Power transformation element cosmic scene construction method constructed by virtual engine and data system of power transformation element cosmic scene construction method

Abstract

The invention provides a power transformation element universe scene construction method constructed by a virtual engine and a data system thereof, and the construction method comprises the following steps: obtaining the related data of a transformer substation, including the three-dimensional model data of transformer substation equipment, and obtaining the real-time detection data of the transformer substation equipment through a sensor network; importing three-dimensional model data of the equipment into a virtual engine, associating real-time detection data with an equipment model in a virtual scene, and establishing data monitoring; establishing a fault diagnosis module in the virtual engine to perform fault diagnosis and defect analysis on the equipment according to the real-time detection data of the equipment; an inspection virtual person model is created, association between an inspection virtual person and the detection data is established, and the inspection virtual person moves according to the key position between the two sensors determined by optimization of the sensor layout. According to the invention, the accuracy of detection data and the inspection efficiency can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of substation inspection, and in particular to a method for building a substation metaverse scene using a virtual engine and a data system thereof. Background Art

[0002] In the power industry, substations are important energy conversion and distribution facilities, and their operation and management are of great significance. In recent years, the development of substations has experienced a transition from digital substations, smart substations to a new generation of smart substations, and the entire station has been digitized. However, compared with this, with the increasing scale of the power grid, the contradiction between the traditional operation and inspection model and the development of the power grid has become prominent, and the operation and inspection model of substations and substations has lagged behind significantly.

[0003] Traditional substation inspection methods have many problems, such as the need to shut down the power system, safety risks and high personnel costs. Currently, virtual reality technology has been applied to substation inspection, such as using 3D modeling technology to create a three-dimensional model of the substation, and operating and demonstrating it in a virtual reality environment. At the same time, there are also some mobile terminal applications based on virtual reality technology, such as using smartphones and VR glasses to enable users to conduct virtual inspections at any location.

[0004] However, these existing technical solutions have many problems, such as poor accuracy of device detection data by sensor networks and low inspection efficiency. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a substation metaverse scene construction method and a data system constructed by a virtual engine in response to the deficiencies in the prior art, so as to improve the accuracy of detection data and improve inspection efficiency.

[0006] In order to solve the above technical problems, the technical solution of the present invention is:

[0007] A method for constructing a substation metaverse scene using a virtual engine comprises the following steps: obtaining substation-related data, including three-dimensional model data of substation equipment, and obtaining real-time detection data of substation equipment using a sensor network; importing the three-dimensional model data of the equipment into the virtual engine, and associating the real-time detection data with the equipment model in the virtual scene to establish data monitoring; establishing a fault diagnosis module in the virtual engine to perform fault diagnosis and defect analysis on the equipment according to the real-time detection data of the equipment; creating a patrol virtual human model, and establishing an association between the patrol virtual human and the detection data, wherein the patrol virtual human moves according to a key position between two sensors determined by optimizing the sensor layout.

[0008] Preferably, the management data of the equipment includes the technical parameters, operation history data and maintenance records of the equipment, including the normal working status data and known fault status data of the equipment; the real-time detection data of the equipment includes the current, voltage, temperature and vibration of the equipment; the sensor network includes an infrared sensor for detecting the status of the equipment, a temperature sensor for detecting the temperature of the equipment, a humidity sensor for detecting the humidity of the equipment environment, a current sensor for detecting the current of the equipment, a voltage sensor for detecting the voltage of the equipment and a vibration sensor for detecting the working status of the equipment.

[0009] Preferably, the virtual engine is Unreal Engine or Unity.

[0010] Preferably, the fault diagnosis algorithm adopted by the fault diagnosis module is a machine learning-based fault diagnosis algorithm or a model-based fault diagnosis algorithm; the diagnosis and analysis results are displayed in a visual manner in a virtual scene.

[0011] Preferably, when using the sensor network to obtain real-time detection data of substation equipment, the sensor layout is optimized to improve the accuracy and comprehensiveness of the real-time detection data: the accuracy component, coverage component and data redundancy component of the sensor are comprehensively considered to determine the detection efficiency value of the sensor; based on the detection efficiency value of the sensor, the layout of the two sensors is optimized using the lever principle.

[0012] Preferably, the optimization of the sensor layout includes adjusting the micro-position of the sensor, optimizing the sensor parameters or adding auxiliary sensors; wherein, the adjustment of the micro-position of the sensor includes adjusting the installation angle, height or distance of the sensor from the device; the sensor parameter optimization includes adjusting the sensitivity, sampling frequency or data processing algorithm of the sensor; when the device position is fixed, if it is found that the existing sensor layout has obvious detection blind spots or imbalances, auxiliary sensors can be added at appropriate locations.

[0013] Preferably, the accuracy component A of the sensor is expressed as the degree of deviation between the measurement error of the sensor and the ideal accurate value; the coverage component C of the sensor is expressed as the effective detection range of the sensor, measured by the volume or area covered by the sensor; the data redundancy component R of the sensor is expressed as the degree of similarity of the measurement values ​​of multiple sensors at the same position or similar positions; combining the above three components, the detection efficiency value of the sensor P = ω1A + ω2C - ω3R, where ω1, ω2, ω3 are weight coefficients.

[0014] Preferably, the ratio of the detection efficiency values ​​of different sensors is calculated to find the fulcrum position of the two sensors, and this fulcrum position is the key position for the movement of the inspection virtual human or provides a reference indication for it.

[0015] Preferably, for a fulcrum position that the inspection virtual human cannot enter or is in a dangerous area, a safety buffer zone is set for the fulcrum position.

[0016] A data system related to the above-mentioned substation metaverse scene construction method, including a device model data system, a scene environment data system, a business management data system and a sensor energy efficiency database table; the device model data system includes a device parameter database, a device geometric model library and a device status monitoring data interface; the scene environment data system includes a topography database and a building layout database; the business management data system includes an inspection task database, a maintenance record database and a fault case database; the sensor energy efficiency database table includes two tables, one table is used to store the sensor's detection energy efficiency value, and the fields in the table include sensor number, sensor type, detection energy efficiency value and calculation time, and the other table is used to store fulcrum position data, and the fields in the table include fulcrum number, numbers corresponding to two sensors, coordinates of the fulcrum position and calculation time.

[0017] The beneficial effects of the present invention are:

[0018] The present invention can efficiently complete the daily inspection and fault diagnosis of the substation through the construction of the substation metaverse scene, which helps to improve the inspection efficiency, reduce the risk probability of on-site operations and ensure the safe operation of the power grid. The present invention optimizes the sensor layout by using the lever principle, which can more scientifically determine the position of the sensor and improve the accuracy and comprehensiveness of the detection data. By calculating the ratio of the detection efficiency values ​​of different sensors, the fulcrum position of the two sensors is determined to reflect the relative importance and balance state of different sensors in the detection task, and also provides a more reliable data basis for subsequent fault diagnosis and defect analysis. The fulcrum position represents the key position in the sensor layout, which is often a place where the detection efficiency is relatively balanced, and is also an area where faults may occur or need to be focused on. The virtual man moves at these positions, which can be more efficient in inspection and monitoring, and abnormal conditions of the equipment can be discovered in time. At the same time, by moving at the fulcrum position, the virtual man can better integrate the data of different sensors for more accurate fault diagnosis and defect analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a flow chart of the method for building a substation meta-universe scene of the present invention. DETAILED DESCRIPTION

[0020] To facilitate understanding of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood by those skilled in the art that the embodiments are only intended to help understand the present invention and should not be regarded as specific limitations of the present invention.

[0021] In the description of the following embodiments, specific details such as specific system structures, technologies, etc. are provided for illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0022] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or their collections. It should also be understood that the term "and / or" used in the present specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations.

[0023] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0024] In addition, in the description of the present application specification and the attached claims, the terms "first", "second", "third", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance. The reference to "one embodiment" or "some embodiments" described in the present application specification means that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in the different parts of this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0025] Embodiment 1:

[0026] like Figure 1As shown, the present invention provides a method for building a substation metaverse scene using a virtual engine, comprising the following steps: obtaining substation-related data, including three-dimensional model data of substation equipment, and using a sensor network to obtain real-time detection data of substation equipment; importing the three-dimensional model data of the equipment into the virtual engine, and associating the real-time detection data with the equipment model in the virtual scene to establish data monitoring; establishing a fault diagnosis module in the virtual engine to perform fault diagnosis and defect analysis on the equipment based on the real-time detection data of the equipment; creating an inspection virtual human model, and establishing an association between the inspection virtual human and the detection data, wherein the inspection virtual human moves according to the key position between two sensors determined by optimizing the sensor layout.

[0027] Embodiment 2:

[0028] Obtaining 3D model data of substation equipment: The first step is data collection, including: collecting actual data of the substation, including the geometry, size, electrical parameters and layout of the equipment; collecting on-site photos and videos of the substation for reference and texture production; collecting relevant technical documents and operating procedures for information display and interactive design in the scene.

[0029] Based on the collected data, 3D modeling software is used to create 3D models of substation equipment and buildings, and the models are optimized, such as reducing the number of polygons and improving rendering efficiency, to ensure that the models accurately reflect the actual appearance and structure of the equipment; the created 3D models are then imported into the virtual engine; the terrain and environment of the substation, such as the ground, grass, and walls, are constructed in the virtual engine, and then the equipment models are placed according to the actual layout to ensure that the position and direction of the equipment are consistent with the actual substation layout; materials and textures are added to the model to make it more realistic; lighting and weather effects are set to enhance the realism of the scene.

[0030] Equipment management data is used to understand the performance, operating status, maintenance requirements of equipment, and to perform fault diagnosis and prediction, so as to help operation and maintenance personnel better manage and maintain substation equipment and improve equipment reliability and safety. Equipment management data includes equipment technical parameters, operating history data and maintenance records, including normal working status data and known fault status data of equipment. Technical parameters provide the basic specifications and performance indicators of equipment, which are an important basis for equipment selection, installation and commissioning; operating history data records the operating status of equipment at different time points, including changes in parameters such as current, voltage, and temperature, which can be used to analyze equipment operating trends and potential problems; maintenance records include equipment maintenance time, maintenance content, replaced parts and other information, which helps to formulate reasonable maintenance plans and budgets.

[0031] The sensor network is used to obtain real-time detection data of substation equipment. Real-time detection data includes equipment current, voltage, temperature, vibration and other data. The sensor network includes infrared sensors for detecting equipment status, temperature sensors for detecting equipment temperature, humidity sensors for detecting equipment ambient humidity, current sensors for detecting equipment current, voltage sensors for detecting equipment voltage and vibration sensors for detecting equipment working status.

[0032] Embodiment 3:

[0033] Considering factors such as graphics rendering capabilities, physical simulation accuracy, and data interfaces, the virtual engine uses UnrealEngine or Unity. The virtual engine supports the creation and control of virtual human characters, and has a complete animation system, character control script interface, etc.

[0034] Perform basic settings for the virtual engine, including adjusting graphics quality, physical simulation parameters, lighting settings, etc.; import necessary plug-ins and tools, such as electrical simulation plug-ins, data visualization tools, etc., to meet the specific needs of substation scenarios; configure the physical simulation parameters of the virtual engine so that the virtual human can interact reasonably with objects in the scene.

[0035] Data association and monitoring system construction: associate the collected real-time detection data and electrical parameters with the three-dimensional model of the equipment in the virtual scene to reflect the real status of the equipment in real time in the virtual scene; build a data monitoring system to display the various parameters of the equipment in real time; set thresholds for the various parameters of the equipment and issue an alarm when the parameters exceed the normal range.

[0036] Embodiment 4:

[0037] Using the scripting and programming functions of the virtual engine, a fault diagnosis module is established: the fault diagnosis algorithm is integrated into the virtual engine to perform fault diagnosis and defect analysis on the equipment based on the real-time detection data of the equipment. The fault diagnosis algorithm can adopt a machine learning-based fault diagnosis algorithm or a model-based fault diagnosis algorithm. The diagnosis and analysis results are displayed in a visual way in the virtual scene, such as using different colors or animation effects to represent different fault states.

[0038] Embodiment 5:

[0039] Create a patrol virtual human model, implement the patrol virtual human's motion control and behavior logic, so that it can conduct patrols in virtual scenes, observe equipment status at close range, trigger specific diagnostic operations, etc. Establish an association between the patrol virtual human and the detection data, for example, the virtual human can read the equipment's status data and respond accordingly to changes in the data.

[0040] Users can interact with the virtual inspection person. For example, users can control the movement, actions and perspective of the virtual inspection person through the keyboard, mouse or handle, or control the virtual inspection person to perform inspections or specific operations through voice commands. The status information of the virtual person, such as location and health status, is displayed on the user interface.

[0041] The inspection avatar can trigger the fault diagnosis algorithm. For example, when the inspection avatar approaches the equipment, it will automatically diagnose the fault or prompt the user to perform a diagnostic operation. The avatar can take corresponding actions based on the diagnosis results, such as marking the faulty equipment and issuing an alarm.

[0042] Embodiment 6:

[0043] When using the sensor network to obtain real-time detection data of substation equipment, the sensor layout is optimized to improve the accuracy and comprehensiveness of the real-time detection data: the accuracy component, coverage component and data redundancy component of the sensor are comprehensively considered to determine the detection efficiency value of the sensor; based on the detection efficiency value of the sensor, the leverage principle is used to optimize the layout of the two sensors.

[0044] Optimization of sensor layout, including adjustment of sensor micro-position, optimization of sensor parameters or addition of auxiliary sensors; among which, adjustment of sensor micro-position includes adjustment of sensor installation angle, height or distance from the device; sensor parameter optimization includes adjustment of sensor sensitivity, sampling frequency or data processing algorithm; when the device position is fixed, if it is found that the existing sensor layout has obvious detection blind spots or imbalance, auxiliary sensors can be added at appropriate locations.

[0045] Sensor accuracy component A: For each sensor, its accuracy component is determined based on the degree of deviation between its measurement error and the ideal accurate value. The calculation formula of accuracy component A is: A = 1 / (1+E); where E is the measurement error, which can be the sum of the squares of the difference between the actual value and the measured value. The smaller the measurement error, the larger the accuracy component.

[0046] The coverage component C is expressed as the effective detection range of the sensor, measured by the volume or area covered by the sensor. Let the effective detection radius of the sensor be r. For three-dimensional space, the spherical volume it covers is For a two-dimensional plane, the circular area it covers is S = πr 2 The coverage component C is then expressed as: C=kV (or C=kS), where k is an adjustment coefficient used to adjust the coverage value to a suitable range.

[0047] The data redundancy component R is expressed as the similarity of the measured values ​​of multiple sensors at the same or similar locations. The calculation formula of the data redundancy component R is: R = 1 / (1+D), where D is the data redundancy index, which can be the variance of the measured values ​​of multiple sensors. The smaller the data redundancy, the larger the data redundancy component.

[0048] Combining the above three components, the detection performance value P is expressed as: P = ω1A + ω2C - ω3R, where ω1, ω2, and ω3 are weight coefficients used to adjust the relative importance of accuracy, coverage, and data redundancy.

[0049] For example, for two sensors S1 and S2, their detection efficiency values ​​are P1 and P2 respectively. Consider these two sensors S1 and S2 as the two ends of a lever, and the end with a larger detection efficiency value is "heavier". Using the line connecting the positions of these two sensors as a lever, we can find a fulcrum position that makes the lever balanced.

[0050] Assume the position of sensor S1 is (x1, y1, z1), the position of sensor S2 is (x2, y2, z2), and the position of the fulcrum is (x, y, z). According to the lever principle, P1×d1=P2×d2, where d1 is the distance from the fulcrum to sensor S1, and d2 is the distance from the fulcrum to sensor S2. The distance can be calculated using the Euclidean distance formula, that is, Substituting the lever balance formula above, solving for the fulcrum position (x, y, z) yields the fulcrum position that makes the two sensors S1 and S2 "balanced" in terms of detection performance.

[0051] Using the idea of ​​lever principle to measure the rationality of the layout of two sensors can provide a reference for optimizing the sensor layout. At the same time, this method can be extended to the case of multiple sensors, and the position of the sensors can be gradually adjusted to improve the accuracy and comprehensiveness of the detection data.

[0052] Embodiment 7:

[0053] Calculate the ratio of the detection efficiency values ​​of different sensors and find the fulcrum position of the two sensors. This fulcrum position is the key position for the movement of the inspection virtual man or provides a reference indication for it to improve efficiency and enhance accuracy.

[0054] The virtual man moves at a certain fulcrum position, which can avoid aimless inspections and save time and resources. The virtual man can focus on key areas for inspection, increasing the probability and speed of fault discovery. For example, in a large substation, the virtual man can quickly switch between various fulcrum positions to focus on important equipment and potential fault areas instead of moving randomly throughout the substation.

[0055] The fulcrum positions are obtained based on the optimization of sensor layout, and these positions usually have higher detection efficiency. The virtual human can obtain more accurate data by monitoring these positions, which can improve the accuracy of fault diagnosis and defect analysis. For example, when the virtual human detects abnormal data near the fulcrum position, it can combine the data of surrounding sensors for comprehensive analysis to more accurately determine the fault type and location.

[0056] For the fulcrum position where the inspection dummy cannot enter or is in a dangerous area, a safety buffer zone is set for the fulcrum position, and the fulcrum position provides a reference for the movement of the inspection dummy. When the dummy approaches the dangerous area, it will keep a certain distance to ensure that it does not enter the dangerous area. The size of this buffer zone can be determined according to the degree of danger of the high-voltage equipment and safety regulations.

[0057] The virtual human can use the path planning algorithm to automatically re-plan the inspection route when it detects that the fulcrum position is in an inaccessible or dangerous area, bypassing the dangerous area to reach a nearby safe location, while minimizing the impact on inspection efficiency.

[0058] Embodiment 8:

[0059] The present invention also provides a data system related to the above-mentioned substation metaverse scene construction method, including a device model data system, a scene environment data system, a business management data system and a sensor energy efficiency database table; the device model data system includes a device parameter database, a device geometry model library and a device status monitoring data interface; the scene environment data system includes a topography database and a building layout database; the business management data system includes an inspection task database, a maintenance record database and a fault case database; the sensor energy efficiency database table includes two tables, one table is used to store the sensor's detection energy efficiency value, and the fields in the table include sensor number, sensor type, detection energy efficiency value and calculation time, and the other table is used to store fulcrum position data, and the fields in the table include fulcrum number, the numbers corresponding to the two sensors, the coordinates of the fulcrum position and calculation time.

[0060] The equipment parameter database is used to store the technical parameters of various equipment in the substation, such as the rated capacity, voltage level and short-circuit impedance of the transformer; the breaking current, rated voltage and number of operations of the circuit breaker. The equipment geometric model library is used to store the three-dimensional model data of the equipment created by point cloud scanning, drone scanning or 3D modeling software. Each device has a corresponding model file, which records the geometric information of the device such as shape, size, structure, etc., so as to accurately present the appearance of the device in the virtual scene. The equipment status monitoring data interface is used to connect to the real-time monitoring system of the substation to obtain the status data of the equipment in actual operation, such as the temperature, vibration, current, voltage, etc. of the equipment. These data will be transmitted to the virtual scene in real time to synchronize the status of the virtual equipment with the actual equipment, so that the operation and maintenance personnel can accurately understand the operating status of the equipment in the virtual environment.

[0061] The topographic database is used to record the topographic and geomorphic information of the area where the substation is located, including ground elevation, terrain undulations, geological conditions, etc. These data are used to construct the base of the metaverse scene, so that the terrain of the virtual scene is consistent with the terrain of the actual substation. The building layout database is used to store the layout information of the buildings in the substation, such as the location, shape, size, floor structure, etc. of the substation building, as well as the location and size of ancillary facilities such as walls and roads. These data are used to construct the overall outline of the substation, so that the architectural layout of the virtual scene is consistent with the actual substation.

[0062] The inspection task database is used to store the inspection task information of the substation, such as inspection routes, inspection points, inspection time, etc. The virtual person can perform simulated inspections in the virtual scene based on the inspection task data to help the operation and maintenance personnel become familiar with the inspection process and equipment location. The maintenance record database is used to store the maintenance history of the equipment, such as maintenance time, maintenance content, maintenance personnel, etc. These data can provide a reference for equipment maintenance for the virtual person and help the operation and maintenance personnel formulate a reasonable maintenance plan. The fault case database is used to store the fault case information of the substation equipment, such as fault type, fault cause, fault handling method, etc. When a virtual device fails, the system can provide corresponding fault diagnosis and handling suggestions based on the fault case data to help the operation and maintenance personnel quickly solve the problem.

[0063] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0064] In various embodiments, the hardware implementation of the technology can directly adopt existing intelligent devices, including but not limited to industrial computers, PCs, smart phones, handheld stand-alone computers, floor-standing stand-alone computers, etc. Its input device preferably adopts a screen keyboard, its data storage and calculation module adopts existing memory, calculator, controller, its internal communication module adopts existing communication ports and protocols, and its remote communication adopts existing GPRS network, World Wide Internet, etc.

[0065] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0066] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms. The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0067] Each functional unit in each embodiment of the present invention may be integrated into a processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of a software functional unit. If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it may be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and may also instruct the relevant hardware to complete it through a computer program. The computer program may be stored in a computer-readable storage medium, and the computer program may be executed by the processor to implement the steps of the above-mentioned various method embodiments. Among them, the computer program includes computer program code, and the computer program code may be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0068] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for constructing a substation metaverse scene using a virtual engine, characterized in that: The following steps are involved: Obtain substation-related data, including 3D model data of substation equipment, and use sensor networks to obtain real-time detection data of substation equipment; import the 3D model data of the equipment into the virtual engine, and associate the real-time detection data with the equipment model in the virtual scene to establish data monitoring; A fault diagnosis module is established in the virtual engine to perform fault diagnosis and defect analysis on the equipment according to the real-time detection data of the equipment; a patrol virtual human model is created, and an association between the patrol virtual human and the detection data is established, and the patrol virtual human moves according to the key position between two sensors determined by the sensor layout optimization.

2. The method for constructing a substation metaverse scene using a virtual engine according to claim 1, characterized in that: The management data of the equipment includes the technical parameters, operation history data and maintenance records of the equipment, including the normal working status data and known fault status data of the equipment; the real-time detection data of the equipment includes the current, voltage, temperature and vibration of the equipment; the sensor network includes an infrared sensor for detecting the status of the equipment, a temperature sensor for detecting the temperature of the equipment, a humidity sensor for detecting the humidity of the equipment environment, a current sensor for detecting the current of the equipment, a voltage sensor for detecting the voltage of the equipment and a vibration sensor for detecting the working status of the equipment.

3. The method for constructing a substation metaverse scene using a virtual engine according to claim 1, characterized in that: The virtual engine is Unreal Engine or Unity.

4. The method for constructing a substation metaverse scene using a virtual engine according to claim 1, characterized in that: The fault diagnosis algorithm adopted by the fault diagnosis module is a machine learning-based fault diagnosis algorithm or a model-based fault diagnosis algorithm; the diagnosis and analysis results are displayed in a visual manner in a virtual scene.

5. The method for constructing a substation metaverse scene using a virtual engine according to claim 1, characterized in that: When using the sensor network to obtain real-time detection data of substation equipment, the sensor layout is optimized to improve the accuracy and comprehensiveness of the real-time detection data: the accuracy component, coverage component and data redundancy component of the sensor are comprehensively considered to determine the detection efficiency value of the sensor; based on the detection efficiency value of the sensor, the leverage principle is used to optimize the layout of the two sensors.

6. The method for constructing a substation metaverse scene using a virtual engine according to claim 1, characterized in that: The optimization of the sensor layout includes adjusting the micro-position of the sensor, optimizing the sensor parameters or adding auxiliary sensors; wherein, the adjustment of the micro-position of the sensor includes adjusting the installation angle, height or distance of the sensor from the device; the sensor parameter optimization includes adjusting the sensitivity, sampling frequency or data processing algorithm of the sensor; when the device position is fixed, if it is found that the existing sensor layout has obvious detection blind spots or imbalances, auxiliary sensors can be added at appropriate locations.

7. The method for constructing a substation metaverse scene using a virtual engine according to claim 5, characterized in that: The accuracy component A of the sensor is expressed as the degree of deviation between the measurement error of the sensor and the ideal accurate value; the coverage component C of the sensor is expressed as the effective detection range of the sensor, measured by the volume or area covered by the sensor; the data redundancy component R of the sensor is expressed as the similarity of the measurement values ​​of multiple sensors at the same position or near positions; Combining the above three components, the detection efficiency value of the sensor is P=ω1A+ω2C-ω3R, where ω1, ω2, and ω3 are weight coefficients.

8. The method for constructing a substation metaverse scene using a virtual engine according to claim 5, characterized in that: Calculate the ratio of the detection efficiency values ​​of different sensors and find the fulcrum position of the two sensors. This fulcrum position is the key position for the movement of the inspection virtual human or provides a reference indication for it.

9. The method for constructing a substation metaverse scene using a virtual engine according to claim 8, characterized in that: For the fulcrum position that the inspection virtual human cannot enter or is in a dangerous area, a safety buffer zone is set for the fulcrum position.

10. A data system related to the method for building a substation meta-universe scene according to any one of claims 1 to 9, characterized in that: It includes a device model data system, a scene environment data system, a business management data system and a sensor energy efficiency database table; the device model data system includes a device parameter database, a device geometry model library and a device status monitoring data interface; the scene environment data system includes a topography database and a building layout database; the business management data system includes an inspection task database, a maintenance record database and a fault case database; the sensor energy efficiency database table includes two tables, one table is used to store the sensor's detection energy efficiency value, and the fields in the table include sensor number, sensor type, detection energy efficiency value and calculation time, and the other table is used to store fulcrum position data, and the fields in the table include fulcrum number, numbers corresponding to two sensors, coordinates of the fulcrum position and calculation time.

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