IOC intelligent operation system and method

Abstract

The invention relates to the field of data systems, in particular to an IOC intelligent operation system and method. The method comprises the following steps: carrying out modeling area acquisition on a park to obtain a modeling area; performing multi-angle photographing and point cloud data acquisition on the modeling area, constructing a regional three-dimensional model, performing real-time multi-energy data monitoring on the modeling area, constructing a regional energy topology network based on the real-time multi-energy data and the regional three-dimensional model, and setting security nodes on the energy topology network, and the regional three-dimensional model analysis is combined to obtain regional passage path data, a regional security sequence is generated, network edge node analysis is performed on the regional energy topology network, and abnormal scheduling security strategy generation is performed on the obtained network edge node and a preset abnormal energy alarm to obtain an abnormal scheduling security strategy. According to the invention, by effectively fusing the park three-dimensional model and the energy topology network, intelligent security early warning and scheduling based on energy abnormity are realized.

Description

Technical Field

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

[0002] With the development of society and the advancement of science and technology, modern parks are becoming increasingly complex in terms of scale, personnel, assets, etc., and higher requirements are placed on security. Traditional security systems mainly rely on fixed camera monitoring and manual patrols, which are difficult to meet the growing security needs. Specifically, traditional fixed cameras have monitoring blind spots and are difficult to fully cover all key areas; manual autonomous patrols are inefficient and difficult to meet the security patrol needs of large-scale parks; the IOC intelligent operation system accesses various devices and sensors through the Internet of Things technology, collects massive data in real time, realizes the perception of the operating environment, and 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, and reduces operating costs, making up for the shortcomings of traditional operation methods. However, there are still problems such as each subsystem operating independently, it is difficult to form a linkage security mechanism, and the response speed is slow. 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 purpose, an IOC intelligent operation method includes the following steps:

[0005] Step S1: acquiring a modeling area of ​​the park to obtain a modeling area; performing multi-angle photography on the modeling area to obtain regional multi-angle image data; 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 the regional multi-angle image data to obtain a regional three-dimensional model;

[0006] 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;

[0007] Step S3: Setting security nodes for the regional energy topology network to obtain regional security nodes; acquiring regional traffic paths for the regional three-dimensional model to obtain regional traffic path data; generating regional security sequences for the regional security nodes based on the regional traffic path data to obtain regional security sequences;

[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 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.

[0009] The present invention constructs a fine three-dimensional model of the park through multi-angle photography and point cloud data collection, laying a solid foundation for subsequent intelligent applications. The model not only truly restores the spatial information of the park's buildings, roads, vegetation, etc., but can also be integrated with other system data to achieve visual management, providing intuitive reference for security patrol route planning, emergency event handling, etc.; real-time collection of multi-energy data such as electricity, water, gas, etc. in the park, and combined with the three-dimensional model to build a regional energy topology network, so that the flow and consumption of park energy are clear at a glance. The network visualizes the production, transmission, and use of energy, helping managers to grasp the use of energy in real time, timely discover abnormal energy consumption, and provide data support for energy optimization and energy saving and consumption reduction; comprehensively consider the energy topology network structure and the regional three-dimensional model, scientifically set security nodes, and combine the regional traffic path data to generate a reasonable security patrol route. This method avoids the blindness of the traditional security system node setting, improves the utilization rate of security resources, realizes accurate deployment, and effectively improves the security prevention capability of the park; by analyzing the edge nodes of the energy topology network, presets the abnormal energy alarm threshold, and formulates a scheduling security strategy for abnormal situations of network edge nodes. This strategy can promptly detect and respond to potential security risks. For example, it can strengthen monitoring of key areas by adjusting security patrol routes, increasing patrol frequency, etc., realize dynamic scheduling of security resources, and further improve the security level of the park.

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

[0011] Step S11: Acquire the modeling area of ​​the park to obtain the modeling area;

[0012] Step S12: photograph the modeling area from multiple angles to obtain multi-angle image data of the area;

[0013] Step S13: acquiring regional point cloud data for the modeling area to obtain regional point cloud data;

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

[0015] Step S15: classify the regional classified point cloud data to identify the building outline, and obtain the regional building outline data;

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

[0017] 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.

[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 use of resources, avoids blindness and duplication of work; shoots the modeling area from multiple perspectives, obtains rich image information, and provides a sufficient data basis for subsequent three-dimensional reconstruction. Multi-angle shooting can effectively make up for the shortcomings 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 laser radar and other equipment. Point cloud data can accurately express the three-dimensional spatial information of the target, providing a reliable data basis for subsequent identification, classification and three-dimensional modeling; 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 three-dimensional modeling, making model construction more accurate and efficient; based on the classified building point cloud data, the outline information of the building is identified, providing accurate building boundary constraints for subsequent three-dimensional modeling, so that the generated model is more in line with the actual shape of the building; point cloud data is used to densely match multi-angle images to obtain dense point cloud data containing rich texture information and geometric information, providing data guarantee for generating more realistic and refined three-dimensional models; based on building contour data, dense point cloud data is 3D modeled 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.

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

[0020] Step S151: acquiring building point cloud data from the regional classification point cloud data to obtain building point cloud data;

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

[0022] Step S153: constructing a triangulated mesh surface on the building two-dimensional point cloud data to obtain a building triangulated mesh surface;

[0023] Step S154: Calculate the normal vector of the triangle of the building triangular mesh to obtain the normal vector data of the triangle;

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

[0025] Step S156: Connect the boundary triangle data to the building outline boundary to obtain the regional building outline line segment;

[0026] Step S157: Perform data aggregation on the regional building outline segments to obtain regional building outline data.

[0027] The present invention acquires building point cloud data by performing building point cloud data acquisition on regional classified point cloud data, and can effectively separate buildings from complex urban environments to obtain pure building point cloud data, thereby 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 building point cloud data and simplifying 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, and providing a convenient data form for subsequent triangulation surface construction and contour extraction; converting discrete point cloud data into a continuous mesh surface can more completely express the geometric form of the building, and provide a basic topological structure for subsequent normal vector calculation and boundary extraction, making subsequent processing more Efficient and accurate; it can obtain the normal vector information of each triangular face, reflecting the directional characteristics of the building surface, and providing an important basis for subsequent boundary triangle identification and contour extraction; it can effectively identify the triangles that constitute the boundary of the building outline by traversing the boundary triangles of the building triangulated surface based on the triangle normal vector data, and provide accurate boundary information for the subsequent contour line segment connection, avoiding the interference of non-boundary triangles and improving the accuracy of contour extraction; connecting discrete boundary triangles into continuous segments can more clearly express the outline of the building, and provide intuitive geometric information for the final acquisition of building outline data; it can comprehensively reflect the distribution and morphological characteristics of buildings in the region, and provide important data support for applications such as urban planning, architectural design, and 3D modeling.

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

[0029] Step S21: monitoring the modeling area in real time for multiple energy data to obtain real-time multiple energy data;

[0030] 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;

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

[0032] Step S24: configuring node data of the regional energy node to obtain a configured regional energy node;

[0033] 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.

[0034] The present invention can timely obtain real-time data on the production, transportation, and consumption of multiple energy sources in the region, such as electricity, heat, and natural gas, by monitoring the real-time multi-energy data of the modeling area, so as to provide a reliable data basis for subsequent energy analysis, management, and optimization, and realize comprehensive perception and dynamic grasp of the regional energy system; the present invention performs data mapping on the regional three-dimensional model based on the real-time multi-energy data, combines the abstract energy data with the intuitive geographic spatial information, and constructs a regional multi-energy three-dimensional model, which can present complex energy data in a visual form, enhance the readability and comprehension of the energy data, and facilitate users to intuitively understand the operating status of the regional energy system; the regional multi-energy three-dimensional model performs regional energy node setting, abstracts key facilities such as energy production, conversion, storage, and consumption into nodes, and constructs regional energy nodes The network can effectively simplify the complexity of the regional energy system and provide a basic framework for the subsequent construction and analysis of the energy topology network; configure the node data of the regional energy nodes, set the corresponding attribute information for each node, such as energy type, capacity, efficiency, etc., and construct the configured regional energy nodes, which can describe the various components of the regional energy system in more detail, provide more refined data support for the subsequent energy flow analysis and optimization, and enhance the pertinence and effectiveness of energy management; construct the regional energy topology network for the configured regional energy nodes based on the regional three-dimensional model, express the connection relationship between each energy node in the form of a network, and construct the regional energy topology 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 comprises the following steps:

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

[0037] 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;

[0038] Step S253: performing node spatial relationship analysis on the three-dimensional node range data to obtain node spatial relationship data;

[0039] 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;

[0040] 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;

[0041] 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.

[0042] The present invention acquires node range data of energy nodes in the configured area, and can clarify the influence range or service area of ​​each energy node, such as the power supply range of the substation, the heating range of the thermal power station, etc., to provide necessary spatial information for subsequent node spatial relationship analysis and topological network connection, and ensure that the constructed energy topological network accurately reflects the actual situation; based on the node range data, the regional three-dimensional model is mapped to the node range, and the abstract node range data is visually presented 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 the subsequent spatial relationship analysis; the three-dimensional node range data is analyzed for node spatial relationship, such as judging whether there is overlap, adjacency, inclusion and other relationships between nodes, and obtaining node spatial relationship data, which can effectively Identify the spatial correlation between nodes to provide a basis for subsequent adjacent node acquisition and path planning; obtain adjacent area node data to accurately screen out the nodes that need to be connected, provide target node information for subsequent path planning, avoid unnecessary connections, and improve the efficiency of topological network construction; obtain adjacent node path data to simulate the actual energy transmission path, provide specific connection information for subsequent topological network connections, and make the constructed topological network more realistic and practical; based on the adjacent node path data, configure the regional energy nodes to connect the regional energy topological network, connect the adjacent nodes according to the planned path, and form a complete regional energy topological 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.

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

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

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

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

[0047] Step S34: acquiring the regional traffic path of the regional three-dimensional model to obtain regional traffic path data;

[0048] 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;

[0049] 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 obtain a regional security sequence.

[0050] The present invention can deploy security monitoring equipment to key energy nodes to form regional security nodes by setting security nodes for the regional energy topology network, thereby providing infrastructure support for subsequent security monitoring and safety management, and ensuring the safe and stable operation of the energy system; the present invention can allocate security resources to the regional security nodes, for example, allocate corresponding monitoring ranges, alarm levels, response strategies, etc. to each security node, obtain security resource allocation data, and reasonably configure security resources according to the importance of different nodes and the security risk level, thereby improving the efficiency and pertinence of the security system and effectively reducing security risks; the present invention can obtain adjacent security nodes, establish spatial correlations between security nodes, and provide basic data for subsequent security passage path analysis and regional security sequence generation, Realize the coordinated linkage between security nodes; obtain regional access path data, build the access network within the area, provide basic path information for subsequent security access path analysis, and ensure that security monitoring covers all access paths; perform security access path analysis on adjacent security nodes based on regional access path data, such as analyzing whether there is a passable path between adjacent security nodes, as well as the length and safety of the path, and obtain node security access path data, which can evaluate the accessibility and access efficiency between adjacent security nodes; for example, based on the importance of security nodes, security risk levels, access paths and other information, generate the order of security patrols or monitoring, obtain the regional security sequence, and formulate reasonable security patrol or monitoring plans to improve the efficiency and coverage of security work.

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

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

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

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

[0055] 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;

[0056] 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.

[0057] The present invention can identify nodes that need special attention and priority patrols by evaluating regional security nodes based on security resource allocation data, and provide a sorting basis for subsequent regional security sequence generation, ensuring that limited security resources can give priority to protecting the safety of key nodes; the node patrol frequency of regional security nodes is acquired based on security resource allocation data, and differentiated patrol strategies can be formulated according to the security requirements of different nodes, thereby improving the pertinence and effectiveness of security work and avoiding waste of resources; the node security interval is set for the regional security nodes, and the node security interval data is obtained, so that the patrol time can be reasonably arranged to avoid excessive patrols. Frequent or long intervals ensure that each node can be monitored in a timely and effective manner, while taking into account patrol efficiency; 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 data to ensure that security personnel can patrol each node in a predetermined order and frequency, thereby improving patrol efficiency and coverage; based on the node security path sequence, the regional security sequence is generated for the priority security node data to obtain the regional security sequence, which can formulate a globally optimized security patrol plan, ensure priority patrol of key nodes, and reasonably arrange patrol routes and times to maximize the security of the regional energy system.

[0058] Preferably, step S4 comprises 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 alarm for the regional energy topology network to obtain network abnormal energy alarm;

[0061] Step S43: responding to the abnormal energy alarm of the network based on the network edge node to obtain an edge abnormal response node;

[0062] 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;

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

[0064] The present invention obtains network edge nodes by analyzing the network edge nodes of the regional energy topology network, and can determine the nodes in the energy system that directly interact with users. These nodes are usually more susceptible to external factors, so it is necessary to focus on their security and stability, and provide target node information for subsequent abnormal alarm responses; abnormal energy alarms are preset for the regional energy topology network to obtain network abnormal energy alarms, which can predict potential energy security risks in advance and formulate corresponding response measures to improve the security and reliability of the energy system, and provide a judgment basis for subsequent abnormal alarm responses; abnormal alarm responses to network abnormal energy alarms based on network edge nodes are obtained to obtain edge abnormal response nodes, which can Timely discover abnormal situations in the energy system and accurately locate abnormal nodes, provide target node information for subsequent security sequence scheduling, so as to quickly take countermeasures; extract and schedule the regional security sequence based on the edge abnormal response node to obtain the abnormal scheduling security sequence, which can dynamically adjust the security patrol plan according to the actual situation, give priority to the nodes with abnormalities, improve the security response speed and efficiency, and control security risks in time; generate abnormal scheduling security strategies for the abnormal scheduling security sequence, and obtain the abnormal scheduling security strategy, 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.

[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 of the regional energy topology network to obtain a data flow vector;

[0068] 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;

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

[0070] Step S415: Analyze the node computing demand of the regional energy topology network to obtain node computing demand data;

[0071] 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.

[0072] The present invention can quantify the importance and influence of nodes in the network by calculating the node centrality of the regional energy topology 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 key path connecting different network areas, providing basic data for subsequent node importance evaluation; performing data flow analysis on the regional energy topology network can grasp the flow law of energy data in the network and provide supplementary information for subsequent node importance evaluation. For example, nodes with large data flow usually undertake more important data transmission tasks; performing node importance evaluation on the regional energy topology network based on node centrality and data flow vector can more comprehensively evaluate the importance of nodes in the network, and focus on their security and stability, which is necessary for subsequent Provide a basis for edge node identification; identify edge nodes in the regional energy topology network based on node importance, and filter out less important nodes in the network. These nodes are usually located at the edge of the network, connected to end users or less important devices, and provide a set of candidate nodes for subsequent edge node positioning; perform node computing demand analysis on the regional energy topology network to understand the computing resource requirements of each node and provide supplementary information for subsequent edge node positioning; locate edge nodes based on preliminary identification data of edge nodes based on node computing demand data, and more accurately identify edge nodes in the network. These nodes usually have weak computing power, are connected to end users or less important devices, and require 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 comprising:

[0074] 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;

[0075] 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;

[0076] 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;

[0077] 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.

[0078] In summary, the present invention provides an IOC intelligent operation system and method. The IOC intelligent operation system consists 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. It can realize any IOC intelligent operation method described in the present invention, and is used to combine the operations between computer programs running on various modules to realize any IOC intelligent operation method. The internal structures of the system cooperate with each other, which can greatly reduce duplication of work and manpower investment, 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, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0080] Figure 1 A schematic diagram of the steps of an IOC intelligent operation method of the present invention;

[0081] Figure 2 for Figure 1 Detailed step flow diagram of step S3;

[0082] Figure 3 for Figure 2 Detailed step flow diagram of step S36; DETAILED DESCRIPTION

[0083] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0084] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying 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 the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. 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 this, please refer to Figures 1 to 3 The present invention provides an IOC intelligent operation method, comprising the following steps:

[0087] Step S1: acquiring a modeling area of ​​the park to obtain a modeling area; performing multi-angle photography on the modeling area to obtain regional multi-angle image data; 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 the regional multi-angle image data to obtain a regional three-dimensional model;

[0088] 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;

[0089] Step S3: Setting security nodes for the regional energy topology network to obtain regional security nodes; acquiring regional traffic paths for the regional three-dimensional model to obtain regional traffic path data; generating regional security sequences for the regional security nodes based on the regional traffic path data to obtain regional security sequences;

[0090] 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.

[0091] In the embodiment of the present invention, please refer to Figure 1FIG. 1 is a schematic diagram of the steps 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: acquiring a modeling area of ​​the park to obtain a modeling area; performing multi-angle photography on the modeling area to obtain regional multi-angle image data; 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 the regional multi-angle image data to obtain a regional three-dimensional model;

[0093] In an embodiment of the present invention, the scope of the modeling area is determined by field survey or reference map, and the boundary information is recorded. Then, a drone or camera is used for multi-angle photography, and a laser radar or photogrammetry software is used to obtain regional point cloud data. Next, the point cloud data is segmented and classified using point cloud processing software to identify the building outline. On this basis, multi-angle images are densely matched based on the regional point cloud data to generate high-density point cloud data. Finally, the high-density point cloud is meshed using the regional building outline data to generate the final three-dimensional model of the park.

[0094] 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;

[0095] In an embodiment of the present invention, the energy type to be monitored is determined, and sensors are deployed in 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, the regional energy nodes are set in the three-dimensional model, and data is configured 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 between energy nodes and the energy flow.

[0096] Step S3: Setting security nodes for the regional energy topology network to obtain regional security nodes; acquiring regional traffic paths for the regional three-dimensional model to obtain regional traffic path data; generating regional security sequences for the regional security nodes based on the regional traffic path data to obtain regional security sequences;

[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, the 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 security nodes around it is analyzed, the adjacent security nodes of each security node are determined, and the passage path data in the area is extracted. After that, 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. Finally, according to the importance of the security node, the risk level and the configuration of the security resources, a regional security sequence is generated to guide the order of security inspections or monitoring.

[0098] 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.

[0099] In an embodiment of the present invention, by using a graph theory algorithm to calculate the connectivity of each node in the regional energy topology network, network edge nodes with connectivity below a preset threshold are identified. Then, based on actual needs and historical data analysis, abnormal energy alarm rules are preset, and alarm information is generated when abnormal data is monitored. 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 strategy execution process, a detailed abnormal scheduling security strategy is generated.

[0100] The present invention constructs a fine three-dimensional model of the park through multi-angle photography and point cloud data collection, laying a solid foundation for subsequent intelligent applications. The model not only truly restores the spatial information of the park's buildings, roads, vegetation, etc., but can also be integrated with other system data to achieve visual management, providing intuitive reference for security patrol route planning, emergency event handling, etc.; real-time collection of multi-energy data such as electricity, water, gas, etc. in the park, and combined with the three-dimensional model to build a regional energy topology network, so that the flow and consumption of park energy are clear at a glance. The network visualizes the production, transmission, and use of energy, helping managers to grasp the use of energy in real time, timely discover abnormal energy consumption, and provide data support for energy optimization and energy saving and consumption reduction; comprehensively consider the energy topology network structure and the regional three-dimensional model, scientifically set security nodes, and combine the regional traffic path data to generate a reasonable security patrol route. This method avoids the blindness of the traditional security system node setting, improves the utilization rate of security resources, realizes accurate deployment, and effectively improves the security prevention capability of the park; by analyzing the edge nodes of the energy topology network, presets the abnormal energy alarm threshold, and formulates a scheduling security strategy for abnormal situations of network edge nodes. This strategy can promptly detect and respond to potential security risks. For example, it can strengthen monitoring of key areas by adjusting security patrol routes, increasing patrol frequency, etc., realize dynamic scheduling of security resources, and further improve the security level of the park.

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

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

[0103] In an embodiment of the present invention, by acquiring the modeling area of ​​the park, for example, through field surveys or reference to the park map, the scope of the area where three-dimensional modeling is required is determined, and a GPS device can be used to record the coordinates of the area boundary points, or map software can be used to draw the area boundary lines, and the boundary information can be saved in a GIS data format.

[0104] Step S12: photograph the modeling area from multiple angles to obtain multi-angle image data of the area;

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

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

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

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

[0109] In an embodiment of the present invention, point cloud data is segmented and classified by using point cloud processing software, and the point cloud data is divided into different categories, such as buildings, vegetation, ground, etc., and a rule-based classification method can be used, such as classification based on the height, density, color and other features of the point cloud. A machine learning-based classification method can also be used, such as 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: classify the regional classified point cloud data to identify the building outline, and obtain the regional building outline data;

[0111] In an embodiment of the present invention, by classifying the regional classified point cloud data to identify the building contour, extracting the building point cloud data, and identifying the building contour line, a plane fitting-based method can be used to fit the building wall and extract the wall boundary line, or an edge detection-based method, such as the Canny operator, can be used to detect the edge of the building point cloud and connect the edge points to form a contour line.

[0112] Step S16: densely matching 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, regional multi-angle image data is densely matched based on regional point cloud data. For example, photogrammetry software is used to use regional point cloud data as a reference to perform dense matching on multi-angle image data to generate high-density point cloud data. Feature point-based matching methods, such as SIFT algorithm and SURF algorithm, can be used to extract image feature points and perform matching. Pixel-based matching methods, such as semi-global matching and stereo matching algorithms, can also be used to match image pixels.

[0114] 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.

[0115] In an embodiment of the present invention, regional three-dimensional modeling is performed on regional dense point cloud data based on regional building contour data. For example, three-dimensional modeling software is used to mesh the regional dense point cloud data using the regional building contour data as constraints to generate a three-dimensional model of the region. A mesh reconstruction method based on triangulation, such as a Poisson reconstruction algorithm, can be used to generate a triangular mesh model. A mesh reconstruction method based on surface fitting, such as a B-spline surface fitting algorithm, can also be used to generate a smooth surface model.

[0116] The present invention clarifies the scope of park modeling, provides a clear target area for subsequent data collection and modeling work, ensures the effective use of resources, avoids blindness and duplication of work; shoots the modeling area from multiple perspectives, obtains rich image information, and provides a sufficient data basis for subsequent three-dimensional reconstruction. Multi-angle shooting can effectively make up for the shortcomings 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 laser radar and other equipment. Point cloud data can accurately express the three-dimensional spatial information of the target, providing a reliable data basis for subsequent identification, classification and three-dimensional modeling; 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 three-dimensional modeling, making model construction more accurate and efficient; based on the classified building point cloud data, the outline information of the building is identified, providing accurate building boundary constraints for subsequent three-dimensional modeling, so that the generated model is more in line with the actual shape of the building; point cloud data is used to densely match multi-angle images to obtain dense point cloud data containing rich texture information and geometric information, providing data guarantee for generating more realistic and refined three-dimensional models; based on building contour data, dense point cloud data is 3D modeled 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 comprises the following steps:

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

[0119] In an embodiment of the present invention, point cloud data is classified by clarifying the region, which refers to point cloud data that has been classified based on features such as color, shape, and height, and contains information about different types of objects. Next, it is necessary to extract point cloud data corresponding to the building from the classified point cloud data. This usually requires the use of known building classification labels or preset building feature thresholds. For example, buildings usually have features such as height, density, and plane shape, and these features are used to filter out points belonging to the building. The specific implementation can use a variety of algorithms, such as those based on regional growth, model matching, and machine learning, to filter out points that meet the conditions and store them as new point cloud data.

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

[0121] In the embodiment of the present invention, the obtained building point cloud data is read and a suitable projection plane is selected, usually a horizontal plane is selected as the projection plane. Then, each point in the building point cloud data is traversed, its X and Y coordinates are extracted, and projected onto the projection plane. The projected point cloud data only contains the X and Y coordinates, and the Z coordinate is ignored.

[0122] Step S153: constructing a triangulated mesh surface on the building two-dimensional point cloud data to obtain a building triangulated mesh surface;

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

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

[0125] In an embodiment of the present invention, by reading the building triangulated mesh data, traversing each triangle in the triangulated mesh, and calculating the normal vector of each triangle. The method for calculating the normal vector of a triangle is to take the coordinates of the three vertices of the triangle, calculate the cross product of the two sides, obtain a vector, and then normalize it to obtain the normal vector of the triangle. The direction of the normal vector can be adjusted as needed, for example, it can be uniformly set to point to the outside of the building.

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

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

[0128] Step S156: Connect the boundary triangle data to the building outline boundary to obtain the regional building outline line segment;

[0129] In the embodiment of the present invention, by traversing the boundary triangle data, the edge of each triangle is extracted. If an edge belongs to two boundary triangles at the same time, it is considered to be an internal edge and is removed; if an edge belongs to only one boundary triangle, it is considered to be a boundary edge and is saved in the boundary line segment data set. All boundary line segment data are connected to form a building outline segment.

[0130] Step S157: Perform data aggregation on the regional building outline segments to obtain regional building outline data.

[0131] In the embodiment of the present invention, the regional building outline segment data obtained by traversal is integrated for all building outline segments, and the building outline segments in all regions are combined together to form a complete regional building outline. This includes merging overlapping parts, filling any gaps, and simplifying the outline segments to achieve a smoother representation, thereby forming a complete regional building outline data.

[0132] The present invention acquires building point cloud data by performing building point cloud data acquisition on regional classified point cloud data, and can effectively separate buildings from complex urban environments to obtain pure building point cloud data, thereby 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 building point cloud data and simplifying 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, and providing a convenient data form for subsequent triangulation surface construction and contour extraction; converting discrete point cloud data into a continuous mesh surface can more completely express the geometric form of the building, and provide a basic topological structure for subsequent normal vector calculation and boundary extraction, making subsequent processing more Efficient and accurate; it can obtain the normal vector information of each triangular face, reflecting the directional characteristics of the building surface, and providing an important basis for subsequent boundary triangle identification and contour extraction; it can effectively identify the triangles that constitute the boundary of the building outline by traversing the boundary triangles of the building triangulated surface based on the triangle normal vector data, and provide accurate boundary information for the subsequent contour line segment connection, avoiding the interference of non-boundary triangles and improving the accuracy of contour extraction; connecting discrete boundary triangles into continuous segments can more clearly express the outline of the building, and provide intuitive geometric information for the final acquisition of building outline data; it can comprehensively reflect the distribution and morphological characteristics of buildings in the region, and provide important data support for applications such as urban planning, architectural design, and 3D modeling.

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

[0134] Step S21: monitoring the modeling area in real time for multiple energy data to obtain real-time multiple energy data;

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

[0136] 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;

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

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

[0139] In an embodiment of the present invention, regional energy nodes are set in a three-dimensional model according to the regional energy system structure and energy flow. 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 cogeneration 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 setting corresponding attributes for each node, such as node type, node name, node coordinates, etc.

[0140] Step S24: configuring node data of the regional energy node to obtain a configured regional energy node;

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

[0142] 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.

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

[0144] The present invention can timely obtain real-time data on the production, transportation, and consumption of multiple energy sources in the region, such as electricity, heat, and natural gas, by monitoring the real-time multi-energy data of the modeling area, so as to provide a reliable data basis for subsequent energy analysis, management, and optimization, and realize comprehensive perception and dynamic grasp of the regional energy system; the present invention performs data mapping on the regional three-dimensional model based on the real-time multi-energy data, combines the abstract energy data with the intuitive geographic spatial information, and constructs a regional multi-energy three-dimensional model, which can present complex energy data in a visual form, enhance the readability and comprehension of the energy data, and facilitate users to intuitively understand the operating status of the regional energy system; the regional multi-energy three-dimensional model performs regional energy node setting, abstracts key facilities such as energy production, conversion, storage, and consumption into nodes, and constructs regional energy nodes The network can effectively simplify the complexity of the regional energy system and provide a basic framework for the subsequent construction and analysis of the energy topology network; configure the node data of the regional energy nodes, set the corresponding attribute information for each node, such as energy type, capacity, efficiency, etc., and construct the configured regional energy nodes, which can describe the various components of the regional energy system in more detail, provide more refined data support for the subsequent energy flow analysis and optimization, and enhance the pertinence and effectiveness of energy management; construct the regional energy topology network for the configured regional energy nodes based on the regional three-dimensional model, express the connection relationship between each energy node in the form of a network, and construct the regional energy topology 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.

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

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

[0147] In an embodiment of the present invention, by reading the configuration area energy node data, each node contains information such as node type and location coordinates, and the influence range or service range of each node is determined according to the node type and function. For example, for a thermal power station, its node range can be set to an area within a certain radius with the thermal power station as the center, and all buildings in the area use the thermal resources provided by the thermal power station. The determination of the node range can be set according to actual conditions, for example, it can be determined according to the coverage range of thermal pipelines, the coverage range of power lines, etc.

[0148] 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;

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

[0150] Step S253: performing node spatial relationship analysis on the three-dimensional node range data to obtain node spatial relationship data;

[0151] In an embodiment of the present invention, the spatial relationship between the nodes is determined by performing spatial relationship analysis on the ranges of different nodes. The spatial relationship analysis can be performed in a variety of ways, for example, it can be determined whether the ranges of two nodes intersect, contain, or are adjacent. For example, it can be determined whether the ranges of two thermal power stations overlap, or whether the range of one thermal power station contains the range of another thermal power station. The analysis results can be stored as a relationship table, which contains spatial relationship information between each node pair, for example, node A intersects with node B, node C contains node D, etc.

[0152] 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;

[0153] In an embodiment of the present invention, the adjacent nodes of each node are screened out according to the node spatial relationship data. For example, if the ranges of two nodes intersect or are adjacent, the two nodes are considered to be adjacent nodes. The screening process can traverse the node spatial relationship data, find the relationship record 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: performing adjacent connection path planning on the regional three-dimensional model based on the adjacent regional node data to obtain adjacent node path data;

[0155] In the embodiment of the present invention, a connection path is planned in the three-dimensional model for each node and its adjacent nodes. Path planning can use a variety of algorithms, such as the shortest path algorithm, A* algorithm, etc., to find the optimal path connecting two nodes according to elements such as roads and pipelines in the three-dimensional model. The result of path planning can be a series of point coordinate sequences, indicating the direction of the path.

[0156] 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.

[0157] In an embodiment of the present invention, a regional energy topological network is formed by establishing a connection relationship between nodes according to adjacent node path data. The topological network can be represented by a graph structure, in which nodes represent energy nodes, edges represent connection relationships between nodes, and edge weights can represent the length of the path or other attributes.

[0158] The present invention acquires node range data of energy nodes in the configured area, and can clarify the influence range or service area of ​​each energy node, such as the power supply range of the substation, the heating range of the thermal power station, etc., to provide necessary spatial information for subsequent node spatial relationship analysis and topological network connection, and ensure that the constructed energy topological network accurately reflects the actual situation; based on the node range data, the regional three-dimensional model is mapped to the node range, and the abstract node range data is visually presented 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 the subsequent spatial relationship analysis; the three-dimensional node range data is analyzed for node spatial relationship, such as judging whether there is overlap, adjacency, inclusion and other relationships between nodes, and obtaining node spatial relationship data, which can effectively Identify the spatial correlation between nodes to provide a basis for subsequent adjacent node acquisition and path planning; obtain adjacent area node data to accurately screen out the nodes that need to be connected, provide target node information for subsequent path planning, avoid unnecessary connections, and improve the efficiency of topological network construction; obtain adjacent node path data to simulate the actual energy transmission path, provide specific connection information for subsequent topological network connections, and make the constructed topological network more realistic and practical; based on the adjacent node path data, configure the regional energy nodes to connect the regional energy topological network, connect the adjacent nodes according to the planned path, and form a complete regional energy topological 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 comprises the following steps:

[0160] Step S31: Setting 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: performing adjacent security node analysis on the regional three-dimensional model based on the regional security node to obtain adjacent security nodes;

[0163] Step S34: acquiring the regional traffic path of the regional three-dimensional model to obtain regional traffic path data;

[0164] 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;

[0165] 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 obtain a regional security sequence.

[0166] As an embodiment of the present invention, refer to Figure 2 As shown, Figure 1 Detailed step flow diagram of step S3 in the embodiment, step S3 includes the following steps:

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

[0168] In an embodiment of the present invention, by reading the regional energy topology network data, the data 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 in the energy topology network are selected as security nodes. For example, 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, corresponding security resources are allocated according to the importance, risk level and security requirements of each security node. Security resources may include video surveillance equipment, infrared detectors, personnel patrols, intelligent security robots, etc. Resource allocation needs to consider factors such as resource type, quantity, deployment location and cost. For example, for important power plants, more video surveillance equipment and personnel patrols can be allocated; for transmission lines with lower risks, intelligent security robots can be deployed for inspection.

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

[0172] In the embodiment of the present invention, the spatial relationship between each security node and other surrounding security nodes is analyzed based on a three-dimensional model to determine the adjacent security nodes of each security node. Spatial analysis methods, such as buffer zone 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 zone can be set, and if the buffer zones of two security nodes intersect, the two nodes are considered to be adjacent nodes.

[0173] Step S34: acquiring the regional traffic path of the regional three-dimensional model to obtain regional traffic path data;

[0174] In the embodiment of the present invention, the data of the passage path in the area is extracted based on the three-dimensional model. The passage path can be a passable place such as a road, a sidewalk, a pipe gallery, etc. The element information in the three-dimensional model can be used to extract the passage path, such as a road network, a sidewalk network, etc. A path planning algorithm can also be used to plan a passage path based on information such as terrain and obstacles in the three-dimensional model.

[0175] 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;

[0176] In the embodiment of the present invention, by analyzing the feasible paths between each security node and its adjacent nodes, and selecting the optimal security path, the shortest path or the safest path connecting two nodes can be found based on the regional path data and the spatial relationship between the nodes. For example, paths with higher safety, such as roads and sidewalks, can be given priority to avoid paths that pass through dangerous areas.

[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 obtain a regional security sequence.

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

[0179] The present invention can deploy security monitoring equipment to key energy nodes to form regional security nodes by setting security nodes for the regional energy topology network, thereby providing infrastructure support for subsequent security monitoring and safety management, and ensuring the safe and stable operation of the energy system; the present invention can allocate security resources to the regional security nodes, for example, allocate corresponding monitoring ranges, alarm levels, response strategies, etc. to each security node, obtain security resource allocation data, and reasonably configure security resources according to the importance of different nodes and the security risk level, thereby improving the efficiency and pertinence of the security system and effectively reducing security risks; the present invention can obtain adjacent security nodes, establish spatial correlations between security nodes, and provide basic data for subsequent security passage path analysis and regional security sequence generation, Realize the coordinated linkage between security nodes; obtain regional access path data, build the access network within the area, provide basic path information for subsequent security access path analysis, and ensure that security monitoring covers all access paths; perform security access path analysis on adjacent security nodes based on regional access path data, such as analyzing whether there is a passable path between adjacent security nodes, as well as the length and safety of the path, and obtain node security access path data, which can evaluate the accessibility and access efficiency between adjacent security nodes; for example, based on the importance of security nodes, security risk levels, access paths and other information, generate the order of security patrols or monitoring, obtain the regional security sequence, and formulate reasonable security patrol or monitoring plans to improve the efficiency and coverage of security work.

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

[0181] Step S361: evaluating the regional security nodes for priority security nodes based on the security resource allocation data to obtain priority security node data;

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

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

[0184] 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;

[0185] 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.

[0186] As an embodiment of the present invention, refer to Figure 3 As shown, Figure 2FIG. 5 is a schematic diagram of a detailed step flow chart of step S36 in FIG. 5 . In this embodiment, step S36 includes the following steps:

[0187] Step S361: evaluating the regional security nodes for priority security nodes based on the security resource allocation data to obtain priority security node data;

[0188] In the embodiment of the present invention, all security nodes are prioritized according to factors such as the type and quantity of security resources, the importance of security nodes, and risk levels. For example, nodes that are assigned more video surveillance equipment and patrol personnel can be rated as high-priority nodes, while nodes that are assigned fewer security resources can be rated as low-priority nodes. Priority assessment can be performed in a variety of ways. For example, the priority score of each node can be calculated based on factors such as the total value of security resources and the weight of risk levels, and the nodes can be sorted according to the scores.

[0189] Step S362: acquiring node patrol frequency of regional security nodes based on security resource allocation data to obtain node patrol frequency data;

[0190] In the embodiment of the present invention, the patrol frequency of each security node is determined according to the type and quantity of security resources and the priority of the priority security node. For example, a high-priority node can be set with a higher patrol frequency, such as patrolling once an hour; a low-priority node 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 the node, and the risk level.

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

[0192] In the embodiment of the present invention, the security interval of each security node is set according to the patrol frequency of the node and the security requirements. The setting of the security interval needs to consider factors such as the risk level of the node, the response time of the security resources and the security strategy.

[0193] 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;

[0194] In the embodiment of the present invention, a security path sequence of each node is generated according to the patrol frequency of the node, the security interval, and the security passage path between the nodes. The security path sequence is a sequential list of path nodes, indicating the path sequence of the security action. For example, the generation of a node path sequence needs to consider the distance and travel time between the nodes 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 node and the node security path sequence. The regional security sequence is an overall order list of security actions, indicating the security action sequence of the entire area. For example, the security path sequence of the high priority node can be executed first, and then the security path sequence of the low priority node can be executed.

[0197] The present invention can identify nodes that need special attention and priority patrols by evaluating regional security nodes based on security resource allocation data, and provide a sorting basis for subsequent regional security sequence generation, ensuring that limited security resources can give priority to protecting the safety of key nodes; the node patrol frequency of regional security nodes is acquired based on security resource allocation data, and differentiated patrol strategies can be formulated according to the security requirements of different nodes, thereby improving the pertinence and effectiveness of security work and avoiding waste of resources; the node security interval is set for the regional security nodes, and the node security interval data is obtained, so that the patrol time can be reasonably arranged to avoid excessive patrols. Frequent or long intervals ensure that each node can be monitored in a timely and effective manner, while taking into account patrol efficiency; 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 data to ensure that security personnel can patrol each node in a predetermined order and frequency, thereby improving patrol efficiency and coverage; based on the node security path sequence, the regional security sequence is generated for the priority security node data to obtain the regional security sequence, which can formulate a globally optimized security patrol plan, ensure priority patrol of key nodes, and reasonably arrange patrol routes and times to maximize the security of the regional energy system.

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

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

[0200] In an embodiment of the present invention, a regional energy topology network model is used to calculate the connectivity of each node, that is, the number of other nodes directly connected to the node, using a graph theory algorithm, such as a degree centrality algorithm. Nodes with low connectivity are usually located at the edge of the network, and a connectivity threshold is set. Nodes below the 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 edge of the network and output a list of edge nodes.

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

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

[0203] Step S43: responding to the abnormal energy alarm of the network based on the network edge node to obtain an edge abnormal response node;

[0204] In the embodiment of the present invention, by associating the alarm with the identified network edge nodes, the affected edge nodes are screened out, and the risk level of each affected edge node is evaluated, such as high risk, medium risk, and low risk, based on factors such as the severity and type of the alarm and the importance of the edge node itself. All affected edge node information, including node ID, risk level, associated alarm information, etc., is sorted into a list and output as an edge abnormality response node.

[0205] 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;

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

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

[0208] In the embodiment of the present invention, by analyzing the type, function parameters, control interface and other characteristics of each security resource in the abnormal scheduling security sequence, the executable operation instruction set is determined. For example, the operations that can be executed by the surveillance camera include: screen zooming, angle adjustment, infrared night vision switching, real-time recording, etc.; the system formulates a detailed strategy execution process for each security resource according to the security needs and actual conditions, and clearly stipulates what operations are performed under what conditions, the specific parameters of the operation, the order and time interval of execution, etc. For example, for a high-risk node, the nearby surveillance camera immediately starts high-definition real-time monitoring after receiving the instruction, and transmits the picture to the monitoring center in real time; the strategy execution process of each security resource is integrated to form the final abnormal scheduling security strategy.

[0209] The present invention obtains network edge nodes by analyzing the network edge nodes of the regional energy topology network, and can determine the nodes in the energy system that directly interact with users. These nodes are usually more susceptible to external factors, so it is necessary to focus on their security and stability, and provide target node information for subsequent abnormal alarm responses; abnormal energy alarms are preset for the regional energy topology network to obtain network abnormal energy alarms, which can predict potential energy security risks in advance and formulate corresponding response measures to improve the security and reliability of the energy system, and provide a judgment basis for subsequent abnormal alarm responses; abnormal alarm responses to network abnormal energy alarms based on network edge nodes are obtained to obtain edge abnormal response nodes, which can Timely discover abnormal situations in the energy system and accurately locate abnormal nodes, provide target node information for subsequent security sequence scheduling, so as to quickly take countermeasures; extract and schedule the regional security sequence based on the edge abnormal response node to obtain the abnormal scheduling security sequence, which can dynamically adjust the security patrol plan according to the actual situation, give priority to the nodes with abnormalities, improve the security response speed and efficiency, and control security risks in time; generate abnormal scheduling security strategies for the abnormal scheduling security sequence, and obtain the abnormal scheduling security strategy, 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.

[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 embodiment of the present invention, a suitable node centrality algorithm is selected according to actual needs and network characteristics, such as degree centrality, betweenness centrality, closeness centrality, eigenvector centrality, etc. Different centrality algorithms focus on different network structure characteristics, for example, degree centrality focuses on the number of connections of a node, and betweenness centrality focuses on the number of times a node is on the shortest path in the network. The selected centrality algorithm is used to calculate the centrality value of each node in the network.

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

[0214] In an embodiment of the present invention, by analyzing the transmission path of data from the source node to the sink node based on the network topology structure and the data transmission protocol, the data transmission process can be simulated using a network simulation tool or algorithm, the amount of data received and sent by each node is recorded, and the data flow vector of each node is calculated based on the data transmission path and the amount of data. The data flow vector is a vector containing direction and size, which indicates the flow and direction of data passing through the node. For example, if a node mainly receives data from an upstream node and transmits it to a downstream node, its data flow vector points to the downstream direction, and the size of the vector is proportional to the data flow.

[0215] 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;

[0216] In an embodiment of the present invention, by determining the weight coefficients of node centrality and data flow vector in node importance evaluation according to actual needs, the standardized node centrality and data flow vector are weighted and summed according to the preset weight coefficients to obtain a comprehensive importance score for each node.

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

[0218] In an embodiment of the present invention, all nodes in the network are sorted in descending order according to the node importance score. The lower the importance score, the lower the ranking. According to the actual needs and the network scale, a preliminary identification ratio is determined, such as 20% or 30%. The ratio indicates how many proportions of nodes with lower rankings will be selected as potential edge nodes. According to the set preliminary identification ratio, the corresponding proportions of nodes with lower rankings are selected and marked as potential edge nodes. For example, if there are 100 nodes in the network and the preliminary identification ratio is set to 20%, the 20 nodes with lower rankings will be selected as potential edge nodes.

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

[0220] In an embodiment of the present invention, the computing requirements of the node are quantified by selecting appropriate indicators, such as CPU occupancy, memory occupancy, network bandwidth requirements, storage space requirements, etc., and the amount of computing resources required for each node to complete its preset function is evaluated based on the node function and historical operation data. Relevant data can be obtained by using simulation, performance testing and other methods. For example, for a data acquisition node, the amount of data required to be collected per second, the CPU resources required for data preprocessing, the storage space required for data storage, etc. can be evaluated.

[0221] 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.

[0222] In an embodiment of the present invention, by analyzing the node computing demand data, the overall distribution of computing resource requirements in the network is understood. For example, the average values ​​of different computing resource indicators and the differences in computing requirements of different types of nodes can be counted. According to the distribution of computing resource requirements, a reasonable computing demand threshold is set. Nodes with computing requirements lower than the threshold are generally considered to have simpler functions, lower computing resource requirements, and are located at the edge of the network. Combined with the preliminary identification data of the edge nodes, potential edge nodes with computing requirements lower than the threshold are confirmed and finally determined as network edge nodes.

[0223] The present invention can quantify the importance and influence of nodes in the network by calculating the node centrality of the regional energy topology 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 key path connecting different network areas, providing basic data for subsequent node importance evaluation; performing data flow analysis on the regional energy topology network can grasp the flow law of energy data in the network and provide supplementary information for subsequent node importance evaluation. For example, nodes with large data flow usually undertake more important data transmission tasks; performing node importance evaluation on the regional energy topology network based on node centrality and data flow vector can more comprehensively evaluate the importance of nodes in the network, and focus on their security and stability, which is necessary for subsequent Provide a basis for edge node identification; identify edge nodes in the regional energy topology network based on node importance, and filter out less important nodes in the network. These nodes are usually located at the edge of the network, connected to end users or less important devices, and provide a set of candidate nodes for subsequent edge node positioning; perform node computing demand analysis on the regional energy topology network to understand the computing resource requirements of each node and provide supplementary information for subsequent edge node positioning; locate edge nodes based on preliminary identification data of edge nodes based on node computing demand data, and more accurately identify edge nodes in the network. These nodes usually have weak computing power, are connected to end users or less important devices, and require 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 comprising:

[0225] 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;

[0226] 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;

[0227] 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;

[0228] 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.

[0229] In summary, the present invention provides an IOC intelligent operation system and method. The IOC intelligent operation system consists 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. It can realize any IOC intelligent operation method described in the present invention, and is used to combine the operations between computer programs running on various modules to realize any IOC intelligent operation method. The internal structures of the system cooperate with each other, which can greatly reduce duplication of work and manpower investment, 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 description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may 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 the embodiments shown herein, but should 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 multi-energy data to obtain real-time multi-energy data; constructing 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; 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; Generate a regional security sequence for the regional security nodes based on the regional traffic path data 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 abnormal network energy alarms; Based on the network edge nodes and network abnormal energy alarms, an abnormal scheduling security strategy is generated for the regional security sequence to obtain the abnormal scheduling security strategy.

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 configured regional energy nodes 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 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 nodes based on the node security passage path data and the security resource allocation data to obtain a regional security sequence.

7. The IOC intelligent operation method according to claim 6, characterized in that: Step S36 includes the following steps: Step S361: evaluating the regional security nodes for priority security nodes based on the 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: Generate a regional security sequence for the priority security node data based on the node security path sequence to obtain a regional security sequence.

8. 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.

9. The IOC intelligent operation method according to claim 8, 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.

10. 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; 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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