An IOC intelligent operation system and method

Abstract

The present invention relates to the field of data systems, and particularly to an IOC intelligent operation system and method. The method includes the following steps: obtaining a modeling area for the park to get the modeling area; performing multi-angle photography and point cloud data acquisition on the modeling area, and constructing a regional three-dimensional model; performing real-time multi-energy data monitoring on the modeling area, and constructing a regional energy topology network based on the real-time multi-energy data and the regional three-dimensional model; setting security nodes on the energy topology network, and analyzing and obtaining regional passage path data in combination with the regional three-dimensional model to generate a regional security sequence; performing network edge node analysis on the regional energy topology network, and generating an abnormal scheduling security strategy by comparing the obtained network edge nodes with a preset abnormal energy alarm to obtain the abnormal scheduling security strategy. The present invention effectively integrates the park three-dimensional model and the energy topology network to realize intelligent security warning and scheduling based on energy anomalies.

Description

Technical Field

[0001] The present invention relates to the field of data systems, and particularly to an IOC intelligent operation system and method. Background Art

[0002] With the development of society and the progress of technology, modern parks have become increasingly complex in terms of scale, personnel, assets, etc., posing higher requirements for security. Traditional security systems mainly rely on fixed camera monitoring and manual patrols, making it difficult to meet the growing security needs. Specifically, traditional fixed cameras have monitoring blind spots and are difficult to comprehensively cover all key areas; manual autonomous patrols are inefficient and difficult to meet the security inspection needs of large-scale parks; the IOC intelligent operation system accesses various devices and sensors through Internet of Things technology, collects a large amount of data in real time, realizes the perception of the operation environment, helps optimize the allocation and scheduling of resources such as energy, water resources, and parking spaces based on real-time data analysis, improves resource utilization efficiency, and reduces operation costs, making up for the deficiencies of traditional operation methods. However, there are still problems such as the independent operation of each subsystem, making it difficult to form a linkage security mechanism and having a slow response speed. Summary of the Invention

[0003] Based on this, it is necessary for the present invention to provide an IOC intelligent operation system and method to solve at least one of the above technical problems.

[0004] To achieve the above object, an IOC intelligent operation method includes the following steps:

[0005] Step S1: Obtain a modeling area for the park to get the modeling area; conduct multi-angle photography on the modeling area to obtain area multi-angle image data; obtain area point cloud data for the modeling area to get the area point cloud data; perform area three-dimensional modeling on the area point cloud data and the area multi-angle image data to obtain an area three-dimensional model;

[0006] Step S2: Monitor real-time multi-energy data for the modeling area to obtain real-time multi-energy data; construct an area energy topology network for the area three-dimensional model based on the real-time multi-energy data to obtain the area energy topology network;

[0007] Step S3: Set security nodes for the area energy topology network to obtain area security nodes; obtain area access path data for the area three-dimensional model to get the area access path data; generate an area security sequence for the area security nodes based on the area access path data to obtain the area security sequence;

[0008] Step S4: Analyze the network edge nodes of the regional energy topology network to obtain the network edge nodes; preset abnormal energy alarms for the regional energy topology network to obtain network abnormal energy alarms; generate an abnormal scheduling security strategy for the regional security sequence based on the network edge nodes and the network abnormal energy alarms to obtain the abnormal scheduling security strategy.

[0009] Through multi-angle photography and point cloud data collection, the present invention constructs a fine three-dimensional model of the park, laying a solid foundation for subsequent intelligent applications. This model not only truly restores the spatial information such as buildings, roads, and vegetation in the park, but also can be integrated with other system data to achieve visual management, providing an intuitive reference basis for security patrol route planning, emergency event handling, etc.; real-time collects multi-energy data such as electricity, water, and gas in the park, and constructs a regional energy topology network in combination with the three-dimensional model, making the flow and consumption of park energy clear at a glance. This network visualizes the production, transmission, use and other links of energy, helps managers to grasp the energy usage status in real time, timely discover abnormal energy consumption, and provides data support for energy optimization configuration and energy conservation and consumption reduction; comprehensively considers the energy topology network structure and the regional three-dimensional model, scientifically sets security nodes, and generates a reasonable security patrol route in combination with the regional traffic path data. This method avoids the blindness of node setting in traditional security systems, improves the utilization rate of security resources, realizes precise deployment, and effectively improves the security prevention ability of the park; by analyzing the edge nodes of the energy topology network, presetting the abnormal energy alarm threshold, and formulating a scheduling security strategy for abnormal situations of network edge nodes. This strategy can timely discover and respond to potential security risks, such as strengthening the monitoring of key areas by adjusting the security patrol route, increasing the patrol frequency, etc., realizing the dynamic scheduling of security resources, and further improving the security guarantee level of the park.

[0010] Preferably, step S1 includes the following steps:

[0011] Step S11: Obtain the modeling area by acquiring the modeling area of the park;

[0012] Step S12: Conduct multi-angle photography on the modeling area to obtain regional multi-angle image data;

[0013] Step S13: Obtain regional point cloud data by acquiring regional point cloud data of the modeling area;

[0014] Step S14: Identify and classify the regional point cloud data to obtain regional classified point cloud data;

[0015] Step S15: Identify the classified building outlines of the regional classified point cloud data to obtain regional building outline data;

[0016] Step S16: Perform dense matching on the regional multi-angle image data based on the regional point cloud data to obtain regional dense point cloud data;

[0017] Step S17: Perform regional 3D modeling on the regional dense point cloud data based on the regional building contour data to obtain a regional 3D model.

[0018] The present invention clarifies the scope of park modeling, provides a clear target area for subsequent data collection and modeling work, ensures the effective utilization of resources, and avoids blindness and repetitive work; shoots the modeling area from multiple perspectives to obtain rich image information, providing a sufficient data basis for subsequent 3D reconstruction. Multi-angle shooting can effectively make up for the deficiencies of a single perspective, restore more complete scene information, and improve the accuracy and realism of the model; high-precision regional point cloud data is obtained by using equipment such as lidar. The point cloud data can accurately express the 3D spatial information of the target, providing a reliable data basis for subsequent identification and classification and 3D modeling; the point cloud data is classified and identified to distinguish different types of point cloud data such as buildings, vegetation, and ground, providing more refined data support for subsequent building contour recognition and 3D modeling, making the model construction more accurate and efficient; based on the classified building point cloud data, the contour information of the building is identified, providing accurate building boundary constraints for subsequent 3D modeling, making the generated model more in line with the actual shape of the building; the multi-angle images are densely matched using the point cloud data to obtain dense point cloud data containing rich texture information and geometric information, providing data guarantee for generating a more realistic and detailed 3D model; based on the building contour data, 3D modeling is performed on the dense point cloud data to generate a refined 3D model containing building structure and texture information. This method effectively utilizes the advantages of point cloud data and image data, making the generated model more complete.

[0019] Preferably, step S15 includes the following steps:

[0020] Step S151: Obtain building point cloud data by acquiring building point cloud data from the regional classified point cloud data;

[0021] Step S152: Perform two-dimensional projection on the building point cloud data to obtain building two-dimensional point cloud data;

[0022] Step S153: Construct a triangular mesh surface for the building two-dimensional point cloud data to obtain a building triangular mesh surface;

[0023] Step S154: Calculate the normal vectors of the mesh triangles for the building triangular mesh surface to obtain triangular normal vector data;

[0024] Step S155: Traverse the boundary triangles of the building triangular mesh surface based on the triangular normal vector data to obtain boundary triangle data;

[0025] Step S156: Connect the boundary triangle data to form the building contour boundary, obtaining the regional building contour line segments;

[0026] Step S157: Aggregate the data of the regional building contour line segments to obtain the regional building contour data.

[0027] Through the acquisition of building point cloud data from regional classified point cloud data, the present invention can effectively separate buildings from complex urban environments, obtaining pure building point cloud data, providing a reliable data basis for subsequent building information extraction and analysis, avoiding interference from other ground object information, and improving the efficiency and accuracy of subsequent processing; performing a two-dimensional projection on the building point cloud data to simplify the three-dimensional point cloud information to a two-dimensional plane can effectively reduce the complexity of data processing while retaining the main contour features of the building, providing a convenient data form for subsequent triangular mesh surface construction and contour extraction; converting discrete point cloud data into a continuous mesh surface can more completely express the geometric shape of the building, providing a basic topological structure for subsequent normal vector calculation and boundary extraction, making subsequent processing more efficient and accurate; being able to obtain the normal vector information of each triangular surface, reflecting the direction characteristics of the building surface, providing an important judgment basis for subsequent boundary triangle identification and contour extraction; traversing the boundary triangles of the building triangular mesh surface based on the triangular normal vector data can effectively identify the triangles that form the building contour boundary, providing accurate boundary information for subsequent contour line segment connection, avoiding interference from non-boundary triangles, and improving the accuracy of contour extraction; connecting discrete boundary triangles into continuous line segments can more clearly express the contour shape of the building, providing intuitive geometric information for obtaining the final building contour data; being able to comprehensively reflect the distribution and morphological characteristics of buildings in the region, providing important data support for applications such as urban planning, architectural design, and 3D modeling.

[0028] Preferably, step S2 includes the following steps:

[0029] Step S21: Monitor real-time multi-energy data in the modeling area to obtain real-time multi-energy data;

[0030] Step S22: Perform data mapping on the regional three-dimensional model based on the real-time multi-energy data to obtain the regional multi-energy three-dimensional model;

[0031] Step S23: Set regional energy nodes for the regional multi-energy three-dimensional model to obtain regional energy nodes;

[0032] Step S24: Configure node data for the regional energy nodes to obtain the configured regional energy nodes;

[0033] Step S25: Construct a regional energy topological network for the energy nodes in the configured area based on the regional three-dimensional model to obtain the regional energy topological network.

[0034] Through real-time multi-energy data monitoring of the modeling area, the present invention can timely obtain real-time data such as production, transmission, and consumption of various energies in the area, such as electricity, heat, natural gas, etc., providing a reliable data basis for subsequent energy analysis, management, and optimization, and realizing the comprehensive perception and dynamic mastery of the regional energy system; based on the real-time multi-energy data, data mapping is performed on the regional three-dimensional model, combining abstract energy data with intuitive geographical space information to construct a regional multi-energy three-dimensional model, which can present complex energy data in a visual form, enhancing the readability and understandability of energy data and facilitating users to intuitively understand the operation status of the regional energy system; setting regional energy nodes for the regional multi-energy three-dimensional model, abstracting key facilities such as energy production, conversion, storage, and consumption into nodes to construct a regional energy node network, which can effectively simplify the complexity of the regional energy system and provide a basic framework for subsequent construction and analysis of the energy topological network; configuring node data for the regional energy nodes, setting corresponding attribute information for each node, such as energy type, capacity, efficiency, etc., to construct the configured regional energy nodes, which can describe each component of the regional energy system in more detail, provide more refined data support for subsequent energy flow analysis and optimization, and enhance the pertinence and effectiveness of energy management; constructing a regional energy topological network for the configured regional energy nodes based on the regional three-dimensional model, expressing the connection relationship between each energy node in the form of a network to construct the regional energy topological network, which can clearly show the structure and flow relationship of the regional energy system and provide an intuitive analysis tool for the planning, design, operation, and optimization of the energy system.

[0035] Preferably, step S25 includes the following steps:

[0036] Step S251: Obtain node range data for the configured regional energy nodes to obtain node range data;

[0037] Step S252: Perform node range mapping on the regional three-dimensional model based on the node range data to obtain three-dimensional node range data;

[0038] Step S253: Analyze the node spatial relationship of the three-dimensional node range data to obtain node spatial relationship data;

[0039] Step S254: Obtain adjacent area nodes for the configured regional energy nodes based on the node spatial relationship data to obtain adjacent area node data;

[0040] Step S255: Plan adjacent connection paths for the regional three-dimensional model based on adjacent area node data to obtain adjacent node path data;

[0041] Step S256: Connect the energy nodes in the configured area to form a regional energy topology network based on the adjacent node path data.

[0042] Through obtaining node range data for the energy nodes in the configured area, the present invention can clarify the influence range or service area of each energy node, such as the power supply range of a substation, the heating range of a heat station, etc., providing necessary spatial information for subsequent node spatial relationship analysis and topology network connection, and ensuring that the constructed energy topology network accurately reflects the actual situation; mapping the node range data to the regional three-dimensional model based on the node range data, visually presenting the abstract node range data on the three-dimensional model to form three-dimensional node range data, which can intuitively display the spatial distribution of the influence range of each energy node, providing an intuitive reference for subsequent spatial relationship analysis; analyzing the node spatial relationship of the three-dimensional node range data, such as determining whether there are overlapping, adjacent, inclusion, etc. relationships between nodes to obtain node spatial relationship data, which can effectively identify the spatial correlation between nodes, providing a basis for subsequent adjacent node acquisition and path planning; obtaining adjacent area node data can accurately screen out the nodes that need to be connected, providing target node information for subsequent path planning, avoiding unnecessary connections, and improving the efficiency of topology network construction; obtaining adjacent node path data can simulate the actual energy transmission path, providing specific connection information for subsequent topology network connection, making the constructed topology network more realistic and practical; connecting the energy nodes in the configured area to form a regional energy topology network based on the adjacent node path data, connecting the adjacent nodes according to the planned path to form a complete regional energy topology network, which can clearly display the node connection relationship and energy flow path of the regional energy system, providing a reliable network model for the analysis, management, and optimization of the energy system.

[0043] Preferably, step S3 includes the following steps:

[0044] Step S31: Set security nodes for the regional energy topology network to obtain regional security nodes;

[0045] Step S32: Allocate security resources for the regional security nodes to obtain security resource allocation data;

[0046] Step S33: Analyze adjacent security nodes for the regional three-dimensional model based on the regional security nodes to obtain adjacent security nodes;

[0047] Step S34: Obtain the regional passage path for the regional three-dimensional model to obtain regional passage path data;

[0048] Step S35: Analyze the security access paths of adjacent security nodes based on the regional access path data to obtain the node security access path data;

[0049] Step S36: Generate a regional security sequence for the regional security nodes based on the node security access path data and the security resource allocation data to obtain the regional security sequence.

[0050] By setting security nodes for the regional energy topology network, the present invention can deploy security monitoring devices to key energy nodes to form regional security nodes, providing infrastructure guarantee for subsequent security monitoring and safety management, and ensuring the safe and stable operation of the energy system; allocate security resources for the regional security nodes, such as allocating corresponding monitoring scopes, alarm levels, response strategies, etc. to each security node, to obtain security resource allocation data, which can rationally allocate security resources according to the importance of different nodes and the security risk levels, improve the efficiency and pertinence of the security system, and effectively reduce security risks; obtain adjacent security nodes, which can establish the spatial association relationship between security nodes, provide basic data for subsequent security access path analysis and regional security sequence generation, and realize the collaborative linkage between security nodes; obtain regional access path data, which can construct the access network within the region, provide basic path information for subsequent security access path analysis, and ensure that security monitoring covers all access paths; analyze the security access paths of adjacent security nodes based on the regional access path data, such as analyzing whether there is a passable path between adjacent security nodes, and the length, security, etc. of the path, to obtain the node security access path data, which can evaluate the accessibility and access efficiency between adjacent security nodes; for example, generate the sequence of security patrol or monitoring according to information such as the importance of security nodes, security risk levels, access paths, etc., to obtain the regional security sequence, which can formulate a reasonable security patrol or monitoring plan, and improve the efficiency and coverage of security work.

[0051] Preferably, step S36 includes the following steps:

[0052] Step S361: Evaluate the priority security nodes for the regional security nodes based on the security resource allocation data to obtain the priority security node data;

[0053] Step S362: Obtain the node patrol frequency for the regional security nodes based on the security resource allocation data to obtain the node patrol frequency data;

[0054] Step S363: Set the node security interval for the regional security nodes to obtain the node security interval data;

[0055] Step S364: Generate a node security path sequence for the node security passage path data based on the node patrol frequency data and the node security interval data, and obtain the node security path sequence;

[0056] Step S365: Generate a regional security sequence for the priority security node data based on the node security path sequence, and obtain the regional security sequence.

[0057] Through the present invention, by evaluating the priority security nodes for the regional security nodes based on the security resource allocation data, the nodes that need to be focused on and patrolled preferentially can be identified, providing a sorting basis for the subsequent generation of the regional security sequence, and ensuring that the limited security resources can preferentially guarantee the safety of key nodes; by obtaining the node patrol frequency for the regional security nodes based on the security resource allocation data, a differentiated patrol strategy can be formulated according to the security requirements of different nodes, improving the pertinence and effectiveness of the security work and avoiding resource waste; by setting the node security interval for the regional security nodes to obtain the node security interval data, the patrol time can be reasonably arranged to avoid being too frequent or having too long an interval, ensuring that each node can be monitored in a timely and effective manner while taking into account the patrol efficiency; by generating a node security path sequence for the node security passage path data based on the node patrol frequency data and the node security interval data, it is ensured that the security personnel can patrol each node in a predetermined order and frequency, improving the patrol efficiency and coverage rate; by generating a regional security sequence for the priority security node data based on the node security path sequence to obtain the regional security sequence, a globally optimized security patrol plan can be formulated, ensuring that the key nodes are patrolled preferentially and reasonably arranging the patrol route and time, maximizing the security of the regional energy system.

[0058] Preferably, step S4 includes the following steps:

[0059] Step S41: Analyze the network edge nodes of the regional energy topology network to obtain the network edge nodes;

[0060] Step S42: Preset abnormal energy alarms for the regional energy topology network to obtain network abnormal energy alarms;

[0061] Step S43: Respond to the network abnormal energy alarms based on the network edge nodes to obtain edge abnormal response nodes;

[0062] Step S44: Extract and schedule the abnormal security sequence for the regional security sequence based on the edge abnormal response nodes to obtain the abnormal scheduling security sequence;

[0063] Step S45: Generate an abnormal scheduling security strategy for the abnormal scheduling security sequence to obtain the abnormal scheduling security strategy.

[0064] By analyzing the network edge nodes of the regional energy topology network, the present invention obtains the network edge nodes, which can determine the nodes directly interacting with users in the energy system. These nodes are usually more vulnerable to external factors, so it is necessary to focus on their security and stability, providing target node information for subsequent abnormal alarm responses; presetting abnormal energy alarms for the regional energy topology network to obtain network abnormal energy alarms, which can predict potential energy security risks in advance and formulate corresponding countermeasures, improving the security and reliability of the energy system and providing a basis for judgment for subsequent abnormal alarm responses; performing abnormal alarm responses to the network abnormal energy alarms based on the network edge nodes to obtain edge abnormal response nodes, which can promptly detect abnormal situations occurring in the energy system and accurately locate the abnormal nodes, providing target node information for subsequent security sequence scheduling so as to quickly take countermeasures; extracting and scheduling abnormal security sequences for the regional security sequence based on the edge abnormal response nodes to obtain abnormal scheduling security sequences, which can dynamically adjust the security patrol plan according to the actual situation, prioritize the processing of abnormal nodes, improve the security response speed and efficiency, and promptly control security risks; generating abnormal scheduling security strategies for the abnormal scheduling security sequences to obtain abnormal scheduling security strategies, which can formulate targeted security measures according to specific situations, effectively respond to various energy security risks, ensure the safe and stable operation of the energy system, and minimize losses to the greatest extent.

[0065] Preferably, step S41 includes the following steps:

[0066] Step S411: Calculate the node centrality of the regional energy topology network to obtain the node centrality;

[0067] Step S412: Analyze the data flow direction of the regional energy topology network to obtain the data flow direction vector;

[0068] Step S413: Evaluate the node importance of the regional energy topology network based on the node centrality and the data flow direction vector to obtain the node importance;

[0069] Step S414: Identify the edge nodes of the regional energy topology network based on the node importance to obtain the preliminary edge node identification data;

[0070] Step S415: Analyze the node calculation requirements of the regional energy topology network to obtain the node calculation requirement data;

[0071] Step S416: Locate the preliminary edge node identification data based on the node calculation requirement data to obtain the network edge nodes.

[0072] By calculating the node centrality of the regional energy topological network, the present invention can quantify the importance and influence of nodes in the network. For example, nodes with high degree centrality are usually connected to more other nodes, and nodes with high betweenness centrality are usually located on the critical paths connecting different network regions, providing basic data for subsequent node importance evaluation. Analyzing the data flow direction of the regional energy topological network can grasp the flow law of energy data in the network, providing supplementary information for subsequent node importance evaluation. For example, nodes with large data flow usually undertake more important data transmission tasks. Evaluating the node importance of the regional energy topological network based on node centrality and data flow vector can more comprehensively evaluate the importance of nodes in the network, and it is necessary to focus on their security and stability, providing a basis for subsequent edge node identification. Identifying edge nodes of the regional energy topological network based on node importance can screen out less important nodes in the network, which are usually located at the network edge and connected to end users or less important devices, providing a candidate node set for subsequent edge node positioning. Analyzing the node computing requirements of the regional energy topological network can understand the computing resource requirements of each node, providing supplementary information for subsequent edge node positioning. Positioning edge nodes based on the edge node preliminary identification data and node computing requirement data can more accurately identify edge nodes in the network. These nodes usually have weak computing power and are connected to end users or less important devices, and need to perform lightweight computing and data processing, providing target node information for subsequent edge computing deployment.

[0073] Preferably, the present invention also provides an IOC intelligent operation system for executing the IOC intelligent operation method as described above. The IOC intelligent operation system includes:

[0074] A regional three-dimensional model construction module for obtaining a modeling area by performing modeling area acquisition on a park, obtaining multi-angle image data of the area by performing multi-angle photography on the modeling area, obtaining regional point cloud data by performing regional point cloud data acquisition on the modeling area, and performing regional three-dimensional modeling on the regional point cloud data and the multi-angle image data of the area to obtain a regional three-dimensional model;

[0075] A regional energy topological network construction module for monitoring real-time multi-energy data of the modeling area to obtain real-time multi-energy data, and constructing a regional energy topological network for the regional three-dimensional model based on the real-time multi-energy data to obtain a regional energy topological network;

[0076] A regional security sequence generation module for setting security nodes for the regional energy topological network to obtain regional security nodes, obtaining regional passage path data by performing regional passage path acquisition on the regional three-dimensional model, and generating a regional security sequence for the regional security nodes based on the regional passage path data to obtain a regional security sequence;

[0077] An abnormal scheduling security strategy module is used to analyze network edge nodes of a regional energy topology network to obtain network edge nodes; preset abnormal energy alarms for the regional energy topology network to obtain network abnormal energy alarms; and generate an abnormal scheduling security strategy for the regional security sequence based on the network edge nodes and the network abnormal energy alarms to obtain an abnormal scheduling security strategy.

[0078] In summary, the present invention provides an IOC intelligent operation system and method. The IOC intelligent operation system is composed of a regional three-dimensional model construction module, a regional energy topology network construction module, a regional security sequence generation module, and an abnormal scheduling security strategy module, and can implement any IOC intelligent operation method described in the present invention. It is used to realize any IOC intelligent operation method through the operations between computer programs running on each module. The internal structure of the system cooperates with each other, which can greatly reduce repetitive work and manpower input, and can quickly and effectively provide a more accurate and efficient IOC intelligent operation process, thereby simplifying the operation process of the IOC intelligent operation system. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Other features, objectives, and advantages of the present invention will become more apparent by reading the detailed description of non-limiting embodiments with reference to the following drawings:

[0080] Figure 1 It is a schematic flow chart of the steps of an IOC intelligent operation method of the present invention;

[0081] Figure 2 is Figure 1 a detailed schematic flow chart of step S3 in

[0082] Figure 3 is Figure 2 a detailed schematic flow chart of step S36 in DETAILED DESCRIPTION OF THE EMBODIMENTS

[0083] The technical method of the present invention will be clearly and completely described below with reference to the drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0084] In addition, the attached drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0085] It should be understood that although terms such as "first" and "second" may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0086] To achieve the above object, please refer to Figures 1 to 3 , the present invention provides an IOC intelligent operation method, including the following steps:

[0087] Step S1: Obtain a modeling area for the park to obtain a modeling area; perform multi-angle photography on the modeling area to obtain area multi-angle image data; obtain area point cloud data for the modeling area; perform area three-dimensional modeling on the area point cloud data and the area multi-angle image data to obtain an area three-dimensional model;

[0088] Step S2: Monitor real-time multi-energy data for the modeling area to obtain real-time multi-energy data; construct an area energy topology network for the area three-dimensional model based on the real-time multi-energy data to obtain an area energy topology network;

[0089] Step S3: Set security nodes for the area energy topology network to obtain area security nodes; obtain area access path data for the area three-dimensional model; generate an area security sequence for the area security nodes based on the area access path data to obtain an area security sequence;

[0090] Step S4: Analyze network edge nodes for the area energy topology network to obtain network edge nodes; preset abnormal energy alarms for the area energy topology network to obtain network abnormal energy alarms; generate an abnormal scheduling security strategy for the area security sequence based on the network edge nodes and the network abnormal energy alarms to obtain an abnormal scheduling security strategy.

[0091] In the embodiments of the present invention, please refer to Figure 1As shown in the figure, it is a schematic diagram of the step flow of the IOC intelligent operation method of the present invention. In this example, the IOC intelligent operation method includes the following steps:

[0092] Step S1: Obtain the modeling area of the park to get the modeling area; conduct multi-angle photography on the modeling area to obtain area multi-angle image data; obtain area point cloud data for the modeling area to get area point cloud data; perform area three-dimensional modeling on the area point cloud data and the area multi-angle image data to obtain an area three-dimensional model;

[0093] In an embodiment of the present invention, the range of the modeling area is determined through on-site investigation or by referring to a map, and the boundary information is recorded. Then, a drone or camera is used for multi-angle photography, and a lidar or photogrammetry software is used to obtain area point cloud data. Next, point cloud processing software is used to segment and classify the point cloud data to identify the building outlines. On this basis, dense matching is performed on the multi-angle images based on the area point cloud data to generate high-density point cloud data. Finally, grid reconstruction is performed on the high-density point cloud using the area building outline data to generate the final three-dimensional model of the park.

[0094] Step S2: Monitor the real-time multi-energy data of the modeling area to obtain real-time multi-energy data; construct a regional energy topology network for the regional three-dimensional model based on the real-time multi-energy data to obtain a regional energy topology network;

[0095] In an embodiment of the present invention, the types of energy to be monitored are determined, and sensors are deployed within the modeling area to collect real-time energy data. Then, according to the elements in the three-dimensional model, the real-time multi-energy data is mapped to the corresponding three-dimensional model elements. Next, according to the regional energy system structure and energy flow direction, regional energy nodes are set in the three-dimensional model, and data configuration is performed for each energy node, including node attribute data and associated data. Finally, based on the three-dimensional model and node data, a regional energy topology network is constructed to represent the connection relationship and energy flow direction between energy nodes.

[0096] Step S3: Set security nodes for the regional energy topology network to obtain regional security nodes; obtain regional traffic path data for the regional three-dimensional model to get regional traffic path data; generate a regional security sequence for the regional security nodes based on the regional traffic path data to obtain a regional security sequence;

[0097] In an embodiment of the present invention, by reading the regional energy topology network data, key nodes are selected as security nodes according to the security level and importance. Then, corresponding security resources are allocated according to the importance, risk level and security requirements of each security node. Next, based on the three-dimensional model, the spatial relationship between each security node and other surrounding security nodes is analyzed to determine the adjacent security nodes of each security node, and the traffic path data within the region is extracted. After that, for each security node and its adjacent nodes, the feasible traffic paths between the nodes are analyzed, and the optimal security traffic path is selected. Finally, according to the importance, risk level and security resource configuration of the security nodes, a regional security sequence is generated to guide the order of security patrol or monitoring.

[0098] Step S4: Analyze the network edge nodes of the regional energy topology network to obtain the network edge nodes; preset the abnormal energy alarm for the regional energy topology network to obtain the network abnormal energy alarm; generate the abnormal scheduling security strategy for the regional security sequence based on the network edge nodes and the network abnormal energy alarm.

[0099] In an embodiment of the present invention, by using the graph theory algorithm to calculate the connection degree of each node in the regional energy topology network, the network edge nodes with connection degrees lower than the preset threshold are identified. Then, according to the actual demand and historical data analysis, the abnormal energy alarm rules are preset, and alarm information is generated when abnormal data is detected. Next, the alarm information is associated with the network edge nodes, the affected edge nodes are screened out, and their risk levels are evaluated. Subsequently, according to the risk level and geographical location of the edge nodes, the corresponding security resources are extracted and scheduled from the regional security sequence to form an abnormal scheduling security sequence. Finally, according to the characteristics of the security resources and the preset policy execution process, a detailed abnormal scheduling security strategy is generated.

[0100] Through multi-angle photography and point cloud data acquisition, the present invention constructs a detailed 3D model of the park, laying a solid foundation for subsequent intelligent applications. This model not only truly restores the spatial information of buildings, roads, vegetation, etc. in the park, but also can be integrated with other system data to achieve visual management, providing an intuitive reference basis for security patrol route planning, emergency event handling, etc.; real-time collects multi-energy data such as electricity, water, and gas in the park, and constructs a regional energy topology network in combination with the 3D model, making the flow and consumption of park energy clear at a glance. This network visualizes the production, transmission, use and other links of energy, helps managers grasp the energy usage status in real time, timely discovers abnormal energy consumption, and provides data support for energy optimization configuration and energy conservation and consumption reduction; comprehensively considers the energy topology network structure and the regional 3D model, scientifically sets security nodes, and generates a reasonable security patrol route in combination with the regional traffic path data. This method avoids the blindness of node setting in traditional security systems, improves the utilization rate of security resources, realizes precise deployment, and effectively enhances the security prevention ability of the park; by analyzing the edge nodes of the energy topology network, presetting abnormal energy alarm thresholds, and formulating a dispatching security strategy for abnormal situations of network edge nodes. This strategy can timely discover and respond to potential security risks, such as strengthening the monitoring of key areas by adjusting the security patrol route, increasing the patrol frequency, etc., realizing the dynamic dispatching of security resources, and further enhancing the security guarantee level of the park.

[0101] Preferably, step S1 includes the following steps:

[0102] Step S11: Obtain the modeling area of the park to obtain the modeling area;

[0103] In the embodiment of the present invention, by obtaining the modeling area of the park, for example, through on-site investigation or referring to the park map, determining the area range that needs to be 3D modeled, the coordinates of the area boundary points can be recorded using a GPS device, or the area boundary line can be drawn using map software, and the boundary information is saved in the GIS data format.

[0104] Step S12: Perform multi-angle photography on the modeling area to obtain regional multi-angle image data;

[0105] In the embodiment of the present invention, by performing multi-angle photography on the modeling area, for example, using a drone or a handheld camera, taking photos of the modeling area from multiple angles. When taking photos, it is necessary to ensure sufficient overlap for subsequent dense matching. Professional photogrammetry software can be used to plan flight routes and shooting parameters, and a high-resolution camera can be used to take clear photos.

[0106] Step S13: Obtain regional point cloud data of the modeling area to obtain regional point cloud data;

[0107] In an embodiment of the present invention, by acquiring regional point cloud data for a modeling area, for example, using a lidar scanner or photogrammetry software, the point cloud data of the modeling area is obtained. The lidar scanner can directly obtain high-precision point cloud data, and the photogrammetry software can generate point cloud data by processing multi-angle image data. A ground lidar scanner can be used, or a lidar scanner carried by a drone can be used.

[0108] Step S14: Identify and classify the regional point cloud data to obtain regional classified point cloud data;

[0109] In an embodiment of the present invention, by using point cloud processing software, the point cloud data is segmented and classified, and the point cloud data is divided into different categories, such as buildings, vegetation, ground, etc. A rule-based classification method can be used, for example, classifying according to features such as the height, density, and color of the point cloud, or a machine learning-based classification method can be used, for example, using a random forest or support vector machine to train a classification model and using the trained model to classify the point cloud data.

[0110] Step S15: Identify the building outlines of the regional classified point cloud data to obtain regional building outline data;

[0111] In an embodiment of the present invention, by performing building outline recognition on the regional classified point cloud data, the building point cloud data is extracted, and the outline lines of the buildings are identified. A method based on plane fitting can be used to fit the building walls and extract the boundary lines of the walls, or a method based on edge detection, such as the Canny operator, can be used to detect the edges of the building point cloud and connect the edge points to form outline lines.

[0112] Step S16: Perform dense matching on the regional multi-angle image data based on the regional point cloud data to obtain regional dense point cloud data;

[0113] In an embodiment of the present invention, by performing dense matching on the regional multi-angle image data based on the regional point cloud data, for example, using photogrammetry software, using the regional point cloud data as a reference, performing dense matching on the multi-angle image data, and generating high-density point cloud data. A feature point-based matching method can be used, such as the SIFT algorithm and the SURF algorithm, to extract image feature points and perform matching, or a pixel-based matching method, such as semi-global matching and stereo matching algorithms, can be used to match the image pixels.

[0114] Step S17: Perform regional 3D modeling on the regional dense point cloud data based on the regional building outline data to obtain a regional 3D model.

[0115] In the embodiments of the present invention, by performing three-dimensional modeling of regional dense point cloud data based on regional building contour data, for example, using three-dimensional modeling software, and using the regional building contour data as a constraint to reconstruct a grid for the regional dense point cloud data to generate a three-dimensional model of the region, a grid reconstruction method based on triangulation, such as the Poisson reconstruction algorithm, can be used to generate a triangular mesh model, or a grid reconstruction method based on surface fitting, such as the B-spline surface fitting algorithm, can be used to generate a smooth surface model.

[0116] The present invention provides a clear target area for subsequent data collection and modeling work by defining the scope of park modeling, ensuring the effective use of resources and avoiding blindness and duplication of work; capturing the modeling area from multiple perspectives to obtain rich image information, providing an adequate data basis for subsequent three-dimensional reconstruction. Multi-angle shooting can effectively make up for the deficiencies of a single perspective, restore more complete scene information, and improve the accuracy and realism of the model; high-precision regional point cloud data is obtained using devices such as lidar. The point cloud data can accurately represent the three-dimensional spatial information of the target, providing a reliable data basis for subsequent identification classification and three-dimensional modeling; classifying and identifying the point cloud data to distinguish different types of point cloud data such as buildings, vegetation, and ground, providing more refined data support for subsequent building contour recognition and three-dimensional modeling, making the model construction more accurate and efficient; based on the classified building point cloud data, identifying the contour information of the building, providing precise building boundary constraints for subsequent three-dimensional modeling, making the generated model more conform to the actual shape of the building; using the point cloud data to perform dense matching on multi-angle images to obtain dense point cloud data containing rich texture information and geometric information, providing data guarantee for generating a more realistic and detailed three-dimensional model; based on the building contour data, performing three-dimensional modeling on the dense point cloud data to generate a refined three-dimensional model containing building structure and texture information. This method effectively utilizes the advantages of point cloud data and image data, making the generated model more complete.

[0117] Preferably, step S15 includes the following steps:

[0118] Step S151: Obtain building point cloud data from the regional classified point cloud data to obtain building point cloud data;

[0119] In the embodiments of the present invention, by classifying point cloud data in a clear area, this refers to point cloud data that has been classified based on features such as color, shape, and height, which contains information about different types of objects. Next, it is necessary to extract the point cloud data corresponding to the building from this classified point cloud data. This usually requires using known building classification labels or preset building feature thresholds. For example, buildings usually have features such as height, density, and planar shape. By these features, the points belonging to the building are screened out. Specific implementation can adopt various algorithms, such as region-growing-based, model-matching-based, machine-learning-based methods, etc., to screen out the points that meet the conditions and store them as new point cloud data.

[0120] Step S152: Perform a two-dimensional projection on the building point cloud data to obtain building two-dimensional point cloud data;

[0121] In the embodiments of the present invention, by reading the obtained building point cloud data and selecting a suitable projection plane, usually the horizontal plane is selected as the projection plane. Then, traverse each point in the building point cloud data, extract its X and Y coordinates, and project them onto the projection plane. The projected point cloud data only contains X and Y coordinates, and the Z coordinate is ignored.

[0122] Step S153: Construct a triangular mesh surface for the building two-dimensional point cloud data to obtain a building triangular mesh surface;

[0123] In the embodiments of the present invention, by reading the obtained building two-dimensional point cloud data, the Delaunay triangulation algorithm is used to construct a triangular mesh for the two-dimensional point cloud data. The Delaunay triangulation algorithm can ensure that the generated triangles are close to equilateral triangles and avoid generating overly long or flat triangles. During the process of constructing the triangular mesh, appropriate parameters need to be set, such as the maximum side length and minimum angle of the triangle. The generated triangular meshes are combined into a triangular mesh surface.

[0124] Step S154: Calculate the normal vectors of the triangles on the building triangular mesh surface to obtain triangle normal vector data;

[0125] In the embodiments of the present invention, by reading the building triangular mesh surface data, traverse each triangle in the triangular mesh surface and calculate the normal vector of each triangle. The calculation method of the triangle normal vector is to take the coordinates of the three vertices of the triangle, calculate the cross product of two sides to obtain a vector, and then normalize it to obtain the normal vector of the triangle. The direction of the normal vector can be adjusted according to needs. For example, it can be uniformly set to point to the outside of the building.

[0126] Step S155: Traverse the boundary triangles of the building triangular mesh surface based on the triangle normal vector data to obtain boundary triangle data;

[0127] In an embodiment of the present invention, by reading the triangular normal vector data and the building triangular mesh surface data, each triangle in the triangular mesh is traversed to check its adjacent triangles around it. If a triangle has an opposite normal vector direction to its adjacent triangle, then this triangle is considered a boundary triangle. For example, if the normal vector of a triangle points to the outside of the building while the normal vector of its adjacent triangle points to the inside of the building, then this triangle is considered a boundary triangle.

[0128] Step S156: Connect the boundary triangle data to form the regional building contour line segments.

[0129] In an embodiment of the present invention, by traversing the boundary triangle data, the sides of each triangle are extracted. If a side belongs to two boundary triangles simultaneously, then this side is considered an internal side and is removed; if a side belongs to only one boundary triangle, then this side is considered a boundary side and is saved to the boundary line segment dataset. All the boundary line segment data are connected to form the building contour line segments.

[0130] Step S157: Aggregate the data of the regional building contour line segments to obtain the regional building contour data.

[0131] In an embodiment of the present invention, by traversing the obtained regional building contour line segment data, all the building contour line segments are integrated. The building contour line segments within all regions are combined together to form a complete regional building contour. This includes merging overlapping parts, filling any gaps, and simplifying the contour line segments to achieve a smoother representation, thereby forming the complete regional building contour data.

[0132] The present invention can effectively separate buildings from complex urban environments by obtaining building point cloud data from classified regional point cloud data, obtaining pure building point cloud data, providing a reliable data basis for subsequent building information extraction and analysis, avoiding interference from other ground object information, and improving the efficiency and accuracy of subsequent processing; performing two-dimensional projection on the building point cloud data to simplify the three-dimensional point cloud information to a two-dimensional plane, which can effectively reduce the complexity of data processing while retaining the main contour features of the building, providing a convenient data form for subsequent triangular mesh surface construction and contour extraction; converting the discrete point cloud data into a continuous mesh surface, which can more completely express the geometric shape of the building, providing a basic topological structure for subsequent normal vector calculation and boundary extraction, making subsequent processing more efficient and accurate; being able to obtain the normal vector information of each triangular surface, reflecting the direction characteristics of the building surface, providing an important judgment basis for subsequent boundary triangle recognition and contour extraction; traversing the boundary triangles of the building triangular mesh surface based on the triangular normal vector data, which can effectively identify the triangles constituting the building contour boundary, providing accurate boundary information for subsequent contour line segment connection, avoiding interference from non-boundary triangles, and improving the accuracy of contour extraction; connecting the discrete boundary triangles into continuous line segments, which can more clearly express the contour shape of the building, providing intuitive geometric information for obtaining the final building contour data; being able to comprehensively reflect the distribution and morphological characteristics of buildings in the region, providing important data support for applications such as urban planning, architectural design, and 3D modeling.

[0133] Preferably, step S2 includes the following steps:

[0134] Step S21: Monitor real-time multi-energy data for the modeling area to obtain real-time multi-energy data;

[0135] In the embodiment of the present invention, by determining the types of energy to be monitored, such as electricity, natural gas, heat, water, etc., corresponding sensors are deployed in the modeling area, such as electricity meters, natural gas flow meters, heat flow meters, water meters, etc., for collecting real-time energy data. These sensors can be connected to the data acquisition system by wired or wireless means, and the data acquisition system is responsible for collecting sensor data and storing it in the database.

[0136] Step S22: Perform data mapping on the regional three-dimensional model based on the real-time multi-energy data to obtain a regional multi-energy three-dimensional model;

[0137] In an embodiment of the present invention, by according to elements such as buildings, roads, pipelines, etc. in the regional three-dimensional model, real-time multi-energy data is mapped onto the corresponding three-dimensional model elements. For example, power data is mapped onto the power facilities of buildings, natural gas data is mapped onto natural gas pipelines, and heat data is mapped onto heat pipelines. The mapping process can be achieved by establishing data association relationships. For example, each sensor is associated with a specific element in the three-dimensional model, and the sensor data is mapped onto this element.

[0138] Step S23: Set regional energy nodes for the regional multi-energy three-dimensional model to obtain regional energy nodes;

[0139] In an embodiment of the present invention, by according to the regional energy system structure and energy flow direction, regional energy nodes are set in the three-dimensional model. Energy nodes can be energy production nodes, energy consumption nodes, energy conversion nodes, etc. For example, a power plant can be set as a power production node, a production area can be set as a power consumption node, and a combined heat and power plant can be set as a power and heat conversion node. The setting of nodes can be achieved by adding point elements in the three-dimensional model, and corresponding attributes are set for each node, such as node type, node name, node coordinates, etc.

[0140] Step S24: Configure node data for the regional energy nodes to obtain configured regional energy nodes;

[0141] In an embodiment of the present invention, by configuring data for each energy node, including the attribute data and association data of the node. The node attribute data includes node type, node name, node capacity, node efficiency, etc. The node association data includes the connection relationship between nodes, the relationship between nodes and energy pipelines, etc. For example, power generation capacity, power generation efficiency, etc. can be configured for the power production node, and power consumption, power load, etc. can be configured for the power consumption node. The node data configuration can be achieved by establishing a relational database.

[0142] Step S25: Based on the regional three-dimensional model, construct a regional energy topological network for the configured regional energy nodes to obtain a regional energy topological network.

[0143] In an embodiment of the present invention, by based on the three-dimensional model and node data, a regional energy topological network is constructed. The topological network represents the connection relationship between energy nodes and the energy flow direction. For example, according to the connection relationship between nodes, a power network, a natural gas network, a heat network, etc. can be constructed. The topological network can be represented by a graph structure, where nodes represent energy nodes and edges represent the connection relationship between nodes.

[0144] By monitoring real-time multi-energy data in the modeling area, the present invention can timely obtain real-time data such as production, transmission, and consumption of various energies in the area, such as electricity, heat, natural gas, etc., providing a reliable data basis for subsequent energy analysis, management, and optimization, and realizing the comprehensive perception and dynamic control of the regional energy system; based on the real-time multi-energy data, data mapping is performed on the regional three-dimensional model, combining abstract energy data with intuitive geographical spatial information to construct a regional multi-energy three-dimensional model, which can present complex energy data in a visual form, enhancing the readability and understandability of energy data and facilitating users to intuitively understand the operating status of the regional energy system; by setting regional energy nodes for the regional multi-energy three-dimensional model, abstracting key facilities such as energy production, conversion, storage, and consumption into nodes to construct a regional energy node network, which can effectively simplify the complexity of the regional energy system and provide a basic framework for subsequent construction and analysis of the energy topology network; by configuring node data for the regional energy nodes, setting corresponding attribute information for each node, such as energy type, capacity, efficiency, etc., to construct a configured regional energy node, which can more detailedly describe each component of the regional energy system, provide more refined data support for subsequent energy flow analysis and optimization, and enhance the pertinence and effectiveness of energy management; based on the regional three-dimensional model, a regional energy topology network is constructed for the configured regional energy nodes, expressing the connection relationship between each energy node in the form of a network to construct a regional energy topology network, which can clearly display the structure and flow relationship of the regional energy system and provide an intuitive analysis tool for the planning, design, operation, and optimization of the energy system.

[0145] Preferably, step S25 includes the following steps:

[0146] Step S251: Obtain node range data for the configured regional energy nodes to obtain node range data;

[0147] In the embodiment of the present invention, by reading the configured regional energy node data, each node contains information such as node type and position coordinates. According to the node type and function, the influence range or service range of each node is determined. For example, for a heat station, its node range can be set as the area within a certain radius centered on the heat station, and the buildings in this area use the heat resources provided by the heat station. The determination of the node range can be set according to the actual situation. For example, it can be determined according to the coverage range of heat pipelines, the coverage range of power lines, etc.

[0148] Step S252: Perform node range mapping on the regional three-dimensional model based on the node range data to obtain three-dimensional node range data;

[0149] In an embodiment of the present invention, by mapping node range data onto a three-dimensional model, three-dimensional node range data is formed. The mapping process can adopt various methods. For example, a sphere or a cylinder can be generated in the three-dimensional model according to the center point coordinates and radius of the node range to represent the node range; alternatively, a polygon or other geometric figure can be generated in the three-dimensional model according to the boundary data of the node range to represent the node range.

[0150] Step S253: Analyze the spatial relationship of nodes in the three-dimensional node range data to obtain node spatial relationship data;

[0151] In an embodiment of the present invention, by analyzing the spatial relationship of the ranges of different nodes, the spatial relationship between the nodes is judged. The spatial relationship analysis can adopt various methods. For example, it can be judged whether the ranges of two nodes intersect, whether one contains the other, whether they are adjacent, etc. For example, it can be judged whether the ranges of two heat stations overlap, or whether the range of one heat station contains the range of another heat station. The analysis result can be stored as a relationship table, which contains the spatial relationship information between each pair of nodes. For example, node A intersects with node B, and node C contains node D, etc.

[0152] Step S254: Based on the node spatial relationship data, obtain adjacent area node data by acquiring adjacent area nodes for the configured area energy nodes;

[0153] In an embodiment of the present invention, by according to the node spatial relationship data, the adjacent nodes of each node are screened. For example, if the ranges of two nodes intersect or are adjacent, then these two nodes are considered adjacent nodes. The screening process can traverse the node spatial relationship data, find the relationship records between each node and its adjacent nodes, and extract the information of the adjacent nodes, such as node ID, node type, node coordinates, etc.

[0154] Step S255: Based on the adjacent area node data, plan adjacent connection paths for the configured area energy nodes to obtain adjacent node path data;

[0155] In an embodiment of the present invention, for each node and its adjacent nodes, connection paths are planned in the three-dimensional model. The path planning can adopt various algorithms, such as the shortest path algorithm, A* algorithm, etc. According to the elements such as roads and pipelines in the three-dimensional model, the optimal path connecting two nodes is found. The result of the path planning can be a sequence of point coordinates representing the trend of the path.

[0156] Step S256: Based on the adjacent node path data, perform regional energy topological network connection on the configured area energy nodes to obtain a regional energy topological network.

[0157] In an embodiment of the present invention, by establishing a connection relationship between nodes based on adjacent node path data, a regional energy topology network is formed. The topology network can be represented by a graph structure, where nodes represent energy nodes, edges represent the connection relationship between nodes, and the weight of an edge can represent the length of a path or other attributes.

[0158] Through obtaining node range data for configured regional energy nodes, the present invention can clarify the influence range or service area of each energy node, such as the power supply range of a substation, the heating range of a heat station, etc., providing necessary spatial information for subsequent node spatial relationship analysis and topology network connection, and ensuring that the constructed energy topology network accurately reflects the actual situation; mapping the node range data to the regional three-dimensional model based on the node range data, visually presenting the abstract node range data on the three-dimensional model to form three-dimensional node range data, which can intuitively show the spatial distribution of the influence range of each energy node and provide an intuitive reference for subsequent spatial relationship analysis; performing node spatial relationship analysis on the three-dimensional node range data, such as determining whether there are overlapping, adjacent, inclusion, etc. relationships between nodes to obtain node spatial relationship data, which can effectively identify the spatial correlation between nodes and provide a basis for subsequent adjacent node acquisition and path planning; obtaining adjacent regional node data, which can accurately screen out the nodes that need to be connected, providing target node information for subsequent path planning, avoiding unnecessary connections, and improving the efficiency of topology network construction; obtaining adjacent node path data, which can simulate the actual energy transmission path and provide specific connection information for subsequent topology network connection, making the constructed topology network more realistic and practical; connecting the configured regional energy nodes based on the adjacent node path data to form a complete regional energy topology network, which can clearly show the node connection relationship and energy flow path of the regional energy system and provide a reliable network model for the analysis, management, and optimization of the energy system.

[0159] Preferably, step S3 includes the following steps:

[0160] Step S31: Set security nodes for the regional energy topology network to obtain regional security nodes;

[0161] Step S32: Allocate security resources to the regional security nodes to obtain security resource allocation data;

[0162] Step S33: Analyze adjacent security nodes for the regional three-dimensional model based on the regional security nodes to obtain adjacent security nodes;

[0163] Step S34: Obtain the regional passage path for the regional three-dimensional model to obtain regional passage path data;

[0164] Step S35: Analyze the security access paths of adjacent security nodes based on the regional access path data to obtain the node security access path data;

[0165] Step S36: Generate a regional security sequence for the regional security nodes based on the node security access path data and the security resource allocation data to obtain the regional security sequence.

[0166] As an embodiment of the present invention, referring to Figure 2 shown, for Figure 1 the detailed step flow diagram of step S3 in

[0167] Step S31: Set security nodes for the regional energy topology network to obtain regional security nodes;

[0168] In the embodiment of the present invention, by reading the regional energy topology network data, which includes energy nodes, connection relationships between nodes, and path information. According to the security level, importance, and potential risks of the regional energy system, key nodes are selected as security nodes in the energy topology network. For example, key nodes of power plants, substations, key nodes of transmission lines, important energy storage facilities, etc. can be selected as security nodes.

[0169] Step S32: Allocate security resources to the regional security nodes to obtain security resource allocation data;

[0170] In the embodiment of the present invention, by according to the importance, risk level, and security requirements of each security node, corresponding security resources are allocated. Security resources can include video surveillance devices, infrared detectors, personnel patrols, intelligent security robots, etc. Resource allocation needs to consider factors such as the type, quantity, deployment location, and cost of resources. For example, for an important power plant, more video surveillance devices and personnel patrols can be allocated; for a transmission line with a lower risk, intelligent security robots can be deployed for inspection.

[0171] Step S33: Analyze adjacent security nodes for the regional three-dimensional model based on the regional security nodes to obtain adjacent security nodes;

[0172] In the embodiment of the present invention, by based on the three-dimensional model, analyze the spatial relationship between each security node and other security nodes around it to determine the adjacent security nodes of each security node. Spatial analysis methods, such as buffer analysis, proximity analysis, etc., can be used to determine other security nodes within a certain distance range around each security node. For example, a buffer can be set, and if the buffers of two security nodes intersect, then these two nodes are considered adjacent nodes.

[0173] Step S34: Obtain the regional passage path for the regional three-dimensional model to get the regional passage path data;

[0174] In the embodiment of the present invention, based on the three-dimensional model, the passage path data within the region is extracted. The passage path can be places where passage is possible, such as roads, sidewalks, pipe galleries, etc. The passage path can be extracted using the feature information in the three-dimensional model, such as road networks, sidewalk networks, etc. It is also possible to use path planning algorithms to plan the passage path according to the terrain, obstacles, etc. information in the three-dimensional model.

[0175] Step S35: Analyze the security passage path for adjacent security nodes based on the regional passage path data to get the node security passage path data;

[0176] In the embodiment of the present invention, for each security node and its adjacent nodes, the feasible passage paths between the nodes are analyzed, and the optimal security passage path is selected. According to the regional passage path data, combined with the spatial relationship between the nodes, the shortest path or the safest path connecting two nodes can be found. For example, paths with higher security such as roads and sidewalks can be preferentially selected, and paths passing through dangerous areas can be avoided.

[0177] Step S36: Generate a regional security sequence for the regional security nodes based on the node security passage path data and the security resource allocation data to get the regional security sequence.

[0178] In the embodiment of the present invention, the regional security sequence is generated according to the importance, risk level of the security nodes and the configuration of security resources. The security sequence can be an ordered list of nodes, indicating the order of security inspections or monitoring. For example, security nodes with high importance can be preferentially inspected, and a reasonable inspection order can be arranged according to the distance and passage path between the nodes to obtain the regional security sequence.

[0179] By setting security nodes for the regional energy topology network, the present invention can deploy security monitoring devices to key energy nodes to form regional security nodes, providing infrastructure guarantee for subsequent security monitoring and safety management, and ensuring the safe and stable operation of the energy system; allocating security resources to the regional security nodes, such as allocating corresponding monitoring scopes, alarm levels, response strategies, etc. to each security node, obtaining security resource allocation data, and being able to reasonably allocate security resources according to the importance of different nodes and the security risk levels, improving the efficiency and pertinence of the security system and effectively reducing security risks; obtaining adjacent security nodes, being able to establish the spatial association relationship between security nodes, providing basic data for subsequent security passage path analysis and regional security sequence generation, and realizing the collaborative linkage between security nodes; obtaining regional passage path data, being able to construct the passage network within the region, providing basic path information for subsequent security passage path analysis, and ensuring that security monitoring covers all passage paths; performing security passage path analysis on adjacent security nodes based on the regional passage path data, such as analyzing whether there is a passable path between adjacent security nodes, and the length, security, etc. of the path, obtaining node security passage path data, and being able to evaluate the accessibility and passage efficiency between adjacent security nodes; for example, generating the sequence of security patrol or monitoring according to information such as the importance of security nodes, security risk levels, passage paths, etc., obtaining the regional security sequence, and being able to formulate a reasonable security patrol or monitoring plan, improving the efficiency and coverage of security work.

[0180] Preferably, step S36 includes the following steps:

[0181] Step S361: Based on the security resource allocation data, conduct priority security node assessment on the regional security nodes to obtain priority security node data;

[0182] Step S362: Based on the security resource allocation data, obtain the node patrol frequency of the regional security nodes to obtain node patrol frequency data;

[0183] Step S363: Set the node security interval for the regional security nodes to obtain node security interval data;

[0184] Step S364: Based on the node patrol frequency data and the node security interval data, generate a node security path sequence for the node security passage path data to obtain a node security path sequence;

[0185] Step S365: Based on the node security path sequence, generate a regional security sequence for the priority security node data to obtain a regional security sequence.

[0186] As an embodiment of the present invention, refer to Figure 3 shown, for Figure 2The detailed step - by - step process schematic diagram of step S36. In this embodiment, step S36 includes the following steps:

[0187] Step S361: Based on the security resource allocation data, conduct a priority security node assessment on the regional security nodes to obtain priority security node data;

[0188] In the embodiments of the present invention, by considering factors such as the type and quantity of security resources, and the importance and risk level of security nodes, a priority assessment is conducted on all security nodes. For example, nodes with more video surveillance devices and personnel patrols can be rated as high - priority nodes, while nodes with fewer security resources can be rated as low - priority nodes. The priority assessment can adopt various methods. For example, according to factors such as the total value of security resources and the weight of risk levels, calculate the priority scores of each node and sort them according to the scores.

[0189] Step S362: Based on the security resource allocation data, obtain the node patrol frequency of the regional security nodes to get node patrol frequency data;

[0190] In the embodiments of the present invention, by considering the type and quantity of security resources and the priority of priority security nodes, determine the patrol frequency of each security node. For example, high - priority nodes can be set with a higher patrol frequency, such as patrolling once an hour; low - priority nodes can be set with a lower patrol frequency, such as patrolling once a day. The setting of the patrol frequency needs to consider factors such as the availability of security resources, the importance of nodes, and the risk level.

[0191] Step S363: Set the node security interval for the regional security nodes to obtain node security interval data;

[0192] In the embodiments of the present invention, by considering the node patrol frequency and security requirements, set the security interval time for each security node. The setting of the security interval time needs to consider factors such as the risk level of the node, the response time of security resources, and security strategies.

[0193] Step S364: Based on the node patrol frequency data and the node security interval data, generate a node security path sequence for the node security passage path data to obtain a node security path sequence;

[0194] In the embodiments of the present invention, by considering the node patrol frequency, security interval, and the security passage path between nodes, generate the security path sequence of each node. The security path sequence is an ordered list of path nodes, indicating the path order of security operations. For example, for the generation of a node path sequence, the distance and passage time between nodes need to be considered to form a node security path sequence.

[0195] Step S365: Generate a regional security sequence for the priority security node data based on the node security path sequence to obtain a regional security sequence.

[0196] In the embodiment of the present invention, a regional security sequence is generated according to the priority of the priority security nodes and the node security path sequence. The regional security sequence is an overall order list of security actions, indicating the security action order of the entire region. For example, the security path sequence of high-priority nodes can be executed first, and then the security path sequence of low-priority nodes can be executed.

[0197] In the present invention, by evaluating the priority security nodes of the regional security nodes based on the security resource allocation data, the nodes that need to be focused on and patrolled preferentially can be identified, providing a sorting basis for the subsequent generation of the regional security sequence, ensuring that the limited security resources can preferentially guarantee the safety of key nodes; obtaining the node patrol frequency of the regional security nodes based on the security resource allocation data can formulate a differentiated patrol strategy according to the security requirements of different nodes, improving the pertinence and effectiveness of security work and avoiding resource waste; setting the node security interval for the regional security nodes to obtain the node security interval data can reasonably arrange the patrol time, avoiding excessive frequency or too long intervals, ensuring that each node can be monitored in a timely and effective manner while taking into account the patrol efficiency; generating the node security path sequence for the node security path data based on the node patrol frequency data and the node security interval data to ensure that security personnel can patrol each node in a predetermined order and frequency, improving the patrol efficiency and coverage; generating the regional security sequence for the priority security node data based on the node security path sequence to obtain the regional security sequence, which can formulate a globally optimized security patrol plan, ensuring that key nodes are patrolled preferentially and reasonably arranging the patrol route and time, maximizing the security of the regional energy system.

[0198] Preferably, step S4 includes the following steps:

[0199] Step S41: Analyze the network edge nodes of the regional energy topology network to obtain network edge nodes;

[0200] In the embodiment of the present invention, through the regional energy topology network model, using graph theory algorithms, such as the degree centrality algorithm, the connection degree of each node is calculated, that is, the number of other nodes directly connected to the node. Nodes with low connection degrees are usually located at the network edge. A connection degree threshold is set, and nodes below this threshold are identified as network edge nodes. The selection of the threshold needs to be adjusted according to the specific network scale and structure to ensure that the identified nodes can represent the network edge, and a list of edge nodes is output.

[0201] Step S42: Preset abnormal energy alarms for the regional energy topology network to obtain network abnormal energy alarms;

[0202] In the embodiments of the present invention, by analyzing according to actual requirements and historical data, indicators for judging energy anomalies are determined, such as the abnormal fluctuation ranges of voltage, current, power, frequency, etc. For each abnormal indicator, corresponding warning thresholds are set, such as the voltage fluctuating up and down by more than 10%, the current exceeding 1.2 times the rated value, etc. According to the actual situation, different alarm rules are configured, such as: single - indicator over - limit alarm, multi - indicator correlation alarm, trend anomaly; when abnormal energy data that meets the preset conditions appears in the network, an alarm containing information such as the abnormal type, time, location, severity, etc. is generated.

[0203] Step S43: Based on the network edge nodes, perform an abnormal alarm response to the network abnormal energy alarm to obtain edge abnormal response nodes;

[0204] In the embodiments of the present invention, by associating the alarm with the identified network edge nodes, the affected edge nodes are screened out. According to factors such as the severity, type of the alarm, and the importance of the edge nodes themselves, the risk levels of each affected edge node are evaluated, such as high - risk, medium - risk, low - risk. The information of all affected edge nodes, including node ID, risk level, associated alarm information, etc., is sorted into a list and output as the edge abnormal response nodes.

[0205] Step S44: Based on the edge abnormal response nodes, perform abnormal security sequence extraction and scheduling on the regional security sequence to obtain an abnormal scheduling security sequence;

[0206] In the embodiments of the present invention, by obtaining the pre - deployed security resource information in the target area, such as the location, status, function, etc. of surveillance cameras, sensors, alarms, etc., a regional security sequence is formed. According to the geographical location information, the edge abnormal response nodes are matched with the security resources in the regional security sequence to find the available security resources near each edge node. According to factors such as the risk level of the edge node, the type and availability of the security resources, corresponding security scheduling strategies are formulated, and the security resources selected according to the scheduling strategies are arranged in a predetermined execution order to form an abnormal scheduling security sequence.

[0207] Step S45: Generate an abnormal scheduling security strategy for the abnormal scheduling security sequence to obtain an abnormal scheduling security strategy.

[0208] In the embodiments of the present invention, by analyzing the characteristics of each security resource in the abnormal scheduling security sequence, such as the type, function parameters, control interface, etc., the executable operation instruction set is determined. For example, the operations that a surveillance camera can execute include: picture zooming, angle adjustment, infrared night vision switching, real-time recording, etc.; according to the security requirements and actual situation, the system formulates a detailed policy execution process for each security resource, clearly stipulating what operations to execute under what conditions, the specific parameters of the operations, the execution order and time interval, etc. For example, for a certain high-risk node, the nearby surveillance cameras immediately start high-definition real-time monitoring after receiving the instruction and transmit the pictures to the monitoring center in real time; integrating the policy execution processes of each security resource to form the final abnormal scheduling security policy.

[0209] In the present invention, by analyzing the network edge nodes of the regional energy topology network, the network edge nodes are obtained, and the nodes that directly interact with users in the energy system can be determined. These nodes are usually more vulnerable to external factors, so their security and stability need to be focused on, providing target node information for subsequent abnormal alarm responses; presetting abnormal energy alarms for the regional energy topology network to obtain network abnormal energy alarms, which can predict potential energy security risks in advance and formulate corresponding countermeasures to improve the security and reliability of the energy system, providing a judgment basis for subsequent abnormal alarm responses; based on the network edge nodes, performing abnormal alarm responses to the network abnormal energy alarms to obtain edge abnormal response nodes, which can timely detect abnormal situations in the energy system and accurately locate the abnormal nodes, providing target node information for subsequent security sequence scheduling so as to quickly take countermeasures; based on the edge abnormal response nodes, extracting and scheduling the abnormal security sequence of the regional security sequence to obtain the abnormal scheduling security sequence, which can dynamically adjust the security patrol plan according to the actual situation, prioritize the nodes with abnormalities, improve the security response speed and efficiency, and timely control security risks; generating an abnormal scheduling security policy for the abnormal scheduling security sequence to obtain the abnormal scheduling security policy, which can formulate targeted security measures according to the specific situation, effectively respond to various energy security risks, ensure the safe and stable operation of the energy system, and minimize losses to the greatest extent.

[0210] Preferably, step S41 includes the following steps:

[0211] Step S411: Calculate the node centrality of the regional energy topology network to obtain the node centrality;

[0212] In the embodiments of the present invention, by selecting a suitable node centrality algorithm according to actual requirements and network characteristics, such as degree centrality, betweenness centrality, closeness centrality, eigenvector centrality, etc. Different centrality algorithms focus on different network structure features. For example, degree centrality focuses on the number of connections of nodes, and betweenness centrality focuses on the number of times a node appears on the shortest paths in the network. Using the selected centrality algorithm, calculate the centrality value of each node in the network.

[0213] Step S412: Analyze the data flow direction of the regional energy topology network to obtain a data flow direction vector;

[0214] In the embodiments of the present invention, by analyzing the transmission path of data from the source node to the aggregation node based on the network topology structure and data transmission protocol, a network simulation tool or algorithm can be used to simulate the data transmission process, record the amount of data received and sent by each node, and calculate the data flow direction vector of each node according to the data transmission path and the amount of data. The data flow direction vector is a vector containing direction and magnitude, indicating the flow and direction of data passing through this node. For example, if a node mainly receives data from upstream nodes and transmits it to downstream nodes, its data flow direction vector points to the downstream direction, and the magnitude of the vector is proportional to the data flow.

[0215] Step S413: Evaluate the importance of nodes in the regional energy topology network based on node centrality and the data flow direction vector to obtain node importance;

[0216] In the embodiments of the present invention, by determining the weight coefficients of node centrality and the data flow direction vector in the node importance evaluation according to actual requirements, and according to the preset weight coefficients, perform weighted summation on the standardized node centrality and the data flow direction vector to obtain the comprehensive importance score of each node.

[0217] Step S414: Identify edge nodes in the regional energy topology network based on node importance to obtain preliminary identification data of edge nodes;

[0218] In the embodiments of the present invention, by sorting all the nodes in the network in descending order according to the node importance score, the lower the importance score, the lower the ranking. According to actual requirements and network scale, determine a preliminary identification ratio, such as 20% or 30%. This ratio indicates what proportion of the nodes with lower rankings will be selected as potential edge nodes. According to the set preliminary identification ratio, select the corresponding proportion of nodes with lower rankings and mark them as potential edge nodes. For example, if there are 100 nodes in the network and the preliminary identification ratio is set to 20%, then 20 nodes with lower rankings will be selected as potential edge nodes.

[0219] Step S415: Analyze the node computing requirements of the regional energy topology network to obtain node computing requirement data;

[0220] In the embodiments of the present invention, appropriate metrics are selected to quantify the computing requirements of nodes, such as CPU occupancy rate, memory occupancy rate, network bandwidth requirements, storage space requirements, etc. According to the node functions and historical operation data, the amount of computing resources required for each node to complete its preset functions is evaluated. Methods such as simulation and performance testing can be used to obtain relevant data. For example, for a data acquisition node, the amount of data that needs to be acquired per second, the CPU resources required for data preprocessing, the storage space required for data storage, etc. can be evaluated.

[0221] Step S416: Locate the edge nodes based on the edge node preliminary identification data according to the node computing requirement data to obtain network edge nodes.

[0222] In the embodiments of the present invention, by analyzing the node computing requirement data, the overall distribution of computing resource requirements in the network is understood. For example, the average values of different computing resource metrics and the computing requirement differences of different types of nodes can be statistically analyzed. According to the distribution of computing resource requirements, a reasonable computing requirement threshold is set. Nodes with computing requirements lower than this threshold are generally considered to have relatively simple functions and low computing resource requirements, and are located at the network edge. Combining the edge node preliminary identification data, potential edge nodes with computing requirements lower than the threshold are confirmed and finally determined as network edge nodes.

[0223] By calculating the node centrality of the regional energy topological network, the present invention can quantify the importance and influence of nodes in the network. For example, nodes with high degree centrality are usually connected to more other nodes, and nodes with high betweenness centrality are usually located on the critical paths connecting different network regions, providing basic data for subsequent node importance evaluation; by analyzing the data flow direction of the regional energy topological network, the flow law of energy data in the network can be grasped, providing supplementary information for subsequent node importance evaluation. For example, nodes with large data flow usually undertake more important data transmission tasks; by evaluating the node importance of the regional energy topological network based on node centrality and data flow vector, the importance of nodes in the network can be evaluated more comprehensively, and their security and stability need to be focused on, providing a basis for subsequent edge node identification; by identifying edge nodes of the regional energy topological network based on node importance, nodes that are not very important in the network can be screened out. These nodes are usually located at the network edge and are connected to end users or less important devices, providing a candidate node set for subsequent edge node positioning; by analyzing the node computing requirements of the regional energy topological network, the computing resource requirements of each node can be understood, providing supplementary information for subsequent edge node positioning; by positioning edge nodes based on node computing requirement data for the preliminary edge node identification data, edge nodes in the network can be identified more accurately. These nodes usually have weak computing power and are connected to end users or less important devices and need to perform lightweight computing and data processing, providing target node information for subsequent edge computing deployment.

[0224] Preferably, the present invention also provides an IOC intelligent operation system for executing the IOC intelligent operation method as described above. The IOC intelligent operation system includes:

[0225] A regional three-dimensional model construction module, configured to obtain a modeling area by acquiring a modeling area of the park; perform multi-angle photography on the modeling area to obtain regional multi-angle image data; acquire regional point cloud data for the modeling area to obtain regional point cloud data; perform regional three-dimensional modeling on the regional point cloud data and the regional multi-angle image data to obtain a regional three-dimensional model;

[0226] A regional energy topological network construction module, configured to monitor real-time multi-energy data for the modeling area to obtain real-time multi-energy data; construct a regional energy topological network for the regional three-dimensional model based on the real-time multi-energy data to obtain a regional energy topological network;

[0227] A regional security sequence generation module, configured to set security nodes for the regional energy topological network to obtain regional security nodes; acquire regional passage path data for the regional three-dimensional model to obtain regional passage path data; generate a regional security sequence for the regional security nodes based on the regional passage path data to obtain a regional security sequence;

[0228] An abnormal scheduling security strategy module is used to analyze network edge nodes of a regional energy topology network to obtain network edge nodes; preset abnormal energy alarms for the regional energy topology network to obtain network abnormal energy alarms; generate an abnormal scheduling security strategy for a regional security sequence based on the network edge nodes and the network abnormal energy alarms to obtain an abnormal scheduling security strategy.

[0229] In summary, the present invention provides an IOC intelligent operation system and method. The IOC intelligent operation system is composed of a regional three-dimensional model construction module, a regional energy topology network construction module, a regional security sequence generation module, and an abnormal scheduling security strategy module, and can implement any IOC intelligent operation method described in the present invention. It is used to realize any IOC intelligent operation method through the operations between computer programs running on each module. The internal structure of the system cooperates with each other, which can greatly reduce repetitive work and manpower input, and can quickly and effectively provide a more accurate and efficient IOC intelligent operation process, thereby simplifying the operation process of the IOC intelligent operation system.

[0230] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. An IOC intelligent operation method, characterized in that: The following steps are involved: Step S1: Acquire the modeling area of ​​the park to obtain the modeling area; Take multi-angle photos of the modeling area to obtain multi-angle image data of the area; Acquiring regional point cloud data of the modeling area to obtain regional point cloud data; performing regional three-dimensional modeling on the regional point cloud data and regional multi-angle image data to obtain a regional three-dimensional model; Step S2: monitoring the modeling area in real time for multiple energy data to obtain real-time multiple energy data; constructing a regional energy topology network for the regional three-dimensional model based on the real-time multiple energy data to obtain a regional energy topology network; Step S3: Setting security nodes for the regional energy topology network to obtain regional security nodes; Acquire the regional traffic path of the regional three-dimensional model to obtain the regional traffic path data; Based on the regional traffic path data, a regional security sequence is generated for the regional security node to obtain a regional security sequence; wherein step S3 includes the following steps: Step S31: Setting security nodes for the regional energy topology network to obtain regional security nodes; Step S32: Allocate security resources to the regional security nodes to obtain security resource allocation data; Step S33: performing adjacent security node analysis on the regional three-dimensional model based on the regional security node to obtain adjacent security nodes; Step S34: acquiring the regional traffic path of the regional three-dimensional model to obtain regional traffic path data; Step S35: Perform security passage path analysis on adjacent security nodes based on the regional passage path data to obtain node security passage path data; Step S36: Generate a regional security sequence for the regional security node based on the node security passage path data and the security resource allocation data to obtain a regional security sequence; wherein step S36 includes the following steps: Step S361: Prioritize security nodes for regional security nodes based on security resource allocation data to obtain priority security node data; Step S362: acquiring node patrol frequency of regional security nodes based on security resource allocation data to obtain node patrol frequency data; Step S363: setting node security intervals for regional security nodes to obtain node security interval data; Step S364: generating a node security path sequence for the node security passage path data based on the node patrol frequency data and the node security interval data to obtain a node security path sequence; Step S365: generating a regional security sequence for the priority security node data based on the node security path sequence to obtain a regional security sequence; Step S4: Analyze the network edge nodes of the regional energy topology network to obtain the network edge nodes; preset abnormal energy alarms for the regional energy topology network to obtain network abnormal energy alarms; generate abnormal scheduling security strategies for the regional security sequence based on the network edge nodes and the network abnormal energy alarms to obtain abnormal scheduling security strategies.

2. The IOC intelligent operation method according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Acquire the modeling area of ​​the park to obtain the modeling area; Step S12: photograph the modeling area from multiple angles to obtain multi-angle image data of the area; Step S13: acquiring regional point cloud data for the modeling area to obtain regional point cloud data; Step S14: identifying and classifying the regional point cloud data to obtain regional classified point cloud data; Step S15: classify the regional classified point cloud data to identify the building outline, and obtain the regional building outline data; Step S16: densely matching the regional multi-angle image data based on the regional point cloud data to obtain regional dense point cloud data; Step S17: Performing regional three-dimensional modeling on the regional dense point cloud data based on the regional building outline data to obtain a regional three-dimensional model.

3. The IOC intelligent operation method according to claim 2, characterized in that: Step S15 includes the following steps: Step S151: acquiring building point cloud data from the regional classification point cloud data to obtain building point cloud data; Step S152: performing two-dimensional projection on the building point cloud data to obtain two-dimensional building point cloud data; Step S153: constructing a triangulated mesh surface on the building two-dimensional point cloud data to obtain a building triangulated mesh surface; Step S154: Calculate the normal vector of the triangle of the building triangular mesh to obtain the normal vector data of the triangle; Step S155: traversing the boundary triangles of the building triangulated mesh surface based on the triangle normal vector data to obtain boundary triangle data; Step S156: Connect the boundary triangle data to the building outline boundary to obtain the regional building outline line segment; Step S157: Perform data aggregation on the regional building outline segments to obtain regional building outline data.

4. The IOC intelligent operation method according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: monitoring the modeling area in real time for multiple energy data to obtain real-time multiple energy data; Step S22: performing data mapping on the regional three-dimensional model based on the real-time multi-energy data to obtain a regional multi-energy three-dimensional model; Step S23: setting regional energy nodes for the regional multi-energy three-dimensional model to obtain regional energy nodes; Step S24: configuring node data of the regional energy node to obtain a configured regional energy node; Step S25: constructing a regional energy topology network for the configured regional energy nodes based on the regional three-dimensional model to obtain a regional energy topology network.

5. The IOC intelligent operation method according to claim 4, characterized in that: Step S25 includes the following steps: Step S251: acquiring node range data for the energy nodes in the configured area to obtain node range data; Step S252: performing node range mapping on the regional three-dimensional model based on the node range data to obtain three-dimensional node range data; Step S253: performing node spatial relationship analysis on the three-dimensional node range data to obtain node spatial relationship data; Step S254: acquiring adjacent regional nodes for the configured regional energy nodes based on the node spatial relationship data to obtain adjacent regional node data; Step S255: performing adjacent connection path planning on the regional three-dimensional model based on the adjacent regional node data to obtain adjacent node path data; Step S256: Connecting the configured regional energy nodes to a regional energy topology network based on the adjacent node path data to obtain a regional energy topology network.

6. The IOC intelligent operation method according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: Analyze the network edge nodes of the regional energy topology network to obtain the network edge nodes; Step S42: Preset abnormal energy alarm for the regional energy topology network to obtain network abnormal energy alarm; Step S43: responding to the abnormal energy alarm of the network based on the network edge node to obtain an edge abnormal response node; Step S44: performing abnormal security sequence extraction and scheduling on the regional security sequence based on the edge abnormal response node to obtain an abnormal scheduling security sequence; Step S45: generating an abnormal scheduling security strategy for the abnormal scheduling security sequence to obtain an abnormal scheduling security strategy.

7. The IOC intelligent operation method according to claim 6, characterized in that: Step S41 includes the following steps: Step S411: Calculate the node centrality of the regional energy topology network to obtain the node centrality; Step S412: Analyze the data flow of the regional energy topology network to obtain a data flow vector; Step S413: evaluating the importance of nodes in the regional energy topology network based on the node centrality and the data flow vector to obtain the importance of nodes; Step S414: Identify edge nodes of the regional energy topology network based on node importance to obtain preliminary edge node identification data; Step S415: Analyze the node computing demand of the regional energy topology network to obtain node computing demand data; Step S416: performing edge node positioning on the edge node preliminary identification data based on the node calculation demand data to obtain the network edge node.

8. An IOC intelligent operation system, characterized in that: Used to execute the IOC intelligent operation method according to claim 1, the IOC intelligent operation system comprises: The regional three-dimensional model building module is used to acquire the modeling area of ​​the park to obtain the modeling area; perform multi-angle photography on the modeling area to obtain regional multi-angle image data; acquire regional point cloud data of the modeling area to obtain regional point cloud data; perform regional three-dimensional modeling on the regional point cloud data and the regional multi-angle image data to obtain a regional three-dimensional model; The regional energy topology network construction module is used to monitor the real-time multi-energy data of the modeling area and obtain the real-time multi-energy data; construct the regional energy topology network of the regional three-dimensional model based on the real-time multi-energy data to obtain the regional energy topology network; The regional security sequence generation module is used to set security nodes for the regional energy topology network to obtain regional security nodes; obtain regional traffic paths for the regional three-dimensional model to obtain regional traffic path data; generate regional security sequences for regional security nodes based on the regional traffic path data to obtain regional security sequences; wherein the regional security sequence generation module includes: Setting security nodes for the regional energy topology network to obtain regional security nodes; Allocate security resources to regional security nodes and obtain security resource allocation data; Based on the regional security nodes, the adjacent security nodes of the regional three-dimensional model are analyzed to obtain the adjacent security nodes; Acquire the regional traffic path of the regional three-dimensional model to obtain the regional traffic path data; Based on the regional traffic path data, the security traffic path of adjacent security nodes is analyzed to obtain the node security traffic path data; Generating a regional security sequence for a regional security node based on the node security passage path data and the security resource allocation data to obtain a regional security sequence; specifically, generating a regional security sequence for a regional security node based on the node security passage path data and the security resource allocation data includes: Based on the security resource allocation data, priority security node assessment is performed on the regional security nodes to obtain priority security node data; Based on the security resource allocation data, the node patrol frequency of the regional security node is acquired to obtain the node patrol frequency data; Set node security intervals for regional security nodes and obtain node security interval data; Based on the node patrol frequency data and the node security interval data, the node security path sequence is generated for the node security passage path data to obtain the node security path sequence; Generate a regional security sequence for the priority security node data based on the node security path sequence to obtain a regional security sequence; The abnormal scheduling security strategy module is used to analyze the network edge nodes of the regional energy topology network to obtain the network edge nodes; preset abnormal energy alarms for the regional energy topology network to obtain network abnormal energy alarms; generate abnormal scheduling security strategies for the regional security sequence based on the network edge nodes and network abnormal energy alarms to obtain abnormal scheduling security strategies.

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