Digital and intelligent virtual park power grid management method and platform

Abstract

The present invention provides a digital virtual park power grid management method and platform, which relates to the field of data processing technology, including: determining in a twin model of a digital virtual park based on the actual position and image acquisition range to obtain a virtual position and virtual acquisition range of each monitoring terminal in the twin model; determining the position of each virtual acquisition range of the twin model to obtain first position information, determining a non-virtual acquisition range of the twin model to obtain non-virtual acquisition position information, and analyzing the non-virtual acquisition position information based on the first position information to obtain multiple second position information; associating the first position information and the second position information to obtain an associated group, and adding a corresponding virtual prediction strategy to the associated group; determining the virtual power grid equipment of the twin model, and performing management prediction on the virtual power grid equipment based on the virtual acquisition range and the virtual prediction strategy to obtain management data.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a digital virtual park power grid management method and platform. Background Art

[0002] With the continuous advancement of smart city and smart park construction, the demand for energy management and monitoring in parks is increasing. Modern parks usually integrate a variety of infrastructure equipment, such as surveillance cameras, power grid equipment, smart sensors, etc., to achieve efficient management and security monitoring. However, under the traditional physical park management model, monitoring data and energy management are usually scattered in various subsystems, making it difficult for managers to fully grasp the real-time operation of the park.

[0003] Currently, many parks use video surveillance systems and separate energy management systems to handle different management needs. However, there is a lack of effective linkage and data sharing between these systems, resulting in the emergence of information islands. In addition, due to the lack of comprehensive analysis capabilities for equipment status and environmental data, the existing system is unable to achieve early warning and trend prediction of events within the park, affecting management efficiency and timeliness of decision-making.

[0004] Therefore, how to combine virtual models to provide early warning and trend forecasts for events within the park and improve management efficiency has become an urgent problem that needs to be solved. Summary of the invention

[0005] The embodiments of the present invention provide a digital and intelligent virtual park power grid management method and platform, which combine virtual models to provide early warning and trend forecasts for events within the park, thereby improving management efficiency.

[0006] A first aspect of an embodiment of the present invention provides a digital and intelligent virtual park power grid management method, including:

[0007] Obtain monitoring data from all monitoring terminals in the digital virtual park and determine the actual location and image acquisition range of each monitoring terminal in the park;

[0008] Based on the actual position and image acquisition range, the virtual position and virtual acquisition range of each monitoring terminal in the twin model are determined in the digital virtual park;

[0009] Determine the position of each virtual acquisition range of the twin model to obtain first position information, determine the non-virtual acquisition range of the twin model to obtain non-virtual acquisition position information, and analyze the non-virtual acquisition position information based on the first position information to obtain multiple second position information;

[0010] Associating the first position information with the second position information to obtain an associated group, and adding a corresponding virtual prediction strategy to the associated group;

[0011] Determine the virtual power grid equipment of the twin model, perform management prediction of the virtual power grid equipment based on the virtual acquisition range and the virtual prediction strategy, and obtain management data.

[0012] Optionally, in a possible implementation manner of the first aspect, acquiring monitoring data of all monitoring terminals in the digital virtual park and determining the actual location and image acquisition range of the park where each monitoring terminal is located includes:

[0013] Obtain the actual location of each monitoring terminal in the park, each monitoring terminal has a preset actual location;

[0014] The rotation interval of each monitoring terminal is obtained, and the image acquisition range of the monitoring terminal at different rotation angles is determined based on the rotation interval.

[0015] Optionally, in a possible implementation manner of the first aspect, the determination is performed in a twin model of the digital virtual park based on the actual position and the image acquisition range, and obtaining a virtual position and a virtual acquisition range of each monitoring terminal in the twin model includes:

[0016] Receive the lens rated angle and lens rated focal length configured by the user for each monitoring terminal;

[0017] Based on the lens rated angle and lens rated focal length, determine the effective image acquisition range of each monitoring terminal in a non-rotating state, wherein the effective image acquisition range is less than or equal to the actual acquisition range of the monitoring terminal;

[0018] Determine the virtual position of each monitoring end in the twin model, and determine the virtual collection range of the monitoring end in the twin model at different rotation angles based on the rotation range of each monitoring end and the positional relationship of each module in the twin model.

[0019] Optionally, in a possible implementation manner of the first aspect, the calculating based on the rated angle of the lens and the rated focal length of the lens to determine the effective image acquisition range of each monitoring terminal in a non-rotating state, wherein the effective image acquisition range is less than or equal to the actual acquisition range of the monitoring terminal, includes:

[0020] Taking the virtual position as the starting position, diverging according to the direction of the monitoring terminal and the rated angle of the lens to obtain a preliminary virtual collection range;

[0021] The effective acquisition distance is determined based on the rated focal length of the lens, and the effective acquisition distance is extended toward the monitoring end to determine the overlapping area of ​​the extended distance and the preliminary virtual acquisition range as the effective image acquisition range.

[0022] Optionally, in a possible implementation manner of the first aspect, determining the position of each virtual acquisition range of the twin model to obtain first position information, determining the non-virtual acquisition range of the twin model to obtain non-virtual acquisition position information, and analyzing the non-virtual acquisition position information based on the first position information to obtain multiple second position information include:

[0023] Determine the position of each virtual acquisition range of the twin model to obtain first position information, where the first position information is a position where image information can be extracted within a range of rotation allowed by the monitoring end;

[0024] Obtain the three-dimensional coordinate system of the twin model, extract all three-dimensional coordinate points of each twin module of the twin model to obtain a three-dimensional coordinate body set, and extract the three-dimensional coordinate points of each surface of each twin module to obtain a three-dimensional coordinate surface set;

[0025] The faces in each set of three-dimensional coordinate faces that are perpendicular to the Z axis and close to the positive direction of the Z axis are taken as faces to be determined, so that all faces to be determined are positive surfaces of the corresponding twin modules in the twin model;

[0026] Based on the to-be-determined surface and the first position information, an associated surface is obtained, and a three-dimensional coordinate surface set of other to-be-determined surfaces corresponding to the associated surface is determined to obtain non-virtual acquisition position information and second position information.

[0027] Optionally, in a possible implementation manner of the first aspect, obtaining an associated surface based on the surface to be determined and the first position information, and determining a three-dimensional coordinate surface set of other surfaces to be determined corresponding to the associated surface to obtain the non-virtual acquisition position information and the second position information include:

[0028] If it is determined that the coincidence rate between the three-dimensional coordinate point of the to-be-determined surface and the three-dimensional coordinate point of the first position information is greater than or equal to a first preset value, the corresponding to-be-determined surface is used as an associated surface;

[0029] If it is determined that the coincidence rate between the three-dimensional coordinate point of the to-be-determined surface and the three-dimensional point coordinates of the first position information is greater than 0 and less than a first preset value, the corresponding to-be-determined surface is directly attributed to the non-virtual collection position information;

[0030] If it is determined that the coincidence rate between the three-dimensional coordinate point of the to-be-determined surface and the three-dimensional point coordinate of the first position information is equal to 0, then obtaining a first edge contour point set of the corresponding to-be-determined surface;

[0031] Based on the comparison processing of the first edge contour point set of the to-be-determined surface and the second edge contour point set of the associated surface, the non-virtual acquisition position information and the second position information are obtained.

[0032] Optionally, in a possible implementation manner of the first aspect, the obtaining of the non-virtual acquisition position information and the second position information based on the comparison processing of the first edge contour point set of the to-be-determined surface and the second edge contour point set of the associated surface includes:

[0033] Extract all edge points of the associated surface to obtain a second edge contour point set, and if it is determined that the second edge contour point set and the first edge contour point set have adjacent associated edges, use the undetermined surface as the first undetermined surface to which the first label is added;

[0034] If it is determined that there is no adjacent associated edge between the second edge contour point set and the first edge contour point set, the to-be-determined surface is used as the second to-be-determined surface to which the second label is added;

[0035] The coordinate information corresponding to the first to-be-determined surface and the second to-be-determined surface is extracted to obtain the non-virtual acquisition position information, and the coordinate information of the first to-be-determined surface in the non-virtual acquisition position information is extracted to obtain the second position information.

[0036] Optionally, in a possible implementation manner of the first aspect, associating the first position information with the second position information to obtain an association group, and adding a corresponding virtual prediction strategy to the association group includes:

[0037] Determine an associated surface having an associated edge and a first surface to be determined, and associate the first position information of the associated surface with the second position information of the second surface to be determined to obtain an associated group;

[0038] A corresponding virtual prediction strategy is added to the associated group, wherein the virtual prediction strategy includes at least one of a mobile element movement sub-strategy or a power management prediction sub-strategy.

[0039] Optionally, in a possible implementation manner of the first aspect, determining the virtual power grid device of the twin model, performing management prediction of the virtual power grid device based on the virtual acquisition range and the virtual prediction strategy, and obtaining management data includes:

[0040] If it is determined that there are moving elements within the virtual acquisition range, the moving direction of each moving element is obtained to obtain a first moving trajectory of each moving element;

[0041] After determining that the corresponding moving element is out of the virtual acquisition range, a second moving trajectory of the moving element on the first to-be-determined surface is extended based on the first moving trajectory to generate a second moving trajectory of the moving element on the first to-be-determined surface, so as to obtain twin moving information of the uncovered acquisition area in the twin model;

[0042] If it is determined that the first to-be-determined surface is adjacent to other second to-be-determined surfaces, the second motion trajectories of other second to-be-determined surfaces are obtained based on the second motion trajectory prediction of the first to-be-determined surface again;

[0043] If it is determined that there is power grid equipment within the collection range, the relationship between the mobile element and the virtual power grid equipment is obtained to obtain equipment management information, and the twin mobile information and equipment management information are statistically analyzed to obtain management data;

[0044] A management analysis is performed on all first to-be-determined surfaces and second to-be-determined surfaces to determine management recommendation information for the added monitoring terminals.

[0045] Optionally, in a possible implementation manner of the first aspect, the performing management analysis on all first to-be-determined surfaces and second to-be-determined surfaces to determine management recommendation information of the added monitoring terminal includes:

[0046] Obtaining the number of first elements predicted by moving elements of each first to-be-determined surface and second to-be-determined surface within a preset time period;

[0047] Obtaining the levels of the motion trajectories of the mobile elements corresponding to the first to-be-determined surface and the second to-be-determined surface, wherein the levels are the predicted times of the second motion trajectories of the first to-be-determined surface and the second to-be-determined surface, and the higher the predicted times of the second motion trajectories, the higher the level;

[0048] A management recommendation coefficient is obtained based on the weighted calculation of the number and level of the first elements of each first to-be-determined surface and the second to-be-determined surface;

[0049] The first to-be-determined surface and the second to-be-determined surface are sorted in descending order based on the management recommendation coefficient to obtain management recommendation information.

[0050] A second aspect of an embodiment of the present invention provides a digital and intelligent virtual park power grid management platform, including:

[0051] The acquisition module is used to obtain the monitoring data of all monitoring terminals in the digital virtual park and determine the actual location and image acquisition range of each monitoring terminal in the park;

[0052] A determination module, used to determine in the twin model of the digital virtual park based on the actual position and image acquisition range, and obtain the virtual position and virtual acquisition range of each monitoring terminal in the twin model;

[0053] An analysis module, configured to determine the position of each virtual acquisition range of the twin model to obtain first position information, determine the non-virtual acquisition range of the twin model to obtain non-virtual acquisition position information, and analyze the non-virtual acquisition position information based on the first position information to obtain multiple second position information;

[0054] An association module, used for associating the first position information with the second position information to obtain an association group, and adding a corresponding virtual prediction strategy to the association group;

[0055] The prediction module is used to determine the virtual power grid equipment of the twin model, perform management prediction of the virtual power grid equipment based on the virtual acquisition range and virtual prediction strategy, and obtain management data.

[0056] The present invention obtains the monitoring data of all monitoring terminals in the digital virtual park, determines the actual position and image acquisition range of each monitoring terminal in the park, and maps it to the twin model of the virtual park. This process enables the monitoring data to be highly integrated and realizes the virtualization of the functions of the physical monitoring terminals. By calculating the virtual position and virtual acquisition range of each monitoring terminal in the virtual model, managers can grasp the monitoring dynamics in the park in real time, and perform complex spatial analysis and visualization based on the virtual model, which significantly improves the comprehensiveness and accuracy of monitoring.

[0057] The present invention utilizes virtual power grid equipment in the virtual park twin model, and realizes predictive management of power grid equipment through virtual collection scope and virtual prediction strategy. The system can predict and warn of possible changes in power demand and equipment failures, thereby optimizing the allocation of power resources and equipment maintenance time. This greatly improves the intelligence level of energy management and reduces the failure rate and management cost of equipment. At the same time, the system can predict the trajectory of mobile elements in the park, and can effectively monitor even in blind areas of monitoring, thereby improving overall management efficiency.

[0058] In the present invention, by analyzing the virtual and non-virtual collection ranges, the coverage blind spots and potential risk areas in the park can be accurately identified. The system uses the correlation analysis of location information to not only optimize the layout and adjustment strategy of surveillance cameras, but also provide practical suggestions for security management and resource allocation in the park. In addition, by analyzing the equipment and mobile elements inside and outside the collection range, the system can generate management recommendation information to help managers optimize park operations and improve overall safety and efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 It is a flowchart of a digital and intelligent virtual park power grid management method provided by an embodiment of the present invention;

[0060] Figure 2 It is a structural diagram of a digital virtual park power grid management platform provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0061] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0062] See also Figure 1 , is a flow chart of a method for managing a digitalized virtual park power grid provided by an embodiment of the present invention, the method comprising:

[0063] S1, obtain the monitoring data of all monitoring terminals in the digital virtual park, and determine the actual location and image acquisition range of each monitoring terminal in the park.

[0064] The digitalized virtual park can be a twin park corresponding to the target park built using existing technologies. The disclosure needs to obtain relevant information of the monitoring end in the park and then conduct specific analysis. The monitoring end can be a monitoring camera. In the actual park, there will be multiple monitoring cameras cooperating with each other to monitor the park in real time.

[0065] It is understandable that each monitoring terminal has a corresponding actual location and image acquisition range in the park.

[0066] In some embodiments, the acquiring of monitoring data of all monitoring terminals in the digitalized virtual park and determining the actual location and image acquisition range of each monitoring terminal in the park includes:

[0067] S11, obtaining the actual location of each monitoring terminal in the park, each monitoring terminal having a preset actual location.

[0068] First, the present disclosure needs to obtain the actual geographical location of the park where each monitoring terminal is located. These monitoring terminals are pre-set with specific actual locations so that they can be accurately located.

[0069] S12, obtaining a rotation interval of each monitoring terminal, and determining an image acquisition range of the monitoring terminal at different rotation angles based on the rotation interval.

[0070] It is understandable that some cameras can rotate to expand the monitoring angle of view, and the present disclosure needs to obtain the rotation interval information of each monitoring terminal. Based on the rotation interval of the monitoring terminal, the image acquisition range at different rotation angles is calculated and determined. The present disclosure takes into account the rotation interval of the monitoring device and can dynamically adjust and determine its image acquisition range.

[0071] S2, based on the actual position and image acquisition range, determine in the twin model of the digital virtual park to obtain the virtual position and virtual acquisition range of each monitoring terminal in the twin model.

[0072] It can be understood that this step mainly involves determining the virtual position and virtual acquisition range of the monitoring end based on the actual position and image acquisition range in the digital twin model of the digital virtual park, and realizing the mapping of data in the digital virtual park. This step lays an important foundation for the digital monitoring and management of the smart park by virtualizing the functions of the physical monitoring end, enabling managers to better allocate resources and monitor the environment.

[0073] In some embodiments, the actual position and image acquisition range are determined in the twin model of the digital virtual park to obtain the virtual position and virtual acquisition range of each monitoring terminal in the twin model, including:

[0074] S21, receiving the lens rated angle and lens rated focal length configured by the user for each monitoring terminal.

[0075] It is understandable that each monitoring terminal has a corresponding lens rated angle and lens rated focal length. The lens rated angle is, for example, 120°, 150°, etc., and the lens rated focal length is, for example, 3 meters, 10 meters, etc. The lens rated angle and lens rated focal length corresponding to different monitoring terminals may be different.

[0076] S22, based on the rated angle and rated focal length of the lens, determine the effective image acquisition range of each monitoring terminal in a non-rotating state, wherein the effective image acquisition range is less than or equal to the actual acquisition range of the monitoring terminal.

[0077] It is worth mentioning that although the acquisition distance of the monitoring end can be very far, for example, 10 meters, the image may not be clear at 10 meters, and may be clear within 5 meters. Therefore, this solution needs to determine the effective image acquisition range of each monitoring end in the non-rotating state. Among them, the effective image acquisition range refers to the range where a clear image can be obtained, so it is less than or equal to the actual acquisition range of the monitoring end.

[0078] In some embodiments, the calculation based on the rated angle and the rated focal length of the lens to determine the effective image acquisition range of each monitoring terminal in a non-rotating state, wherein the effective image acquisition range is less than or equal to the actual acquisition range of the monitoring terminal, includes:

[0079] S221, taking the virtual position as the starting position, diverging according to the direction of the monitoring terminal and the rated angle of the lens to obtain a preliminary virtual collection range.

[0080] This disclosure uses the virtual position of the monitoring end in the digital twin model as the starting point. Divergence processing is performed according to the orientation of the monitoring end (i.e. the direction the lens points to) and the rated angle of the lens (e.g. 120°). This divergence processing can be understood as starting from the starting position and forming a fan-shaped area according to the rated angle of the lens. This area is the initial virtual acquisition range.

[0081] S222, determining an effective acquisition distance based on the rated focal length of the lens, extending the effective acquisition distance toward the monitoring end, and determining an overlapping area between the extended distance and the preliminary virtual acquisition range as a valid image acquisition range.

[0082] Among them, the rated focal length of the lens is, for example, 10 meters, and the effective acquisition distance is, for example, 5 meters, which can be manually determined. The present disclosure will extend the effective acquisition distance toward the direction (shooting direction) of the monitoring end, and finally determine the overlap area of ​​the extended distance and the preliminary virtual acquisition range as the effective image acquisition range. It can be understood that the original 10-meter fan-shaped area is now a 5-meter fan-shaped area as the effective image acquisition range.

[0083] S23, determine the virtual position of each monitoring terminal in the twin model, and determine the virtual collection range of the monitoring terminal in the twin model at different rotation angles based on the rotation range of each monitoring terminal and the positional relationship of each module in the twin model.

[0084] The present disclosure will integrate all possible acquisition ranges and can dynamically display the visibility of each monitoring terminal at different angles.

[0085] S3, determine the position of each virtual acquisition range of the twin model to obtain first position information, determine the non-virtual acquisition range of the twin model to obtain non-virtual acquisition position information, and analyze the non-virtual acquisition position information based on the first position information to obtain multiple second position information.

[0086] Among them, the virtual collection range is the effective collection range and the clear collection range; the non-virtual collection range is the range that cannot be collected by the monitoring end. In the digital twin model, in order to determine the location of the virtual and non-virtual collection ranges and analyze them to obtain more detailed information, please refer to the following for details.

[0087] In some embodiments, the step of determining the position of each virtual acquisition range of the twin model to obtain first position information, determining the non-virtual acquisition range of the twin model to obtain non-virtual acquisition position information, and analyzing the non-virtual acquisition position information based on the first position information to obtain multiple second position information includes:

[0088] S31, determining the position of each virtual acquisition range of the twin model to obtain first position information, where the first position information is the position where image information can be extracted within the range of rotation allowed by the monitoring end.

[0089] For each monitoring terminal, its specific position within the virtual acquisition range is determined. This involves identifying all positions within the monitoring terminal's allowed rotation range where image information can be extracted, such as a house area, a road area, etc. This position information is called first position information, which represents the area that the monitoring device can cover in the model.

[0090] S32, obtain the three-dimensional coordinate system of the twin model, extract all three-dimensional coordinate points of each twin module of the twin model to obtain a three-dimensional coordinate body set, and extract the three-dimensional coordinate points of each surface of each twin module to obtain a three-dimensional coordinate surface set.

[0091] It is understandable that there will be many twin models in the virtual park, such as houses, roads, chairs, etc. The present disclosure needs to obtain the three-dimensional coordinate system of the twin model, and then extract all the three-dimensional coordinate points of each module from the twin model to generate a three-dimensional coordinate body set. These points represent the spatial position of each module in the model. At the same time, the three-dimensional coordinate points of each face of each twin module are extracted to generate a three-dimensional coordinate face set.

[0092] S33, taking the faces in each three-dimensional coordinate surface set that are perpendicular to the Z axis and close to the positive direction of the Z axis as the faces to be determined, so that all the faces to be determined are the positive surfaces of the corresponding twin modules in the twin model.

[0093] The present disclosure selects the faces that are perpendicular to the Z axis and close to the positive direction of the Z axis, i.e., the upper surfaces, from the three-dimensional coordinate surface set. These faces are selected as the faces to be determined. Taking a house as an example, the faces that are perpendicular to the Z axis and close to the positive direction of the Z axis are the roof faces of the house. The present disclosure will mark them as the positive surfaces of the corresponding twin modules for subsequent analysis.

[0094] S34, obtaining an associated surface based on the surface to be determined and the first position information, and determining a three-dimensional coordinate surface set of other surfaces to be determined corresponding to the associated surface to obtain non-virtual acquisition position information and second position information.

[0095] The present disclosure analyzes the surface to be determined and the first position information to obtain the associated surface. Then, the three-dimensional coordinate surface set of other surfaces to be determined corresponding to the associated surface is found to obtain the non-virtual acquisition position information and the second position information. See below for details.

[0096] The method of obtaining an associated surface based on the surface to be determined and the first position information, and determining a set of three-dimensional coordinate surfaces of other surfaces to be determined corresponding to the associated surface to obtain non-virtual acquisition position information and second position information includes:

[0097] S341: If it is determined that the coincidence rate between the three-dimensional coordinate point of the to-be-determined surface and the three-dimensional coordinate point of the first position information is greater than or equal to a first preset value, the corresponding to-be-determined surface is used as an associated surface.

[0098] The present disclosure determines the positional relationship by judging the overlap rate. First, calculate the overlap rate between the three-dimensional coordinate points of the surface to be determined and the three-dimensional coordinate points of the first position information, where the overlap rate can be the number of overlapping coordinate points. The larger the number of overlaps, the larger the corresponding overlap rate. If the overlap rate is greater than or equal to a preset threshold (the first preset value is, for example, 50%), it means that the surface to be determined can be photographed by the corresponding monitoring end, so the surface to be determined is regarded as an associated surface. Associated surfaces represent areas that are highly overlapped with the first position information, and these areas require special attention in the analysis.

[0099] S342: If it is determined that the coincidence rate between the three-dimensional coordinate points of the to-be-determined surface and the three-dimensional coordinate points of the first position information is greater than 0 and less than a first preset value, the corresponding to-be-determined surface is directly attributed to the non-virtual collection position information.

[0100] Different from the above steps, this step determines whether there is a small overlap, that is, whether it is captured by a small number of people. If the overlap rate is greater than 0 but less than the first preset value, these undetermined faces are directly classified as non-virtual collection position information. Although these faces do not meet the overlap standard of the associated face, they still overlap partially with the first position information.

[0101] S343: If it is determined that the coincidence rate between the three-dimensional coordinate point of the to-be-determined surface and the three-dimensional point coordinate of the first position information is equal to 0, a first edge contour point set of the corresponding to-be-determined surface is obtained.

[0102] If the overlap rate is 0, it means that these to-be-determined faces do not overlap directly with the first position information at all, that is, they cannot be photographed. The present disclosure needs to extract the first edge contour point sets of these faces, which define the boundaries of the faces in three-dimensional space.

[0103] S344, based on the comparison processing of the first edge contour point set of the to-be-determined surface and the second edge contour point set of the associated surface, obtain the non-virtual acquisition position information and the second position information.

[0104] Finally, the present disclosure compares the first edge contour point set of the to-be-determined face with the second edge contour point set of the associated face. By comparing, the non-virtual acquisition position information and the second position information can be adjusted and optimized more accurately, ensuring that the parts that do not directly overlap in the physical model are also effectively analyzed and utilized.

[0105] The non-virtual acquisition position information and the second position information are obtained by comparing the first edge contour point set of the to-be-determined surface and the second edge contour point set of the associated surface, including:

[0106] Extract all edge points of the associated surface to obtain a second edge contour point set. If it is determined that the second edge contour point set and the first edge contour point set have adjacent associated edges, the surface to be determined will be used as the first surface to be determined to add the first label. It can be understood that the present disclosure will determine whether the second edge contour point set and the first edge contour point set have an adjacent relationship, that is, whether there are adjacent associated edges. If so, it means that the two have a connected positional relationship. The present disclosure will use the surface to be determined as the first surface to be determined to add the first label. It is worth mentioning that through the above judgment, the positional relationship between the associated surface and the first surface to be determined of the first label can be obtained. Among them, the method for determining whether there are adjacent associated edges can determine the number of overlapping coordinate points. When the number reaches a preset number, it is considered that there are adjacent associated edges.

[0107] If it is determined that there is no adjacent associated edge between the second edge contour point set and the first edge contour point set, the undetermined surface is used as the second undetermined surface to which the second label is added. Different from the above steps, if there is no adjacent associated edge, indicating that there is no association relationship, the undetermined surface is marked as the second undetermined surface and the second label is added.

[0108] The coordinate information corresponding to the first to-be-determined surface and the second to-be-determined surface is extracted to obtain the non-virtual acquisition position information, and the coordinate information of the first to-be-determined surface in the non-virtual acquisition position information is extracted to obtain the second position information. This solution will extract the coordinate information marked as the first to-be-determined surface and the second to-be-determined surface respectively, and these coordinate information constitute the non-virtual acquisition position information (which cannot be photographed by the monitoring end), and extract the coordinate information of the first to-be-determined surface from the non-virtual acquisition position information. It can be understood that the second position information corresponding to the first to-be-determined surface is connected to the associated surface.

[0109] S4, associating the first position information with the second position information to obtain an associated group, and adding a corresponding virtual prediction strategy to the associated group.

[0110] It can be understood that, since the first position information and the second position information are connected positions, this solution will associate them to obtain an associated group and add a corresponding virtual prediction strategy to the associated group.

[0111] In some embodiments, associating the first location information with the second location information to obtain an associated group, and adding a corresponding virtual prediction strategy to the associated group, includes:

[0112] S41, determining an associated surface having an associated edge and a first surface to be determined, and associating first position information of the associated surface and second position information of the second surface to be determined to obtain an associated group.

[0113] The present disclosure will find the associated surface with the associated edge and the first surface to be determined, and the present disclosure will associate the first position information of the associated surface and the second position information of the second surface to be determined to obtain an associated group for subsequent association analysis.

[0114] S42, and adding a corresponding virtual prediction strategy to the associated group, wherein the virtual prediction strategy includes at least one of a mobile element movement sub-strategy or a power management prediction sub-strategy.

[0115] It is understandable that after determining the associated group, this solution will add a corresponding virtual prediction strategy to it to predict the monitored data. The virtual prediction strategies disclosed in this disclosure include two types, among which the mobile element movement sub-strategy is used to predict or simulate the movement behavior of the mobile element, which is suitable for scenarios that require monitoring of the movement path or position adjustment. The power management prediction sub-strategy is used to optimize the management of power equipment.

[0116] S5, determining the virtual power grid equipment of the twin model, performing management prediction of the virtual power grid equipment based on the virtual acquisition range and the virtual prediction strategy, and obtaining management data.

[0117] In some embodiments, the determining of the virtual power grid device of the twin model, performing management prediction of the virtual power grid device based on the virtual acquisition range and the virtual prediction strategy to obtain management data includes:

[0118] S51, if it is determined that there are moving elements within the virtual acquisition range, the moving direction of each moving element is obtained to obtain a first moving trajectory of each moving element.

[0119] The moving element is, for example, a person in the picture. This solution can obtain the moving direction of each person in the virtual acquisition range, thereby obtaining the first moving trajectory of each moving element. The first moving trajectory is, for example, from east to west.

[0120] S52, after determining that the corresponding mobile element is out of the virtual acquisition range, a second motion trajectory of the mobile element is extended on the first surface to be determined based on the first motion trajectory and generated to obtain twin movement information of the uncovered acquisition area in the twin model.

[0121] It is understandable that the monitoring terminals in the park will have certain blind spots. After the corresponding mobile elements leave the virtual collection range, the monitoring terminals of this solution can no longer collect the corresponding mobile elements, that is, they are in the monitoring blind spot. In order to perform data analysis on the blind spot range of the mobile elements, it is necessary to combine the virtual prediction strategy to predict the trajectory at this time.

[0122] First, the present disclosure will combine the first motion trajectory to extend the first surface to be determined and generate a second motion trajectory of the moving element on the first surface to be determined. For example, the first motion trajectory of character A is from east to west, then the present solution will extend the first motion trajectory to generate the second motion trajectory of the first motion trajectory on the first surface to be determined. In practical applications, the first motion trajectory may not be a straight line. In order to perform a predicted extension, the present solution can determine the starting point and end point of the first motion trajectory, and then connect the starting point to the end point to form a straight line, and then extend the straight line to obtain the second motion trajectory of the first surface to be determined. It can be understood that the second motion trajectory of the first surface to be determined can also be in the form of a straight line. Through the above method, the present disclosure can obtain the twin movement information of the uncovered acquisition area in the twin model.

[0123] S53: If it is determined that the first surface to be determined is adjacent to other second surfaces to be determined, the second motion trajectories of other second surfaces to be determined are obtained again based on the second motion trajectory prediction of the first surface to be determined.

[0124] If the first surface to be determined is adjacent to other second surfaces to be determined, it means that movement prediction can still be performed. The present disclosure can again predict the second motion trajectory of the first surface to be determined to obtain the second motion trajectory of other second surfaces to be determined.

[0125] S54, if it is determined that there is power grid equipment within the collection range, the relationship between the mobile element and the virtual power grid equipment is obtained to obtain equipment management information, and the twin mobile information and equipment management information are statistically analyzed to obtain management data.

[0126] If it is determined that there are power grid devices in the collection range, such as transformers, sensors, etc., the present disclosure will obtain the relationship between the mobile element and the virtual power grid device to obtain device management information. The device management information may be that no one is allowed to approach the transformer, etc. The present disclosure will count the twin mobile information and device management information to obtain management data. Exemplarily, the device management information may be that no one is allowed to approach the transformer. However, the twin mobile information indicates that someone is approaching the transformer, and an early warning is required to obtain management data.

[0127] S55, performing management analysis on all first to-be-determined surfaces and second to-be-determined surfaces to determine management recommendation information of the added monitoring terminal.

[0128] In order to perform optimization, the present invention can generate effective management recommendation information through management analysis of virtual power grid equipment in a digital twin model to optimize monitoring strategies and resource allocation.

[0129] The performing management analysis on all first to-be-determined surfaces and second to-be-determined surfaces to determine management recommendation information of the added monitoring terminal includes:

[0130] S551, obtaining the number of first elements predicted by moving elements of each first to-be-determined surface and second to-be-determined surface within a preset time period.

[0131] It is understandable that the data within the first to-be-determined surface and the second to-be-determined surface need to be predicted. The more first elements predicted by the mobile elements, the larger the corresponding amount of predicted data, which indicates that the prediction demand is large, and indirectly indicates that the demand for the monitoring end is greater.

[0132] S552, obtaining the levels of the motion trajectories of the mobile elements corresponding to the first to-be-determined surface and the second to-be-determined surface, wherein the levels are the predicted times of the second motion trajectories of the first to-be-determined surface and the second to-be-determined surface, and the higher the predicted times of the second motion trajectories, the higher the level.

[0133] It is understandable that, since the second motion trajectory is predicted, the more times it is predicted, the more the data deviates from the truth. Therefore, the present disclosure will set its level higher, which indirectly indicates that the demand for the monitoring end is greater.

[0134] S553: ​​Perform weighted calculation based on the quantity and level of the first elements of each first to-be-determined surface and the second to-be-determined surface to obtain a management recommendation coefficient.

[0135] This solution will weight the number and level of the first elements of each first to-be-determined surface and the second to-be-determined surface to obtain a corresponding management recommendation coefficient. It can be understood that the larger the management recommendation coefficient, the greater the demand for the monitoring end.

[0136] S554: Sort the first to-be-determined surface and the second to-be-determined surface in descending order based on the management recommendation coefficient to obtain management recommendation information.

[0137] Finally, this solution will sort all the first to-be-determined surfaces and the second to-be-determined surfaces in descending order based on the calculated management recommendation coefficients, and prioritize the areas with higher management recommendation coefficients and configure corresponding monitoring terminals for them.

[0138] See also Figure 2 , is a schematic diagram of a digital and intelligent virtual park power grid management platform provided by an embodiment of the present invention, including:

[0139] The acquisition module is used to obtain the monitoring data of all monitoring terminals in the digital virtual park and determine the actual location and image acquisition range of each monitoring terminal in the park;

[0140] A determination module, used to determine in the twin model of the digital virtual park based on the actual position and image acquisition range, and obtain the virtual position and virtual acquisition range of each monitoring terminal in the twin model;

[0141] An analysis module, configured to determine the position of each virtual acquisition range of the twin model to obtain first position information, determine the non-virtual acquisition range of the twin model to obtain non-virtual acquisition position information, and analyze the non-virtual acquisition position information based on the first position information to obtain multiple second position information;

[0142] An association module, used for associating the first position information with the second position information to obtain an association group, and adding a corresponding virtual prediction strategy to the association group;

[0143] The prediction module is used to determine the virtual power grid equipment of the twin model, perform management prediction of the virtual power grid equipment based on the virtual acquisition range and virtual prediction strategy, and obtain management data.

[0144] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A digital and intelligent virtual park power grid management method, characterized in that: include: Obtain monitoring data from all monitoring terminals in the digital virtual park and determine the actual location and image acquisition range of each monitoring terminal in the park; Based on the actual position and image acquisition range, the virtual position and virtual acquisition range of each monitoring terminal in the twin model are determined in the digital virtual park; Determine the position of each virtual acquisition range of the twin model to obtain first position information, determine the non-virtual acquisition range of the twin model to obtain non-virtual acquisition position information, and analyze the non-virtual acquisition position information based on the first position information to obtain multiple second position information; Determine the position of each virtual acquisition range of the twin model to obtain first position information, where the first position information is a position where image information can be extracted within a range of rotation allowed by the monitoring end; The faces in each set of three-dimensional coordinate faces that are perpendicular to the Z axis and close to the positive direction of the Z axis are taken as faces to be determined, so that all faces to be determined are positive surfaces of the corresponding twin modules in the twin model; Based on the to-be-determined surface and the first position information, an associated surface is obtained, and a set of three-dimensional coordinate surfaces of other to-be-determined surfaces corresponding to the associated surface is determined to obtain non-virtual acquisition position information and second position information; If it is determined that the coincidence rate between the three-dimensional coordinate point of the to-be-determined surface and the three-dimensional coordinate point of the first position information is greater than or equal to a first preset value, the corresponding to-be-determined surface is used as an associated surface; If it is determined that the coincidence rate between the three-dimensional coordinate point of the to-be-determined surface and the three-dimensional point coordinates of the first position information is greater than 0 and less than a first preset value, the corresponding to-be-determined surface is directly attributed to the non-virtual collection position information; If it is determined that the coincidence rate between the three-dimensional coordinate point of the to-be-determined surface and the three-dimensional point coordinate of the first position information is equal to 0, then obtaining a first edge contour point set of the corresponding to-be-determined surface; Based on the comparison processing of the first edge contour point set of the to-be-determined surface and the second edge contour point set of the associated surface, the non-virtual acquisition position information and the second position information are obtained; Extract all edge points of the associated surface to obtain a second edge contour point set, and if it is determined that the second edge contour point set and the first edge contour point set have adjacent associated edges, use the undetermined surface as the first undetermined surface to which the first label is added; If it is determined that there is no adjacent associated edge between the second edge contour point set and the first edge contour point set, the to-be-determined surface is used as the second to-be-determined surface to which the second label is added; Extracting coordinate information corresponding to the first to-be-determined surface and the second to-be-determined surface respectively to obtain non-virtual acquisition position information, and extracting coordinate information of the first to-be-determined surface in the non-virtual acquisition position information to obtain second position information; Associating the first position information with the second position information to obtain an associated group, and adding a corresponding virtual prediction strategy to the associated group; Determine the virtual power grid equipment of the twin model, perform management prediction of the virtual power grid equipment based on the virtual acquisition range and the virtual prediction strategy, and obtain management data; If it is determined that there are moving elements within the virtual acquisition range, the moving direction of each moving element is obtained to obtain a first moving trajectory of each moving element; After determining that the corresponding moving element is out of the virtual acquisition range, a second moving trajectory of the moving element on the first to-be-determined surface is extended based on the first moving trajectory to generate a second moving trajectory of the moving element on the first to-be-determined surface, so as to obtain twin moving information of the uncovered acquisition area in the twin model; If it is determined that the first to-be-determined surface is adjacent to other second to-be-determined surfaces, the second motion trajectories of other second to-be-determined surfaces are obtained based on the second motion trajectory prediction of the first to-be-determined surface again; Obtaining the number of first elements predicted by moving elements of each first to-be-determined surface and second to-be-determined surface within a preset time period; Obtaining the levels of the motion trajectories of the mobile elements corresponding to the first to-be-determined surface and the second to-be-determined surface, wherein the levels are the predicted times of the second motion trajectories of the first to-be-determined surface and the second to-be-determined surface, and the higher the predicted times of the second motion trajectories, the higher the level; A management recommendation coefficient is obtained based on the weighted calculation of the number and level of the first elements of each first to-be-determined surface and the second to-be-determined surface; The first to-be-determined surface and the second to-be-determined surface are sorted in descending order based on the management recommendation coefficient to obtain management recommendation information.

2. The digital and intelligent virtual park power grid management method according to claim 1 is characterized in that: The acquisition of monitoring data of all monitoring terminals in the digitalized virtual park and determination of the actual location and image acquisition range of each monitoring terminal in the park include: Obtain the actual location of each monitoring terminal in the park, each monitoring terminal has a preset actual location; The rotation interval of each monitoring terminal is obtained, and the image acquisition range of the monitoring terminal at different rotation angles is determined based on the rotation interval.

3. The digital and intelligent virtual park power grid management method according to claim 1 is characterized in that: The actual position and image acquisition range are determined in the twin model of the digital virtual park to obtain the virtual position and virtual acquisition range of each monitoring terminal in the twin model, including: Receive the lens rated angle and lens rated focal length configured by the user for each monitoring terminal; Based on the lens rated angle and lens rated focal length, determine the effective image acquisition range of each monitoring terminal in a non-rotating state, wherein the effective image acquisition range is less than or equal to the actual acquisition range of the monitoring terminal; Determine the virtual position of each monitoring end in the twin model, and determine the virtual collection range of the monitoring end in the twin model at different rotation angles based on the rotation range of each monitoring end and the positional relationship of each module in the twin model.

4. The digital and intelligent virtual park power grid management method according to claim 3 is characterized in that: The calculation based on the rated angle and the rated focal length of the lens to determine the effective image acquisition range of each monitoring terminal in a non-rotating state, wherein the effective image acquisition range is less than or equal to the actual acquisition range of the monitoring terminal, includes: Taking the virtual position as the starting position, diverging according to the direction of the monitoring terminal and the rated angle of the lens to obtain a preliminary virtual collection range; The effective acquisition distance is determined based on the rated focal length of the lens, and the effective acquisition distance is extended toward the monitoring end to determine the overlapping area of ​​the extended distance and the preliminary virtual acquisition range as the effective image acquisition range.

5. The digital and intelligent virtual park power grid management method according to claim 1 is characterized in that: The method of determining the position of each virtual acquisition range of the twin model to obtain first position information, determining the non-virtual acquisition range of the twin model to obtain non-virtual acquisition position information, and analyzing the non-virtual acquisition position information based on the first position information to obtain multiple second position information includes: Obtain the three-dimensional coordinate system of the twin model, extract all three-dimensional coordinate points of each twin module of the twin model to obtain a three-dimensional coordinate body set, and extract the three-dimensional coordinate points of each surface of each twin module to obtain a three-dimensional coordinate surface set.

6. The digital and intelligent virtual park power grid management method according to claim 1 is characterized in that: The step of associating the first position information with the second position information to obtain an associated group, and adding a corresponding virtual prediction strategy to the associated group includes: Determine an associated surface having an associated edge and a first surface to be determined, and associate the first position information of the associated surface with the second position information of the first surface to be determined to obtain an associated group; A corresponding virtual prediction strategy is added to the associated group, wherein the virtual prediction strategy includes at least one of a mobile element movement sub-strategy or a power management prediction sub-strategy.

7. The digital and intelligent virtual park power grid management method according to claim 6 is characterized in that: The determining of the virtual power grid device of the twin model, performing management prediction of the virtual power grid device based on the virtual acquisition range and the virtual prediction strategy, and obtaining management data includes: If it is determined that there is power grid equipment within the collection range, the relationship between the mobile element and the virtual power grid equipment is obtained to obtain equipment management information, and the twin mobile information and equipment management information are statistically analyzed to obtain management data; A management analysis is performed on all first to-be-determined surfaces and second to-be-determined surfaces to determine management recommendation information for the added monitoring terminals.

8. A digital and intelligent virtual park power grid management platform based on the digital and intelligent virtual park power grid management method according to any one of claims 1 to 7, characterized in that: include: The acquisition module is used to obtain the monitoring data of all monitoring terminals in the digital virtual park and determine the actual location and image acquisition range of each monitoring terminal in the park; A determination module, used to determine in the twin model of the digital virtual park based on the actual position and image acquisition range, and obtain the virtual position and virtual acquisition range of each monitoring terminal in the twin model; An analysis module, configured to determine the position of each virtual acquisition range of the twin model to obtain first position information, determine the non-virtual acquisition range of the twin model to obtain non-virtual acquisition position information, and analyze the non-virtual acquisition position information based on the first position information to obtain multiple second position information; An association module, used for associating the first position information with the second position information to obtain an association group, and adding a corresponding virtual prediction strategy to the association group; The prediction module is used to determine the virtual power grid equipment of the twin model, perform management prediction of the virtual power grid equipment based on the virtual acquisition range and virtual prediction strategy, and obtain management data.

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