extracted. Based on the attention masking mechanism, a mask matrix is ​​constructed according to the spatial coordinates of the line disconnection marker and a dot product operation is performed with the intrinsic connection weights of the neurons to forcibly mask the intrinsic connection weights of the neurons corresponding to the line disconnection markers in the dynamic adjacency matrix. Extract the spatial coordinates of the target element whose numerical state jumps from zero to non-zero in the dynamic adjacency matrix, call the feature aggregation operator to reorganize the computation graph of related nodes, and activate the information transmission channel corresponding to the new connected path; Based on the spatial coordinates of isolated nodes, the feature interaction tensor between the target node and the global network is cut off, driving the local isolated network branch to map single-point running status data, while simultaneously initiating independent local feature perception for isolated nodes.

[0008] As a further aspect of the present invention: in step S4, the process of aggregating and generating a high-dimensional spatial feature map is as follows: Based on the reconstructed topology adaptive sensing network, the real-time running status data of the nodes is captured by a time sliding window, and the data is input into the built-in time series computing unit to analyze the hidden state evolution trajectory frame by frame, and extract the time series dynamic features of the real-time running status data of the nodes. For all nodes that maintain effective physical connections, the extracted temporal dynamic features are mapped to state tensors. The attention kernel function inside the adaptive perception network is called to perform the dot product of the state tensors and calculate the high-dimensional spatial feature correlation degree between all nodes that maintain effective physical connections. Using all nodes that maintain valid physical connections as structural vertices, the calculated high-dimensional spatial feature correlation is assigned to the topological connection edges of the corresponding structural vertices, and matrix splicing of the node state dimension is performed under the global topological framework to aggregate and generate a high-dimensional spatial feature map.

[0009] As a further aspect of the present invention: in step S5, the process of outputting the real-time energy interaction margin of each node during the transient process is as follows: The high-dimensional spatial feature map is input into the reconstructed topology adaptive sensing network, which activates the forward propagation mechanism of the topology adaptive sensing network and drives the hidden layer to generate the high-dimensional feature tensor of each node through layer-by-layer operation. The generated high-dimensional feature tensor is extracted and fed into the regression layer at the end of the topology adaptive sensing network. The theoretical energy boundary values ​​of each node in the transient process are solved by forward propagation. Extract the current actual energy value represented by the real-time operating status data of the nodes, perform a difference operation between it and the theoretical energy boundary value, and output the real-time energy interaction margin of each node in the transient process.

[0010] As a further aspect of the present invention: the process of inputting the high-dimensional spatial feature map into the reconstructed topology-adaptive sensing network, activating the forward propagation mechanism of the topology-adaptive sensing network, and driving the hidden layers to perform layer-by-layer operations to generate the high-dimensional feature tensor of each node is as follows: The structural tensor and state features contained in the high-dimensional spatial feature map are extracted by parsing, and the structural tensor and state features are mapped to the input layer perception nodes of the reconstructed network. The forward propagation mechanism of the topology adaptive sensing network is activated, and the state features carried by the sensing nodes in the input layer are projected into the hidden layer inside the network by following the path channel defined by the structural tensor. The hidden layer is driven to perform layer-by-layer operations, and the built-in feature mapping function is called to perform spatial dimension fusion on the delivered state features, and the high-dimensional feature tensors of each node are generated at the end of the network topology.

[0011] As a further aspect of the present invention: in step S6, the process of generating corresponding anti-limit-overrun scheduling control commands based on real-time energy interaction margin and issuing them to the corresponding node devices for execution is as follows: The calculated energy interaction margin of each node is compared with the preset safety threshold. Nodes with margins lower than the preset safety threshold are selected to exceed the limit, and the load adjustment benchmark of the node is calculated. Based on the load regulation baseline, the built-in rule base is matched, the physical action sequence corresponding to the over-limit node is retrieved, the protocol encoding of the physical action sequence is performed, and the corresponding anti-over-limit scheduling control command is generated. The addressing identifier embedded in the anti-limit-crossing scheduling control command is parsed, a downlink data link is established based on the addressing identifier, and the anti-limit-crossing scheduling control command is sent to the corresponding smart energy network node device for execution.

[0012] The beneficial effects of this invention are: This invention effectively solves the problems of topology sensing lag and failure, achieving accurate and real-time adaptation to transient topology. By collecting switch position signal transitions to generate reconstruction trigger commands, it quickly captures nodes and line sets with abrupt changes in energy flow paths, updates the dynamic adjacency matrix, and implants disconnection and island markers. The constructed transient physical topology association map can accurately reflect the real-time network structure. Combined with an attention masking mechanism, the topology adaptive sensing network can promptly shield failed neuron connections, activate new connected paths, and independently sense island nodes. This breaks the limitations of conventional AI networks being bound to the initial topology, reducing the topology sensing lag time to the millisecond level, and completely avoiding the optimization algorithm issuing scheduling commands based on failure models, thus curbing the triggering of secondary energy exceedances from the source.

[0013] This invention improves the accuracy and safety of system scheduling and control in transient scenarios, reducing the risk of cascading failures. By extracting the temporal dynamic features of nodes and calculating the correlation degree of high-dimensional spatial features, the generated high-dimensional spatial feature map can accurately depict the interaction relationship of effectively connected nodes. The real-time energy interaction margin calculated by the topology adaptive sensing network can accurately reflect the transient carrying capacity of each node. The generated anti-limit-overrun scheduling instructions are highly targeted and can accurately match the transient topology, ensuring that the energy interaction of each node is within a safe range. This effectively avoids the risk of local faults escalating into cascading failures, significantly improving the operational stability and self-healing capability of smart energy networks in transient scenarios such as multi-point equipment failures and emergency isolation switching. Attached Figure Description

[0014] The invention will now be further described with reference to the accompanying drawings.

[0015] Figure 1 This is a flowchart illustrating a smart energy operation optimization method based on AI analysis according to the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Please see Figure 1 As shown, this invention is a smart energy operation optimization method based on AI analysis, comprising the following steps: S1: Collect real-time operating status data and switch position signals of smart energy network nodes, and generate transient reconstruction trigger commands for spatially coupled topology based on the state transitions of the switch position signals; S2: In response to the transient reconstruction trigger command, extract the set of nodes and lines where energy flow path changes occur to update the dynamic adjacency matrix, and implant line disconnection and island node markers to construct a transient physical topology association map; S3: Input the transient physical topology association map into the preset topology adaptive perception network, and based on the attention masking mechanism, shield the inherent connection weights of neurons corresponding to the line disconnection markers in the dynamic adjacency matrix, activate the information transmission channel of the new connected path, and start independent local feature perception for isolated nodes to form the reconstructed topology adaptive perception network. S4: Extract the temporal dynamic features of nodes based on the reconstructed topology adaptive sensing network, calculate the high-dimensional spatial feature correlation degree between nodes that maintain effective physical connections, and aggregate to generate a high-dimensional spatial feature map. S5: Combining high-dimensional spatial feature maps, forward propagation calculations are performed through the reconstructed topological adaptive sensing network to output the real-time energy interaction margin of each node during the transient process; S6: Generate corresponding over-limit scheduling control commands based on real-time energy interaction margin and send them to the corresponding node devices for execution.

[0018] In one embodiment of the present invention, the process of collecting real-time operating status data and switch position signals of smart energy network nodes in step S1, and generating a transient reconstruction trigger command for spatially coupled topology based on the state transition of the switch position signals, is as follows: The starting point of the entire sensing process is to use measuring devices to read the voltage and current of each node in the underlying layer of the smart energy network and convert it into real-time operating status data, while simultaneously acquiring the mechanical contact identifiers of physical switches and encoding them into switch position signals. Intelligent sensing terminals deployed at the bottom layer of the energy network perform high-frequency sampling, directly acquiring the fundamental transient voltage and current waveforms of each electrical node through measuring elements such as current transformers. Analog-to-digital converters convert analog electrical signals into digital sequences, and digital signal processing units further calculate higher-order variables such as active and reactive power of the nodes. These variables together constitute real-time operating status data reflecting the current operating load of the nodes. While continuously acquiring electrical quantities, the digital input interfaces on the edge side monitor the physical operation status of each circuit breaker and disconnector in parallel. Opto-isolation circuits read the level signals of the physical switch mechanical contacts closing or opening, and the main control chip maps the captured high and low levels into binary identifiers. These binary identifiers are encoded according to specific rules to form standardized switch position signals. Through a high-precision clock synchronization mechanism, the measuring devices ensure strict alignment of real-time operating status data and switch position signals in the time dimension.

[0019] The core mechanism of topology evolution analysis involves extracting the switch position signal of the current sampling period and performing a logical XOR operation with the historical signal of the previous period to locate the node where a Boolean flip has occurred, thus confirming the state transition of the switch position signal. The microprocessor's internal cache matrix continuously records and refreshes the discrete switch quantity acquisition results of each period. At the arrival of each new sampling period, the computing unit extracts the latest switch position signal sequence that has just been encoded. The controller calls the historical signal sequence of the previous sampling period stored in the register. The computing unit performs a bitwise logical XOR operation on the signal sequences of these two adjacent periods. If the physical switch state of a node remains consistent in both periods, the output of the XOR operation is logic zero; if the physical switch undergoes a mechanical action of closing or opening, causing the Boolean value of the current period to differ from the Boolean value of the historical period, the output of the XOR operation becomes logic one. By traversing the output vector of the XOR operation and extracting the coordinates of the elements with a value of logic one, the target node where a Boolean flip has occurred can be located extremely efficiently and accurately. Confirming the location of these nodes completes the physical confirmation process of the switch position signal state transition.

[0020] The process of constructing a physical network structure change message based on the node code and timestamp of the state transition, and then encapsulating this message to generate a transient reconstruction trigger command for the spatially coupled topology, ensures the standardization and reliability of command transmission. After confirming a state transition, the edge computing unit immediately extracts the unique identification code of the corresponding node and obtains the absolute timestamp of the action using a microsecond-level high-precision clock source. The data stitching module tightly combines the node code and timestamp, adds an indicator word indicating the direction of the state change, and integrates them into a structured physical network structure change message. To adapt to the complex industrial communication environment at the underlying level and ensure the anti-interference capability of data transmission, the communication coprocessor takes over the message data. The communication coprocessor adds headers and checksums to the physical network structure change message according to a preset industrial Ethernet communication specification or standard. After rigorous protocol encapsulation, the original message is transformed into a transient reconstruction trigger command for the spatially coupled topology with complete addressing and error detection capabilities. This trigger command is sent to the communication queue to await transmission, so as to notify the central control layer to initiate the dynamic reconfiguration operation of the overall structure.

[0021] In one embodiment of the present invention, in step S2, in response to the transient reconstruction triggering command, the process of extracting the set of nodes and lines where energy flow path changes occur to update the dynamic adjacency matrix, and implanting line disconnection and island node markers to construct a transient physical topology association graph is as follows: Receiving transient reconfiguration trigger commands and parsing state transition node codes, calling real-time operational status data to perform power flow direction verification, and extracting the set of nodes and lines where power flow reverses, values ​​return to zero, and values ​​jump from zero to non-zero corresponding to abrupt changes in the underlying energy flow path are the fundamental judgment steps for performing network reconfiguration. Once the central processing unit detects a transient reconfiguration trigger command on the data bus, it immediately starts the decoding program, extracting the state transition node codes encapsulated within the command layer by layer. After obtaining these specific hardware identifiers, the data processing unit does not blindly modify the topology, but uses them as indexes to enter the real-time cache and call the corresponding real-time operational status data. This operational status data records in detail the real-time injection and outflow of active and reactive power at each electrical connection port. The controller uses these power parameters to perform strict power flow direction verification on specific lines to verify the real electrical impact of physical switching actions. The verification algorithm focuses on investigating three extreme power flow change modes. The first type is power flow reversal, where the direction of electrical energy transmission is completely opposite to that of the previous recording cycle. The second type is zeroing out, where the line that originally carried the energy transmission flow loses all power exchange capacity due to the complete disconnection of the circuit breaker. The third type is a jump from zero to non-zero, where a previously standby interconnecting switch is closed, thus establishing a new energy transmission channel between two previously disconnected nodes. By accurately capturing these three abnormal energy flow phenomena, the computing unit can completely extract the set of nodes and lines where abrupt changes in the energy flow path have occurred at the underlying level.

[0022] Based on the extracted set of nodes and lines where energy flow path mutations have occurred, the coordinates of the corresponding mutation lines in the fundamental matrix of the previous sampling period are located. The weight elements of the coordinates where disconnection mutations have occurred are set to zero, and the weight elements of the coordinates where connectivity mutations have occurred are updated to non-zero valid values. This process of updating the dynamic adjacency matrix of the global network ensures strong consistency between the mathematical model and the physical entity. The computational core first imports the fundamental matrix captured from the previous time segment into the computation memory space. This fundamental matrix completely depicts the interconnection relationships of all electrical nodes in the previous moment through a row and column coordinate system. Using the set of nodes and lines where energy flow path mutations have occurred, the addressing module performs rapid coordinate positioning in this huge two-dimensional fundamental matrix. Once these target locations where electrical state changes have occurred are locked, the matrix modification unit performs differentiated numerical overwrite operations according to the specific type of mutation. For lines whose physical connection is interrupted due to switch tripping or line fault isolation, the coordinates that originally carried values ​​are forcibly erased, and the modification unit directly sets the weight element corresponding to the coordinate to zero, thereby mathematically severing the information and energy interaction graph edge between the two nodes. Conversely, for newly established connected paths due to switching operations or self-healing interconnection loop closures, the matrix modification unit calculates a non-zero valid value representing the current transmission capacity based on its real-time state data, and updates the weight elements of the coordinates where connectivity abruptly occurs to this non-zero valid value. After bidirectional precise correction of disconnected and connected elements, the controller finally outputs the updated global dynamic adjacency matrix.

[0023] Traversing the updated dynamic adjacency matrix of the global network, implanting disconnection markers at coordinates where weights are set to zero, and detecting the node indices corresponding to all-zero row vectors in the matrix and implanting isolated node markers at the corresponding positions are key methods for preparing prior features for artificial intelligence algorithms. Since simply setting matrix elements to zero cannot intuitively and efficiently capture the physical meaning of some deep learning networks, it is necessary to attach a tokenized tensor with high-order semantic information to this purely numerical topological array. The algorithm scheduler initiates a large-scale traversal routine, performing a thorough numerical search of all internal elements along the diagonal of the dynamic adjacency matrix. Whenever the probe finds a coordinate point in the two-dimensional array where weights are forcibly set to zero, the feature injection module allocates a specific mask placeholder near that coordinate dimension, formally implanting a disconnection marker at the coordinate point where the weights are set to zero. After completing the edge-level disconnection marker implantation, the logic analysis unit further elevates the examination dimension, moving from microscopic element coordinates to macroscopic node row vectors. The analysis unit scans all rows of the matrix in parallel, detecting whether there are all-zero row vectors where all elements decay to zero. The appearance of an all-zero row vector signifies that the node has lost all physical coupling with the external macro-grid and entered a completely independent, self-powered operation mode. Once this extreme case is identified, the feature injection module immediately extracts the node index corresponding to the all-zero row vector and implants a high-priority isolated node marker at the corresponding location. These markers will become important indicators guiding the allocation of computing resources in the adaptive sensing network.

[0024] The process of extracting the network-wide dynamic adjacency matrix after implantation of tags, mapping the topological relationships of its internal elements to a preset spatial coordinate system to generate a physical node array, and constructing a transient physical topology graph realizes the transformation from an abstract digital matrix to a concrete multidimensional relationship graph. The data conversion engine is responsible for extracting the network-wide dynamic adjacency matrix from main memory, which not only contains the latest electrical connectivity weights but also carries a large number of line disconnection tags and isolated node tags. To enable the perception algorithm to better understand the spatial physical distance and electrical hierarchy relationships between nodes, the graphics processor introduces a calibrated preset spatial coordinate system. This coordinate system integrates the geographical topological location of actual energy devices with the hierarchical structure of the electrical network. Through a nonlinear mapping function, the conversion engine accurately projects the topological relationships of the elements inside the dynamic adjacency matrix into this high-dimensional preset spatial coordinate system. Each device node with an independent row number in the matrix is ​​instantiated as a specific luminous particle within the coordinate system, and all these particles are grouped together according to their respective electrical connections to generate a tightly structured physical node array. Non-zero weights in the matrix are transformed into spatial cables connecting the particles, while the implanted disconnection markers and island markers are highlighted in a special format at their corresponding spatial locations. The controller then uses this information to construct the overall structure, creating a transient physical topology map with extremely high spatiotemporal fidelity, laying a solid foundation for the model's forward inference.

[0025] In one embodiment of the present invention, step S3, wherein the transient physical topology correlation map is input into a preset topology adaptive sensing network, the inherent connection weights of neurons corresponding to line disconnection markers in the dynamic adjacency matrix are masked based on an attention masking mechanism, the information transmission channels of new connected paths are activated, and independent local feature perception is initiated for isolated nodes to form the reconstructed topology adaptive sensing network, is as follows: Inputting the transient physical topology map into a pre-defined adaptive topology sensing network to parse the map's underlying structural tensor and extract the spatial coordinates of embedded line disconnection markers and isolated node markers is the fundamental preparatory stage for reshaping the neural network's computational flow. The algorithm's main control program guides the high-dimensional map data into the input channel of the adaptive sensing network. The preprocessing module first deconstructs the complex data encapsulation format within the map and then extracts the underlying structural tensor representing the interconnection relationships between nodes. For example, in a regional network containing multiple substation nodes and transmission and distribution lines, the underlying structural tensor accurately records the admittance parameters and directed connection states between nodes in the form of a multi-dimensional array. The decoding unit scans this high-dimensional tensor row by row to explore the special numerical markers embedded at specific locations. Once the scanning probe identifies a line disconnection marker representing a physically disconnected power distribution line, the core register immediately locks and records the absolute row and column numbers of the marker in the matrix dimension, thereby obtaining extremely accurate spatial coordinates of the disconnection marker. Similarly, for nodes in the graph that have zero connectivity, the feature probe will accurately capture the isolated node markers that represent independent operating states and store their geometric position information in a dedicated isolated spatial coordinate array for later computation.

[0026] The attention masking mechanism constructs a mask matrix based on the spatial coordinates of line disconnection markers and performs a dot product operation with the intrinsic connection weights of neurons to forcibly mask the intrinsic connection weights of neurons corresponding to the line disconnection markers in the dynamic adjacency matrix, ensuring the physical fidelity of information transmission. The computational core extracts the spatial coordinates of the disconnection markers and generates a Boolean mask matrix with dimensions identical to the original neuron connection weight matrix. In this mask matrix, except for specific elements covered by the spatial coordinates of the disconnection markers which are assigned absolute values ​​of zero, all other normal elements are initialized to the value one. For example, a node whose inter-regional connection line broke due to strong winds will have its corresponding row and column intersection in the mask matrix strictly assigned the value zero. Subsequently, the tensor operation unit performs an element-wise dot product operation between this newly constructed mask matrix and the intrinsic connection weight matrix representing the empirical parameters of the neural network. During this dot product process, any intrinsic connection weight multiplied by zero is unconditionally erased and forcibly set to zero. This operation completely blocks the feature transmission of nodes that have already experienced physical disconnection in the virtual algorithm space through purely mathematical means, enabling the topology adaptive sensing network to fully conform to the real disconnection state of the physical world.

[0027] The process of extracting the spatial coordinates of target elements whose numerical states jump from zero to non-zero in the dynamic adjacency matrix and then invoking the feature aggregation operator to reorganize the computational graph of related nodes to activate the information transmission channel corresponding to the new connected path enables the network model to perceive sudden loop-closing operations. The monitoring module continuously compares the adjacency matrix states of two adjacent computation cycles. Once it detects that some zero elements that originally represented a disconnected state have jumped to a non-zero valid value representing a connected state in the latest cycle, the localization module extracts the spatial coordinates of these target elements. For example, if the dispatch center issues an instruction to close a bus tie switch during peak electricity consumption to achieve load transfer, the coordinates of the matrix element corresponding to that switch will be immediately captured. After obtaining these coordinates, the graph convolution controller inside the topology adaptive sensing network actively invokes the feature aggregation operator. This operator dynamically reorganizes the forward computational graph framework between the two related nodes in memory based on the newly added physical connected edges. By allocating a new weight parameter matrix and establishing a dedicated data cache, the computational graph reorganization operation successfully activates the new information transmission channel between these two nodes, allowing the new features generated by energy flow to smoothly integrate into the iterative evolution of the global network.

[0028] The process of severing the feature interaction tensor between the target node and the global network based on the spatial coordinates of the isolated node, driving a local isolated network branch to map single-point operating status data, and simultaneously initiating independent local feature perception for the isolated node ensures the robustness of the algorithm under extreme isolation conditions. The microprocessor reads the location information stored in the isolated spatial coordinate array and physically blocks the feature interaction tensor radiating from the target node to its surroundings in the global computation graph. This means that the node's data no longer participates in the aggregation calculation of the high-dimensional features of the large power grid. For example, a distributed energy storage and photovoltaic array in an industrial park automatically enters a self-balancing operation mode after a power outage in the external power grid. To accurately monitor this microgrid state, the topology adaptive sensing network immediately drives a pre-trained local isolated network branch. This independent computation branch is specifically responsible for receiving and mapping the voltage fluctuations and load supply and demand data of a single point within the isolated node. By initiating this dedicated local feature perception mechanism, even if the node loses the support of massive external computing power and data, it can still rely on an independent local inference network to optimize and evaluate its own energy management parameters, preventing the risk of local overload instability in isolated operation.

[0029] In one embodiment of the present invention, step S4, which involves extracting temporal dynamic features of nodes based on the reconstructed topology adaptive sensing network, calculating the high-dimensional spatial feature correlation degree between nodes that maintain effective physical connections, and aggregating to generate a high-dimensional spatial feature map, is as follows: The process of extracting real-time operating status data of nodes using a time-sliding window based on the reconstructed topology adaptive sensing network and inputting it into the built-in time-series computing unit to analyze the hidden state evolution trajectory frame by frame and extract the time-series dynamic features of the real-time operating status data of the nodes achieves in-depth mining of the dynamic characteristics of the energy network. The controller first configures a time-sliding window with a fixed step size to extract the continuously flowing data stream of energy node operation; for example, setting the width of the time-sliding window to five minutes and the sliding step size to thirty seconds, to continuously collect real-time operating status data such as voltage amplitude and active power of each node. The extracted slice data forms a data sequence containing multiple discrete time points, and the controller synchronously inputs these data sequences into the built-in time-series computing unit of the preset topology adaptive sensing network. This built-in time-series computing unit typically contains a long short-term memory loop operation structure, and reads the extracted real-time operating status data frame by frame in chronological order. In the processing stage of each frame of data, the computing core combines the input electrical parameters at the current moment with the hidden layer memory information retained at the previous moment to perform joint nonlinear operations, thereby resolving the hidden state evolution trajectory hidden beneath the appearance of electrical quantity fluctuations. By iteratively calculating all data frames within a whole time window, the computational network finally outputs a high-dimensional vector that can fully express the recent electrical behavior change trend of the node. This high-dimensional vector is the temporal dynamic feature of the extracted real-time operating status data of the node.

[0030] The process of mapping extracted temporal dynamic features to state tensors for all nodes maintaining valid physical connections and then calling the attention kernel function within the adaptive sensing network to perform a dot product operation on these state tensors to calculate the high-dimensional spatial feature correlation between all nodes maintaining valid physical connections gives the model the ability to measure the strength of energy coupling between nodes. The computing platform traverses adjacent node pairs that have been confirmed to maintain electrical connectivity after topology reconstruction. For each confirmed node maintaining a valid physical connection, the feature transformation module uses a linear transformation matrix to increase the dimensionality of its previously extracted temporal dynamic features and map them to a standard feature state tensor. For example, if a microgrid grid-connected node and an adjacent energy storage power station node have a physical transmission line, their respective dynamic features are converted into independent query tensors and key-value tensors. At this point, the controller calls the pre-configured multi-head attention kernel function within the adaptive sensing network to guide the state tensors of the two adjacent nodes to perform a dot product operation in a unified high-dimensional feature space. The specific value of the dot product directly reflects the similarity and probability of mutual influence between the two nodes in their temporal evolution behavior. To ensure numerical convergence and dimensional consistency of the calculation results, the computing platform also performs exponential normalization on the initial results of the dot product inner product. The real value output after the normalization and smoothing operation is the high-dimensional spatial feature correlation degree to be solved. This correlation degree accurately quantifies the degree of energy interaction between different energy nodes in the spatial topology dimension under dynamic operation.

[0031] The process of using all nodes maintaining effective physical connections as structural vertices and assigning the calculated high-dimensional spatial feature correlation to the topological connection edges of the corresponding structural vertices, while performing matrix splicing of the node state dimension within a global topology framework to aggregate and generate a high-dimensional spatial feature map, completes the integration and construction from scattered features to a global comprehensive view. The graph construction engine extracts all equipment and facilities performing energy transmission tasks in the actual physical space, using all energy nodes maintaining effective physical connections as abstract structural vertices for graph structure calculation. For any two structural vertices with direct electrical connections, the edge weight configuration module extracts the high-dimensional spatial feature correlation calculated in the previous step and directly assigns this physically meaningful value to the topological connection edge connecting these two corresponding structural vertices; for example, when the feature correlation between two distribution transformer nodes is extremely high, the weight parameter of their connection in the virtual graph is assigned a maximum value close to one. After assigning weights to all connected edges, the data integration unit initiates a matrix concatenation operation within a pre-defined global topology framework. The processing core performs a direct concatenation and fusion operation on the hidden feature dimension between the node state matrix describing the attributes of each node and the topological adjacency matrix composed of the associated edge weights. Through this high-dimensional tensor concatenation and fusion, the previously fragmented single-point temporal states and the global spatial interconnection topology are seamlessly integrated, thereby generating a high-dimensional spatial feature map containing the spatiotemporal evolution patterns of the entire network. This map directly serves as the global digital foundation for driving the algorithm model's output scheduling strategy.

[0032] In one embodiment of the present invention, step S5, which involves combining high-dimensional spatial feature maps and performing forward propagation calculations through the reconstructed topological adaptive sensing network to output the real-time energy interaction margin of each node during the transient process, is as follows: The high-dimensional spatial feature map is input into the reconstructed topology-adaptive sensing network, activating the network's forward propagation mechanism. This drives the hidden layers to perform layer-by-layer computation to generate the high-dimensional feature tensor for each node. Specific details include: The initial data preparation stage for launching the deep feature extraction model involves parsing a high-dimensional spatial feature map to extract its inherent structural tensor and state features, and then mapping these structural tensors and state features to the input layer sensing nodes of the reconstructed network. The computation control unit first receives a high-dimensional spatial feature map containing global spatiotemporal topological relationships; subsequently, the data parsing module performs deep matrix decoupling operations on the map to separate the structural tensor representing the physical network skeleton. For example, in a regional energy area containing 120 microgrid nodes, this structural tensor is specifically represented as a 120x120 two-dimensional sparse matrix, and its non-zero elements precisely indicate the physical connectivity between nodes. Simultaneously with extracting the structural tensor, the computation unit synchronously separates a set of state features describing the operational attributes of each node from the map; this set of state features is a 120x64 feature matrix containing 64 independent operational parameters, such as the real-time voltage and active power of each node. After obtaining these two core mathematical entities, the feature mapping engine begins the data loading operation. The feature mapping engine precisely loads each of the 120 sets of real-valued vectors with 64-dimensional operational features into the input layer sensing nodes of the network, which has already undergone topology reconstruction, according to the absolute physical number of the underlying hardware nodes. Each input layer sensing node is activated and configured as an independent data source after receiving its unique feature vector, thus laying a solid digital foundation for the upcoming deep data inference.

[0033] The forward propagation mechanism of the topology adaptive sensing network, following the path defined by the structural tensor, delivers the state features carried by the input layer sensing nodes to the hidden layers within the network, realizing the directed flow of information in the virtual topology space. Once all input layer sensing nodes have completed data loading, the main control program immediately sends an activation command to the adaptive sensing network to initiate the global forward propagation mechanism. With the activation of this mechanism, the 64-dimensional state features residing in the input layer sensing nodes begin to penetrate the deeper network structure. During the feature data flow, the model does not blindly broadcast fully connected data but strictly follows the physical path defined by the previously extracted structural tensor for targeted delivery. Specifically, when the structural tensor indicates a non-zero weighted connected edge between node A and node B, the forward propagation engine establishes a dedicated data transmission bus between the corresponding virtual hidden layer nodes. Assuming that the feature vector of node A will be accurately delivered to the hidden layer receiver of node B along this bus during the first-level delivery process; conversely, if the structural tensor shows no physical electrical connection between the two nodes, their data transmission will be completely physically shielded. Through this point-to-point delivery method that highly depends on spatial connectivity, the massive state features carried by the input layer sensing nodes are efficiently and orderly pushed into the hidden layer inside the network, thus ensuring that the transmission path of the feature data is highly consistent with the energy flow path of the real physical network structure.

[0034] The core computational process for extracting deep energy interaction patterns involves driving the hidden layer to perform layer-by-layer computations, calling built-in feature mapping functions to perform spatial dimension fusion on the submitted state features, and aggregating them at the network topology end to generate high-dimensional feature tensors for each node. When the initial state feature data floods into the hidden layer, the post-processing array begins to drive the deep network architecture to perform rigorous layer-by-layer iterative computations. At each hidden layer's computation node, the computation engine actively calls a pre-configured built-in feature mapping function. This built-in feature mapping function contains a set of weight parameter matrices with learning capabilities and non-linear activation functions such as linear rectified units. Taking the first hidden layer as an example, the mapping function performs a weighted summation of the 64-dimensional state features received from neighboring nodes and performs deep spatial dimension fusion with the node's own original features; after this round of fusion computation, the original low-dimensional features are mapped to a new feature space containing 128 dimensions. This layer-by-layer progressive spatial dimension fusion operation is continuously repeated between multiple hidden layers, constantly absorbing information from neighboring nodes within a larger topology range. Finally, when the data stream reaches the end of the network topology after passing through all the graph convolutional hidden layers, the data aggregation unit will bring together the data that has undergone multiple rounds of nonlinear transformations and spatial feature integration. For all 120 nodes in the network, the aggregation unit will independently generate a comprehensive output containing 256 floating-point values; this comprehensive output with 256 dimensions is the final generated high-dimensional feature tensor. This tensor fully characterizes the electromagnetic transient response of complex energy nodes in a highly abstract mathematical space.

[0035] The process of extracting the generated high-dimensional feature tensors and feeding them into the regression layer at the end of the topology adaptive sensing network, and then using forward propagation to calculate and fit the theoretical energy boundary values ​​of each node during the transient process, achieves a leap from abstract digital features to specific physical parameters. The computation control motherboard extracts all 256-dimensional feature tensors output from the previous computation stage from the cache area of ​​the hidden layer; these high-dimensional feature tensors are then centrally fed into the fully connected regression layer at the end of the topology adaptive sensing network structure. Inside this regression layer, a weight parameter matrix trained on massive amounts of historical transient data is deployed. When the forward propagation computation flow passes through the regression layer, the computing unit performs an inner product operation on each input high-dimensional feature tensor and the internal weight parameter matrix. During this inner product operation, the bias vector inside the regression layer participates in the addition compensation calculation simultaneously. Through the above complex mathematical calculation model, a specific scalar value is output for each device node. For example, for a distribution transformer node with a rated capacity of 800 kVA, the regression layer uses the extracted feature tensors to calculate its maximum safe load capacity fitting value under the current ambient temperature and real-time electrical stress state as 650 kW. The specific value of 650 kilowatts is the theoretical energy boundary value of the transformer node during the current transient evolution of the network; this boundary value objectively reflects the maximum energy transmission threshold that the underlying hardware equipment can withstand without triggering any over-limit alarms or tripping of protection devices.

[0036] The process of extracting the current actual energy value, representing the real-time operating status data of nodes, and performing a differential operation with the theoretical energy boundary value to output the real-time energy interaction margin of each node during the transient process provides a quantitative decision-making basis for the formulation of intelligent control strategies. The data monitoring bus listens in real-time to the latest messages uploaded by the underlying sensing devices and extracts the current electrical telemetry readings recorded in the messages. The processing unit converts these telemetry readings into the current actual energy value, representing the true operating level of the equipment; at this time, the calculation module retrieves the theoretical energy boundary value of the corresponding node previously output by the regression layer. The calculation core sends the current actual energy value and the theoretical energy boundary value into the arithmetic logic unit and strictly performs the mathematically significant differential operation. Specifically, the differential operation is represented by subtracting the actual energy value representing the current load from the theoretical energy boundary value representing the safety limit. Taking the previous distribution transformer node as an example, its theoretical energy boundary value is fitted to 650 kW, while the underlying measurement device reports its current actual energy value as 480 kW. The arithmetic logic unit calculates the difference between 650 and 480, resulting in 170 kW. This specific difference of 170 kW is directly output as the real-time energy exchange margin of the distribution transformer node during the transient process. This margin value intuitively and accurately quantifies the size of the safe buffer space between the node and the physical danger state, thereby effectively guiding the top-level dispatching instructions to carry out reasonable resource allocation and power transfer within the safe range.

[0037] In one embodiment of the present invention, step S6, which involves generating corresponding anti-limit-overrun scheduling control commands based on real-time energy interaction margin and issuing them to the corresponding node devices for execution, is as follows: The process of generating corresponding over-limit scheduling control commands based on the real-time energy interaction margins of each node output and sending these commands to the corresponding smart energy network node devices involves comparing the calculated energy interaction margins of each node with preset safety thresholds to identify over-limit nodes with margins lower than the preset safety thresholds and calculating the load adjustment baseline for these over-limit nodes. The computing control center first performs high-frequency comparisons between the calculated energy interaction margins of each node and preset safety thresholds stored in the storage array. These preset safety thresholds are dynamically set baselines based on the nameplate rated parameters of different electrical equipment and current ambient temperature and other operating conditions. For example, for a specific industrial park distribution transformer node, the currently dynamically set preset safety threshold is calibrated to 50 kilowatts. The control unit continuously reads the real-time energy interaction margins fed back by this distribution transformer node. If, at any given moment, measurement data shows that the energy margin of a node rapidly decreases to 30 kW, the comparison logic element will immediately determine that the real-time margin of 30 kW is significantly lower than the preset safety threshold of 50 kW, thus accurately identifying the node as an overload risk. After successfully identifying the overload node, the computing unit immediately starts the differential calculation program to assess the scale of power that needs to be reduced or transferred. The arithmetic logic unit calculates a power difference of 20 kW by subtracting the current actual margin from the preset safety threshold; this 20 kW power difference is then multiplied by a safety redundancy ratio constant of 1.2, which includes a line loss compensation coefficient, to ultimately calculate a load regulation baseline of up to 24 kW for the overload node.

[0038] The process of retrieving the physical action sequence corresponding to the over-limit node based on the load regulation baseline and the built-in rule library, and then executing protocol encoding on the physical action sequence to generate the corresponding anti-over-limit scheduling control instruction, achieves a leap from numerical calculation to physical action control. After obtaining the accurately calculated 24 kW load regulation baseline, the microprocessor immediately uses it as the search keyword to enter the built-in rule library stored in the underlying logic controller for optimal matching. This built-in rule library maps in detail the correspondence between different power regulation requirements and executable physical device actions. For the 24 kW load voltage drop requirement, the matching engine retrieves a set of physical action sequences corresponding to the over-limit node containing multiple linkage steps from the rule library; this physical action sequence clearly indicates that the non-core temperature-controlled load loop numbered A05 needs to be disconnected first to reduce power consumption by 15 kW, and then, after a delay of 500 milliseconds, the distributed energy storage discharge module numbered B02 needs to be activated to supplement 9 kW of active power. To ensure that these complex control logics can be accurately identified and executed without error by the underlying intelligent terminal, the communication coprocessor takes over the physical action sequence and executes strict industry standard protocol encoding on it. The encoding module converts the specific opening and closing mechanical actions and delay parameters into a hexadecimal byte stream consisting of 0s and 1s, and appends a high-priority emergency control frame header to the message header to generate a complete anti-overrun scheduling control instruction.

[0039] The process of parsing the addressing identifier embedded in the anti-limit-crossing scheduling control command, establishing a downlink data link based on the addressing identifier, and sending the anti-limit-crossing scheduling control command to the corresponding smart energy network node device is the final communication link to complete the closed-loop control. Upon receiving the newly generated anti-limit-crossing scheduling control command, the edge gateway device's communication interface immediately activates the underlying message parsing protocol stack. The processor uses bitwise operations to extract the binary device physical media access control address and the corresponding network layer logical address hidden at a specific offset address in the command message; these address information together constitute the unique addressing identifier embedded in the command. After obtaining the accurate addressing identifier, the routing control unit plans a transmission path with the lowest latency for the command within the complex industrial communication architecture. Based on this addressing identifier, the network switch establishes a dedicated high-bandwidth downlink data link between the control center and the execution mechanism under the transformer in the target industrial park. With the successful establishment of the downlink data link, the transmission queue sends the anti-limit-crossing scheduling control command losslessly to the corresponding smart energy network node device at a transmission rate of 100 megabits per second. After receiving and verifying the command, the protection and control device of the bottom node equipment immediately triggers the relay output drive hardware circuit to complete the circuit breaker opening and closing operation, thereby completely eliminating the risk of exceeding the limit of the node and ensuring the overall safe and stable operation of the energy grid.

[0040] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A smart energy operation optimization method based on AI analysis, characterized in that, Includes the following steps: S1: Collect real-time operating status data and switch position signals of smart energy network nodes, and generate transient reconstruction trigger commands for spatially coupled topology based on the state transitions of the switch position signals; S2: In response to the transient reconstruction trigger command, extract the set of nodes and lines where energy flow path changes occur to update the dynamic adjacency matrix, and implant line disconnection and island node markers to construct a transient physical topology association map; S3: Input the transient physical topology association map into the preset topology adaptive perception network, and based on the attention masking mechanism, shield the inherent connection weights of neurons corresponding to the line disconnection markers in the dynamic adjacency matrix, activate the information transmission channel of the new connected path, and start independent local feature perception for isolated nodes to form the reconstructed topology adaptive perception network. S4: Extract the temporal dynamic features of nodes based on the reconstructed topology adaptive sensing network, calculate the high-dimensional spatial feature correlation degree between nodes that maintain effective physical connections, and aggregate to generate a high-dimensional spatial feature map. S5: Combining high-dimensional spatial feature maps, forward propagation calculations are performed through the reconstructed topological adaptive sensing network to output the real-time energy interaction margin of each node during the transient process; S6: Generate corresponding over-limit scheduling control commands based on real-time energy interaction margin and send them to the corresponding node devices for execution.

2. The smart energy operation optimization method based on AI analysis according to claim 1, characterized in that, In step S1, the process of generating the transient reconstruction trigger instruction for the spatially coupled topology is as follows: The voltage and current of each node at the bottom layer of the smart energy network are read using measuring devices and converted into real-time operating status data. At the same time, the mechanical contact identifiers of physical switches are acquired and encoded into switch position signals. Extract the switch position signal of the current sampling period and perform a logical XOR operation with the historical signal of the previous period to locate the node where the Boolean flip occurs in order to confirm the state transition of the switch position signal. A physical network structure change message is constructed based on the combination of the node code and timestamp of the state transition. The change message is then encapsulated in a protocol to generate a transient reconstruction trigger instruction for the spatially coupled topology.

3. The smart energy operation optimization method based on AI analysis according to claim 1, characterized in that, In step S2, the process of constructing the transient physical topology correlation map is as follows: Receive transient reconstruction trigger command and parse the state transition node code, call real-time running status data to perform power flow direction verification, and extract the set of nodes and lines at the underlying level that have undergone sudden changes in energy flow path corresponding to power flow reversal, value returning to zero, and value jumping from zero to non-zero. Based on the extracted set of nodes and lines where energy flow paths abruptly change, the coordinates of the corresponding abruptly changing lines in the basic matrix of the previous sampling period are located. The weight elements of the coordinates where disconnection occurs are set to zero, and the weight elements of the coordinates where connectivity occurs are updated to non-zero valid values. The dynamic adjacency matrix of the network is then updated globally. Traverse the updated global dynamic adjacency matrix of the network, insert line disconnection markers at the coordinates where the weights are set to zero, detect the node indices corresponding to the all-zero row vectors in the matrix, and insert island node markers at the corresponding positions. Extract the dynamic adjacency matrix of the network after implantation of tags, map the topological relationships of its internal elements to a preset spatial coordinate system to generate a physical node array, and construct a transient physical topology association map accordingly.

4. The smart energy operation optimization method based on AI analysis according to claim 1, characterized in that, The process in step S3 is as follows: The transient physical topology correlation map is input into a preset topology adaptive sensing network. The underlying structure tensor of the map is analyzed, and the spatial coordinates of the line disconnection markers and the spatial coordinates of the isolated node markers embedded in the map are extracted. Based on the attention masking mechanism, a mask matrix is ​​constructed according to the spatial coordinates of the line disconnection marker and a dot product operation is performed with the intrinsic connection weights of the neurons to forcibly mask the intrinsic connection weights of the neurons corresponding to the line disconnection markers in the dynamic adjacency matrix. Extract the spatial coordinates of the target element whose numerical state jumps from zero to non-zero in the dynamic adjacency matrix, call the feature aggregation operator to reorganize the computation graph of related nodes, and activate the information transmission channel corresponding to the new connected path; Based on the spatial coordinates of isolated nodes, the feature interaction tensor between the target node and the global network is cut off, driving the local isolated network branch to map single-point running status data, while simultaneously initiating independent local feature perception for isolated nodes.

5. The smart energy operation optimization method based on AI analysis according to claim 1, characterized in that, In step S4, the process of aggregating and generating a high-dimensional spatial feature map is as follows: Based on the reconstructed topology adaptive sensing network, the real-time running status data of the nodes is captured by a time sliding window, and the data is input into the built-in time series computing unit to analyze the hidden state evolution trajectory frame by frame, and extract the time series dynamic features of the real-time running status data of the nodes. For all nodes that maintain effective physical connections, the extracted temporal dynamic features are mapped to state tensors. The attention kernel function inside the adaptive perception network is called to perform the dot product of the state tensors and calculate the high-dimensional spatial feature correlation degree between all nodes that maintain effective physical connections. Using all nodes that maintain valid physical connections as structural vertices, the calculated high-dimensional spatial feature correlation is assigned to the topological connection edges of the corresponding structural vertices, and matrix splicing of the node state dimension is performed under the global topological framework to aggregate and generate a high-dimensional spatial feature map.

6. The smart energy operation optimization method based on AI analysis according to claim 1, characterized in that, In step S5, the process of outputting the real-time energy interaction margin of each node during the transient process is as follows: The high-dimensional spatial feature map is input into the reconstructed topology adaptive sensing network, which activates the forward propagation mechanism of the topology adaptive sensing network and drives the hidden layer to generate the high-dimensional feature tensor of each node through layer-by-layer operation. The generated high-dimensional feature tensor is extracted and fed into the regression layer at the end of the topology adaptive sensing network. The theoretical energy boundary values ​​of each node in the transient process are solved by forward propagation. Extract the current actual energy value represented by the real-time operating status data of the nodes, perform a difference operation between it and the theoretical energy boundary value, and output the real-time energy interaction margin of each node in the transient process.

7. The smart energy operation optimization method based on AI analysis according to claim 6, characterized in that, The process of inputting the high-dimensional spatial feature map into the reconstructed topology-adaptive sensing network, activating the forward propagation mechanism of the topology-adaptive sensing network, and driving the hidden layers to perform layer-by-layer computation to generate the high-dimensional feature tensor of each node is as follows: The structural tensor and state features contained in the high-dimensional spatial feature map are extracted by parsing, and the structural tensor and state features are mapped to the input layer perception nodes of the reconstructed network. The forward propagation mechanism of the topology adaptive sensing network is activated, and the state features carried by the sensing nodes in the input layer are projected into the hidden layer inside the network by following the path channel defined by the structural tensor. The hidden layer is driven to perform layer-by-layer operations, and the built-in feature mapping function is called to perform spatial dimension fusion on the delivered state features, and the high-dimensional feature tensors of each node are generated at the end of the network topology.

8. The smart energy operation optimization method based on AI analysis according to claim 1, characterized in that, In step S6, the process of generating corresponding anti-limit-overrun scheduling control commands based on real-time energy interaction margin and issuing them to the corresponding node devices for execution is as follows: The calculated energy interaction margin of each node is compared with the preset safety threshold. Nodes with margins lower than the preset safety threshold are selected to exceed the limit, and the load adjustment benchmark of the node is calculated. Based on the load regulation baseline, the built-in rule base is matched, the physical action sequence corresponding to the over-limit node is retrieved, the protocol encoding of the physical action sequence is performed, and the corresponding anti-over-limit scheduling control command is generated. The addressing identifier embedded in the anti-limit-crossing scheduling control command is parsed, a downlink data link is established based on the addressing identifier, and the anti-limit-crossing scheduling control command is sent to the corresponding smart energy network node device for execution.

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