AR Technology-based Holographic Data Visualization Intelligent Display and Analysis System for Distribution Network

Abstract

The present invention belongs to the technical field of power grid analysis. The present invention provides an intelligent display and analysis system for holographic data visualization of a distribution network based on AR technology, including: integrating a three-dimensional model of the power grid structure into the AR platform by using an AR development platform and tools to realize real-time display of holographic data of the power grid, performing withdrawal simulation of each node of the power grid in a simulation operation software, and conducting analysis to obtain a node impact evaluation value, judging the criticality of the power grid nodes, constructing reasonable resource indicators for the power grid nodes, screening out load power grid nodes, idle power grid nodes and stable power grid nodes at the end of the power grid operation, generating a sorting table of load power grid nodes and a sorting table of idle power grid nodes, and optimizing the power grid resource allocation based on the sorting table of load power grid nodes and the sorting table of idle power grid nodes. While the present invention displays multiple information of the power grid, it realizes the recommendation of optimized information for power grid resource allocation.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grid analysis, and specifically relates to a holographic data visualization intelligent display and analysis system for distribution networks based on AR technology. Background Art

[0002] With the rapid development of smart grids, the complexity and data volume of distribution networks have increased sharply. In the prior art, although some systems have achieved visual display of data, the displayed information is intuitive and simple. At the same time as the information display, the display and recommendation of its diversified analysis results are lacking. Therefore, it is particularly important to develop a system that can integrate AR technology to achieve holographic data visualization intelligent display and analysis of distribution networks.

[0003] A Chinese patent application with the publication number CN115048591A discloses an intelligent display and analysis system for holographic data of distribution networks based on artificial intelligence, including: by setting a monitoring period, then analyzing the line power supply status corresponding to the target distribution network in the historical monitoring period, and obtaining the operation abnormal information of each power equipment on the target distribution network line in the historical monitoring period. Then, comparing the operation abnormal information of each power equipment in the historical monitoring period with the line power supply status health index of the target distribution network in the historical monitoring period, analyzing the correlation between the operation abnormal information of the power equipment and the line power supply status health index of the target distribution network, and then predicting the power equipment that may have operation abnormalities based on this.

[0004] In the above prior art, by setting a monitoring period and analyzing the line power supply status of the distribution network within the monitoring period, analyzing the correlation between the operation abnormality of its power equipment and the power supply status health index, so as to predict the power equipment abnormality and display the results. First, the above prior art has insufficient display of various information such as its distribution network. Second, there is a lack of optimization solutions after predicting the power equipment abnormality, and no solutions can be found from its correlation aspect. Finally, there is a lack of analysis through the displayed information, so that the staff can intuitively see the information changes and also see the effects brought by the information changes.

[0005] Therefore, the present invention provides a holographic data visualization intelligent display and analysis system for distribution networks based on AR technology. Summary of the Invention

[0006] In order to make up for the deficiencies of the prior art and solve at least one technical problem proposed in the background art.

[0007] The technical solution adopted by the present invention to solve its technical problems is: a holographic data visualization intelligent display and analysis system for distribution networks based on AR technology, including:

[0008] Model construction module: Obtain the topological structure of the power grid, construct a 3D model of the power grid structure based on the topological structure of the power grid, and integrate its 3D model into the AR platform using the AR development platform and tools to realize the real-time display of the holographic data of the power grid;

[0009] Simulation evaluation module: Import the 3D model of the power grid structure into the simulation operation software, set the initial operation parameters of the power grid in the simulation operation software, conduct the simulation of the withdrawal of each node of the power grid, record the operation change data of the power grid after the node withdrawal, analyze the operation change data to obtain the node impact evaluation value, judge the criticality of the power grid nodes through the node impact evaluation value, number each node of the power grid based on the node impact evaluation value, and integrate its number information into the AR platform for display in the holographic data of the power grid;

[0010] Resource index establishment module: Construct a reasonable resource index for the power grid nodes based on the node impact evaluation value of the power grid nodes and the historical resource allocation data, and integrate the established reasonable resource index information into the holographic data of the AR platform for display;

[0011] Resource utilization monitoring module: Obtain the resource utilization data of the power grid during the operation cycle, based on the resource utilization data and the reasonable resource index of the power grid nodes, screen out the load power grid nodes, idle power grid nodes and stable power grid nodes at the end of the power grid operation, and integrate the number information of the load power grid nodes and the idle power grid nodes into the holographic data of the AR system for display;

[0012] Resource optimization recommendation module: Generate a sorting table of load power grid nodes and a sorting table of idle power grid nodes based on the load power grid nodes and the idle power grid nodes, and integrate and display them in the holographic data of the AR platform. Optimize the power grid resource allocation based on the sorting table of load power grid nodes and the sorting table of idle power grid nodes.

[0013] As a further technical solution of the present invention: The operation change data includes the type of operation parameter change and the amount of operation parameter change;

[0014] Among them, the acquisition method of the type of operation parameter change is:

[0015] If the operation parameters of the power grid are not equal to the initial operation parameters of the power grid during the withdrawal duration, mark the type of operation parameter as the type of operation parameter change;

[0016] The acquisition method of the amount of operation parameter change is:

[0017] Obtain the maximum value of the operation parameter corresponding to the type of operation parameter change during the withdrawal duration, perform a difference process on the maximum value and the initial operation parameter of the same type of power grid, and take the absolute value of the difference to obtain the amount of operation parameter change.

[0018] As a further technical solution of the present invention: the method for obtaining the node influence evaluation value DYy is as follows:

[0019] Perform data processing on the grid operation parameter change degree value LX, the grid operation quantity change degree YX, and the grid operation time change degree value ZX, and obtain the node influence evaluation value DYy through the formula: DYy = s1*LX + s2*YX + s3*ZX, where s1, s2, and s3 are all preset proportionality coefficients;

[0020] Among them, the method for obtaining the grid operation parameter change degree value LX is: count the number of types of operation parameter changes, and perform a ratio process on the number of types of operation parameter changes and the number of types of initial grid operation parameters to obtain the grid operation parameter change degree value LX.

[0021] As a further technical solution of the present invention: the method for obtaining the grid operation quantity change degree value YX is as follows:

[0022] Perform a ratio process on the grid operation parameter change amount and the corresponding initial operation parameter of the same type to obtain the change degree of the operation parameter, obtain the change degrees of the operation parameters of different change types, and sum and average them to obtain the grid operation quantity change degree value YX;

[0023] The method for obtaining the grid operation time change degree value ZX is as follows:

[0024] Obtain the change time of the grid operation parameters of different change types within the exit duration, sum and average them to obtain the average change time of the grid operation parameters of different change types within the exit duration, and perform a ratio process on it and the exit duration to obtain the grid operation time change degree value ZX.

[0025] As a further technical solution of the present invention: the method for obtaining the reasonable resource index of the grid node is as follows:

[0026] Perform a product process on the node influence evaluation value DYy of the grid node and the average distribution ratio of the grid resource capacity to obtain the distribution performance value of the grid node, sum the distribution performance values of all grid nodes to obtain the total distribution performance value of all grid nodes, and mark it as FBz;

[0027] Obtain the total distribution capacity of the current grid resources and mark it as ZF;

[0028] Through the formula: Obtain the reasonable resource index ZBx of the grid node.

[0029] As a further technical solution of the present invention: the method for obtaining the average distribution ratio of the grid resource capacity is as follows: obtain the historical resource allocation data of the grid nodes in multiple historical allocation cycles, where the historical resource allocation data includes the grid resource allocation capacity allocated to the grid nodes in the historical allocation cycle, and perform a ratio process on the grid resource allocation capacity allocated to the grid nodes in the historical cycle and the total grid resource allocation capacity in the corresponding historical cycle to obtain the grid resource capacity allocation ratio of the grid nodes in the historical cycle. Obtain the grid resource capacity allocation ratios of the grid nodes in multiple historical cycles, sum them up and take the average to obtain the average grid resource capacity allocation ratio of the grid nodes in the historical cycle, and mark it as FB.

[0030] As a further technical solution of the present invention: the screening method for the load grid is as follows: sum up all the actual load amounts of the grid nodes in the operation cycle and take the average to obtain the average load amount of the grid nodes in the operation cycle, and compare the average load amount of the grid nodes in the operation cycle with the reasonable resource index of the corresponding grid nodes:

[0031] If the average load amount is greater than or equal to the reasonable resource index of the grid node, mark the grid node as a load grid node;

[0032] If the average load amount is less than the reasonable resource index of the grid node, mark the grid node as a non-load grid node.

[0033] As a further technical solution of the present invention: the screening method for the idle grid nodes and the balanced grid nodes is as follows:

[0034] Among the non-load grid nodes, perform a difference process on the average load amount of the non-load grid nodes and the reasonable resource index of the grid nodes, and take the absolute value of the difference to obtain the remaining load amount. Perform a ratio process on the remaining load amount and the reasonable resource index of the grid nodes to obtain the remaining load ratio, and mark it as FH;

[0035] Obtain the actual load amount of the non-load grid nodes in the operation cycle, and mark it in the X-Y coordinate system to obtain the load amount change curve. Obtain the actual load amounts of all the peak points of the load amount change curve, sum them up and take the average to obtain the average maximum actual load amount of the non-load grid nodes in the operation cycle. Perform a difference process on the average maximum actual load amount and the reasonable resource index of the non-load grid nodes, and take the absolute value of the difference to obtain the minimum remaining load amount. Perform a ratio process on the minimum remaining load amount and the reasonable resource index of the non-load grid nodes to obtain the minimum remaining load ratio, and mark it as ZF;

[0036] Perform data processing on the remaining load ratio FH of the non-load power grid nodes and the minimum remaining load ratio ZF, and obtain the load evaluation value JK through the formula: JK = a1 * ZF + a2 * FH, where a1 and a2 are both preset proportionality coefficients;

[0037] Compare the load evaluation value JK with the load evaluation threshold:

[0038] If the load evaluation value JK is greater than or equal to the load evaluation threshold, mark its non-load power grid node as an idle power grid node;

[0039] If the load evaluation value JK is less than the load evaluation threshold, mark its non-load power grid node as a balanced power grid node.

[0040] As a further technical solution of the present invention: the generation method of the load power grid node sorting table is as follows:

[0041] Perform difference processing on the average load of the load power grid nodes and the corresponding reasonable resource index, and perform ratio processing on the difference and the reasonable resource index to obtain the resource overrun ratio. Sort the load power grid nodes from largest to smallest according to the resource overrun ratio to obtain the load power grid node sorting table.

[0042] As a further technical solution of the present invention: the generation method of the idle power grid node sorting table is as follows: Sum up the remaining load of all idle power grid nodes to obtain the total remaining load. Perform ratio processing on the remaining load of the idle power grid node and the total remaining load to obtain the remaining load capacity performance value of the idle power grid node, and mark it as BXD;

[0043] Sum up the remaining load capacity performance value BXD of the idle power grid node and the load evaluation value JK to obtain the allocation priority value FY;

[0044] Sort the idle power grid nodes from largest to smallest according to the allocation priority value FY to obtain the idle power grid node sorting table.

[0045] The beneficial effects of the present invention are as follows: Obtain the topological structure of the power grid, construct a three-dimensional model of the power grid structure based on the topological structure of the power grid, and integrate its three-dimensional model into the AR platform using the AR development platform and tools to achieve real-time display of the holographic data of the power grid. Import the three-dimensional model of the power grid structure into the simulation operation software, and set the initial operation parameters of the power grid in the simulation operation software. After the initial operation parameters of the power grid are set, conduct an exit simulation of each node of the power grid. Based on the exit simulation of each node of the power grid, record the operation change data of the power grid after the node exits. Based on the analysis of the operation change data, obtain the node impact evaluation value. Judge the criticality of the power grid nodes through the node impact evaluation value, number each node of the power grid based on the node impact evaluation value, and integrate its number information into the AR platform. Construct a reasonable resource index for the power grid nodes based on the node impact evaluation value of the power grid nodes and the historical resource allocation data, and integrate the established reasonable resource index information into the holographic data of the AR platform for display. Obtain the resource utilization data of the power grid during the operation cycle. Based on the resource utilization data and the reasonable resource index of the power grid nodes, screen out the load power grid nodes, idle power grid nodes, and stable power grid nodes at the end of the power grid operation, and integrate the number information of the load power grid nodes and the idle power grid nodes into the holographic data of the AR system for display. Based on the load power grid nodes and the idle power grid nodes, generate a sorting table of the load power grid nodes and a sorting table of the idle power grid nodes, and integrate and display them in the holographic data of the AR platform. Based on the sorting table of the load power grid nodes and the sorting table of the idle power grid nodes, optimize the power grid resource allocation. The present invention constructs a three-dimensional model of the power grid based on the topological structure of the power grid and combines AR technology for display, and combines the exit simulation analysis of the power grid nodes to identify and three-dimensionally display the key nodes of the power grid. At the same time, establish a resource allocation index based on the criticality of the power grid nodes, and combine the actual load of the power grid nodes to analyze and optimize the resource allocation of the power grid nodes. While displaying various types of information of the power grid, realize the recommendation of the optimized information of the power grid resources. Description of the Drawings

[0046] The present invention will be further described below with reference to the accompanying drawings.

[0047] Figure 1 is the system module diagram of Embodiment 1 of the present invention;

[0048] Figure 2 is the screening flow chart of the idle power grid nodes and the balanced power grid nodes in Embodiment 1 of the present invention. Detailed Embodiments

[0049] In order to make the technical means, creative features, achieved purposes, and effects of the present invention easy to understand, the present invention will be further described below in conjunction with specific embodiments.

[0050] Embodiment 1: As Figure 1 shown, the holographic data visualization intelligent display and analysis system for distribution network based on AR technology described in the embodiments of the present invention includes:

[0051] Model construction module: Obtain the topological structure of the power grid, construct a three-dimensional model of the power grid structure based on the topological structure of the power grid, and integrate its three-dimensional model into the AR platform by using the AR development platform and tools to realize the real-time display of the holographic data of the power grid;

[0052] It should be noted that the AR development platform and tools include but are not limited to Unity, Unreal Engine, etc.;

[0053] Simulation and evaluation module: Import the three-dimensional model of the power grid structure into the simulation operation software, and set the initial operation parameters of the power grid in the simulation operation software. After the initial operation parameters of the power grid are set, perform the withdrawal simulation of each node of the power grid. Based on the withdrawal simulation of each node of the power grid, record the operation change data of the power grid after the node withdrawal. Based on the analysis of the operation change data, obtain the node impact evaluation value, judge the criticality of the power grid nodes through the node impact evaluation value, number each node of the power grid based on the node impact evaluation value, and integrate its number information into the AR platform for display in the holographic data of the power grid;

[0054] It should be noted that the simulation operation software includes but is not limited to PSSPE, PSCAD, PowerFactory, etc.;

[0055] Specifically, the setting method of the initial operation parameters of the power grid is:

[0056] Obtain the operation parameters of the power grid in multiple historical cycles. Among them, the operation parameters include but are not limited to parameters such as voltage, current, and load. Sum and average the same type of operation parameters in multiple historical cycles of the power grid to obtain the initial operation parameters of the power grid, and set them in the PowerFactory simulation operation software;

[0057] In some embodiments, each node of the power grid is withdrawn sequentially during the power grid simulation operation, and the withdrawal duration is set. Among them, the withdrawal duration includes but is not limited to 20 min, 30 min, and 50 min. During the withdrawal duration, record the operation change data of the power grid. Among them, the operation change data includes the type of operation parameter change and the amount of operation parameter change;

[0058] Among them, the acquisition method of the type of operation parameter change is:

[0059] If the operation parameters of the power grid are not equal to the initial operation parameters of the power grid during the withdrawal duration, mark the type of its operation parameters as the type of operation parameter change;

[0060] If the grid operation parameters continuously remain equal to the initial grid operation parameters within the exit duration, no operation is performed;

[0061] The method for obtaining the change amount of the operation parameters is as follows:

[0062] Obtain the maximum value of the operation parameters corresponding to the operation parameter change type within the exit duration, perform a difference operation between the maximum value and the initial operation parameters of the same type of grid, and take the absolute value of the difference to obtain the change amount of the operation parameters;

[0063] Count the number of operation parameter change types, perform a ratio operation between the number of operation parameter change types and the number of initial operation parameter types of the grid to obtain the grid operation parameter change degree value, and mark it as LX;

[0064] Perform a ratio operation between the grid operation parameter change amount and the corresponding initial operation parameters of the same type to obtain the change degree of the operation parameters, obtain the change degrees of the operation parameters of different change types, and sum and average them to obtain the grid operation amount change degree value, and mark it as YX;

[0065] Obtain the change time of the grid operation parameters of different change types within the exit duration, sum and average them to obtain the average change time of the grid operation parameters of different change types within the exit duration, and perform a ratio operation between the average change time and the exit duration to obtain the grid operation time change degree value, and mark it as ZX;

[0066] Perform data processing on the grid operation parameter change degree value LX, the grid operation amount change degree YX, and the grid operation time change degree value ZX. Through the formula: DYy = s1 * LX + s2 * YX + s3 * ZX, obtain the node influence evaluation value DYy, where s1, s2, and s3 are all preset proportionality coefficients;

[0067] It should be noted that those skilled in the art collect multiple groups of sample data and set corresponding preset proportionality coefficients for each group of sample data; substitute the set preset proportionality coefficients and the collected sample data into the formula. Any three formulas form a system of ternary linear equations, screen and average the calculated coefficients to obtain s1, s2, and s3;

[0068] It should also be noted that the purpose of obtaining the node impact evaluation value DYy is as follows: The node impact evaluation value DYy reflects the change in the overall operation response of the power grid after the withdrawal simulation of the power grid node. Among them, the larger the node impact evaluation value DYy, the greater the overall operation response degree of the power grid, the longer and more complex the time, which means the greater the impact of the withdrawn power grid node on its overall power grid. On the contrary, the smaller the node impact evaluation value DYy, the smaller the overall operation response degree of the power grid, the shorter and simpler the time, which means the smaller the impact of the withdrawn power grid node on its overall power grid;

[0069] In some embodiments, the node impact evaluation value DYy is compared with the node impact evaluation threshold. The specific comparison process is as follows:

[0070] The preset node impact evaluation threshold is DYg;

[0071] If DYy is less than or equal to DYg, it means that the power grid node corresponding to DYy has a small impact on its power grid, and the key degree of its power grid node is low;

[0072] If DYy is greater than DYg, it means that the power grid node corresponding to DYy has a large impact on its power grid, and the key degree of its power grid node is high;

[0073] The power grid nodes are numbered in descending order according to the node impact evaluation value DYy, and the numbering information is integrated into the AR platform and displayed in its holographic data.

[0074] Embodiment 2: As Figure 1 shown, the power distribution network holographic data visualization intelligent display and analysis system based on the AR technology described in the embodiments of the present invention includes:

[0075] Resource index establishment module: Based on the node impact evaluation value of the power grid node and the historical resource allocation data, establish a reasonable resource index for the power grid node, and integrate the established reasonable resource index information into the holographic data of the AR platform for display;

[0076] Among them, the reasonable resource index of the power grid node represents the resource capacity that should be reasonably allocated to the power grid node;

[0077] Specifically, obtain the historical resource allocation data of the power grid nodes in multiple historical allocation cycles. Among them, the historical resource allocation data includes the power grid resource allocation capacity allocated to the power grid nodes in the historical allocation cycle. Perform a ratio process on the power grid resource allocation capacity allocated to the power grid nodes in the historical cycle and the total power grid resource allocation capacity in the corresponding historical cycle to obtain the power grid resource capacity allocation ratio of the power grid nodes in the historical cycle. Obtain the power grid resource capacity allocation ratios of the power grid nodes in multiple historical cycles, sum them up and take the average to obtain the average power grid resource capacity allocation ratio of the power grid nodes in the historical cycle, and mark it as FB;

[0078] It should be noted that the historical allocation cycle represents the duration during which the power grid nodes keep the allocated power grid resource capacity unchanged according to the allocation requirements within a certain period of time;

[0079] Perform a product process on the node impact evaluation value DYy of the power grid node and the average power grid resource capacity allocation ratio to obtain the allocation performance value of the power grid node. Sum up the allocation performance values of all power grid nodes to obtain the total allocation performance value of all power grid nodes, and mark it as FBz;

[0080] Obtain the total allocation capacity of the current power grid resources and mark it as ZF;

[0081] Through the formula: Obtain the reasonable resource index ZBx of the power grid node;

[0082] Integrate the reasonable resource index ZBx of the power grid node into the holographic data of the AR platform for display. Among them, the reasonable resource index ZBx of the power grid node is displayed at the corresponding power grid node;

[0083] Resource utilization monitoring module: Obtain the resource utilization data of the power grid during the operation cycle. Based on the resource utilization data and the reasonable resource index of the power grid node, filter out the load power grid nodes, idle power grid nodes and stable power grid nodes at the end of the power grid operation, and integrate the number information of the load power grid nodes and idle power grid nodes into the holographic data of the AR system for display;

[0084] Among them, the resource utilization data includes the actual load of the power grid node;

[0085] As Figure 2 shown, in some embodiments, sum up all the actual loads of the power grid node during the operation cycle and take the average to obtain the average load of the power grid node during the operation cycle. Compare the average load of the power grid node during the operation cycle with the reasonable resource index of the corresponding power grid node:

[0086] If the average load is greater than or equal to the reasonable resource index of the power grid node, mark the power grid node as a load power grid node;

[0087] If the average load amount is less than the reasonable resource index of the power grid node, then mark the power grid node as a non-load power grid node;

[0088] Among the non-load power grid nodes, perform a difference operation on the average load amount of the non-load power grid node and the reasonable resource index of the power grid node, take the absolute value of the difference to obtain the remaining load amount, perform a ratio operation on the remaining load amount and the reasonable resource index of the power grid node to obtain the remaining load ratio, and mark it as FH;

[0089] Obtain the actual load amount of the non-load power grid node during the operation cycle, mark it in the X-Y coordinate system to obtain the load amount change curve, obtain the actual load amounts of all peak points of the load amount change curve, sum and average them to obtain the maximum actual load amount average of the non-load power grid node during the operation cycle, perform a difference operation on the maximum actual load amount average and the reasonable resource index of the non-load power grid node, take the absolute value of the difference to obtain the minimum remaining load amount, perform a ratio operation on the minimum remaining load amount and the reasonable resource index of the non-load power grid node to obtain the minimum remaining load ratio, and mark it as ZF;

[0090] Perform data processing on the remaining load ratio FH and the minimum remaining load ratio ZF of the non-load power grid node, and obtain the load evaluation value JK through the formula: JK = a1 * ZF + a2 * FH, where a1 and a2 are both preset proportionality coefficients;

[0091] It should be noted that the meaning represented by the load evaluation value JK is: it reflects the deviation between the average load amount of the non-load power grid node and the reasonable resource index and the deviation between the maximum load amount average of the non-load power grid node during the operation cycle and the reasonable resource index. If the load evaluation value JK is larger, it means that the deviation between the average load amount of the non-load power grid node and the reasonable resource index and the deviation between the maximum load amount average of the non-load power grid node during the operation cycle and the reasonable resource index are larger, which means that the actual load amount of the non-load power grid node is much smaller than the reasonable resource index, and the utilization rate of the power grid resource capacity is low. On the contrary, if the load evaluation value JK is smaller, it means that the deviation between the average load amount of the non-load power grid node and the reasonable resource index and the deviation between the maximum load amount average of the non-load power grid node during the operation cycle and the reasonable resource index are smaller, which means that the actual load amount of the non-load power grid node is close to the reasonable resource index, and the utilization rate of the power grid resource capacity is high;

[0092] It should also be noted that a person skilled in the art collects multiple sets of sample data and sets corresponding preset proportionality coefficients for each set of sample data; substitutes the set preset proportionality coefficients and the collected sample data into the formula, and any two formulas form a binary linear equation system. Screen and take the average of the calculated coefficients to obtain a1 and a2;

[0093] In some embodiments, the load evaluation value JK is compared with the load evaluation threshold:

[0094] If the load evaluation value JK is greater than or equal to the load evaluation threshold, mark its non-load power grid nodes as idle power grid nodes;

[0095] If the load evaluation value JK is less than the load evaluation threshold, mark its non-load power grid nodes as balanced power grid nodes;

[0096] Obtain the number information of the idle power grid nodes and the load power grid nodes and display them in the holographic data of the AR platform.

[0097] Embodiment 3: As Figure 1 shown, the power distribution network holographic data visualization intelligent display and analysis system according to the embodiment of the present invention includes:

[0098] Resource optimization recommendation module: Based on the load power grid nodes and the idle power grid nodes, generate a load power grid node sorting table and an idle power grid node sorting table, and integrate and display them in the holographic data of the AR platform. Based on the load power grid node sorting table and the idle power grid node sorting table, optimize the power grid resource allocation;

[0099] Specifically, perform a difference process on the average load of the load power grid nodes and the corresponding reasonable resource indicators, and perform a ratio process on the difference and the reasonable resource indicators to obtain a resource overrun ratio. Sort the load power grid nodes from large to small according to the resource overrun ratio to obtain a load power grid node sorting table;

[0100] Sum up the remaining load of all idle power grid nodes to obtain the total remaining load. Perform a ratio process on the remaining load of the idle power grid nodes and the total remaining load to obtain the remaining load capacity performance value of the idle power grid nodes, and mark it as BXD;

[0101] Sum up the remaining load capacity performance value BXD of the idle power grid nodes and the load evaluation value JK to obtain the allocation priority value FY;

[0102] It should be noted that the meaning of the allocated priority value FY is that it reflects the proportion of the remaining capacity of idle power grid nodes in the total remaining capacity of all idle power grid nodes and the deviation between the remaining capacity of idle power grid nodes and the reasonable resource index. If the allocated priority value is larger, it means that there are sufficient idle power grid resources in the idle power grid nodes that can be allocated to the load power grid nodes. On the contrary, if the allocated priority value is smaller, it means that the power grid resources that the idle power grid nodes can allocate to the load power grid nodes are less;

[0103] Sort the idle power grid nodes in descending order according to the allocated priority value FY to obtain the sorting table of idle power grid nodes, and integrate the sorting table of idle power grid nodes into the holographic data of the AR platform for display.

[0104] It should be noted that the staff can allocate the remaining load of the idle power grid nodes to the load power grid nodes in turn according to the sorting table of the load power grid nodes according to the sorting table of the idle power grid nodes;

[0105] The technical solution of the present invention is as follows: Obtain the topological structure of the power grid, construct a three-dimensional model of the power grid structure based on the topological structure of the power grid, and integrate its three-dimensional model into the AR platform using the AR development platform and tools to achieve real-time display of the holographic data of the power grid. Import the three-dimensional model of the power grid structure into the simulation operation software, and set the initial operation parameters of the power grid in the simulation operation software. After the initial operation parameters of the power grid are set, conduct the exit simulation of each node of the power grid. Based on the exit simulation of each node of the power grid, record the operation change data of the power grid after the node exits. Based on the analysis of the operation change data, obtain the node impact evaluation value. Judge the criticality of the power grid nodes through the node impact evaluation value, and number each node of the power grid based on the node impact evaluation value, and integrate its number information into the AR platform. Construct a reasonable resource index for the power grid nodes based on the node impact evaluation value of the power grid nodes and the historical resource allocation data, and integrate the established reasonable resource index information into the holographic data of the AR platform for display. Obtain the resource utilization data of the power grid during the operation cycle. Based on the resource utilization data and the reasonable resource index of the power grid nodes, screen out the load power grid nodes, idle power grid nodes, and stable power grid nodes at the end of the power grid operation, and integrate the number information of the load power grid nodes and the idle power grid nodes into the holographic data of the AR system for display. Based on the load power grid nodes and the idle power grid nodes, generate a load power grid node ranking list and an idle power grid node ranking list, and integrate and display them in the holographic data of the AR platform. Based on the load power grid node ranking list and the idle power grid node ranking list, optimize the power grid resource allocation. The present invention constructs a power grid three-dimensional model based on the topological structure of the power grid and combines AR technology for display, and combines the exit simulation analysis of the power grid nodes to identify and three-dimensionally display the key nodes of the power grid. At the same time, establish a resource allocation index based on the criticality of the power grid nodes, and combine the actual load of the power grid nodes to analyze and optimize the resource allocation of the power grid nodes. While displaying various types of information of the power grid, realize the recommendation of the optimized information of the power grid resources.

[0106] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification only illustrates the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A holographic data visualization intelligent display and analysis system for a distribution network based on AR technology, characterized in that: Including: Model construction module: Obtain the topological structure of the power grid, construct a three-dimensional model of the power grid structure based on the topological structure of the power grid, and integrate its three-dimensional model into the AR platform using the AR development platform and tools to realize the real-time display of the holographic data of the power grid; Simulation evaluation module: Import the three-dimensional model of the power grid structure into the simulation operation software, set the initial operation parameters of the power grid in the simulation operation software, conduct the exit simulation of each node of the power grid, record the operation change data of the power grid after the node exits, analyze the operation change data, obtain the node influence evaluation value, judge the criticality of the power grid node through the node influence evaluation value, number each node of the power grid based on the node influence evaluation value, and integrate its number information into the AR platform for display in the holographic data of the power grid; Resource index establishment module: Construct a reasonable resource index for the power grid node based on the node influence evaluation value of the power grid node and the historical resource allocation data, and integrate the established reasonable resource index information into the holographic data of the AR platform for display; Resource utilization monitoring module: Obtain the resource utilization data of the power grid during the operation period, based on the resource utilization data and the reasonable resource index of the power grid node, screen out the load power grid nodes, idle power grid nodes and stable power grid nodes at the end of the power grid operation, and integrate the number information of the load power grid nodes and idle power grid nodes into the holographic data of the AR system for display; Resource optimization recommendation module: Generate a sorting table of load power grid nodes and a sorting table of idle power grid nodes based on the load power grid nodes and idle power grid nodes, and integrate and display them in the holographic data of the AR platform. Optimize the power grid resource allocation based on the sorting table of load power grid nodes and the sorting table of idle power grid nodes.

2. The power distribution network holographic data visualization intelligent display and analysis system based on AR technology according to claim 1, characterized in that: The operation change data includes the operation parameter change type and the operation parameter change amount; Among them, the acquisition method of the operation parameter change type is: If the power grid operation parameters are not equal to the initial operation parameters of the power grid within the exit duration, mark its operation parameter type as the operation parameter change type; The acquisition method of the operation parameter change amount is: Obtain the maximum value of the operation parameter corresponding to the operation parameter change type within the exit duration, perform a difference process on the maximum value and the initial operation parameters of the same type of power grid, and take the absolute value of the difference to obtain the operation parameter change amount.

3. The power distribution network holographic data visualization intelligent display and analysis system based on AR technology according to claim 1, characterized in that: The acquisition method of the node influence evaluation value DYy is: Perform data processing on the power grid operation parameter change degree value LX, the power grid operation amount change degree YX and the power grid operation time change degree value ZX, and obtain the node influence evaluation value DYy through the formula: DYy = s1 * LX + s2 * YX + s3 * ZX, where s1, s2 and s3 are all preset proportionality coefficients; Among them, the method for obtaining the power grid operation parameter change degree value LX is as follows: count the number of operation parameter change types, and perform a ratio process on the number of operation parameter change types and the number of initial power grid operation parameter types to obtain the power grid operation parameter change degree value LX.

4. The AR technology-based intelligent display and analysis system for holographic data of a distribution network according to claim 3, wherein: The method for obtaining the power grid operation quantity change degree value YX is as follows: Perform a ratio process on the power grid operation parameter change quantity and the corresponding initial operation parameter of the same type to obtain the change degree of the operation parameter, obtain the change degrees of the operation parameters of different change types, sum them up and take the average value to obtain the power grid operation quantity change degree value YX; The method for obtaining the power grid operation time change degree value ZX is as follows: Obtain the change time of the power grid operation parameters of different change types within the exit duration, sum them up and take the average value to obtain the average change time of the power grid operation parameters of different change types within the exit duration, and perform a ratio process on it and the exit duration to obtain the power grid operation time change degree value ZX.

5. The AR technology-based intelligent display and analysis system for holographic data of a distribution network according to claim 1, wherein: The method for obtaining the reasonable resource index of the power grid node is as follows: Perform a product process on the node influence evaluation value DYy of the power grid node and the average distribution ratio of the power grid resource capacity to obtain the distribution performance value of the power grid node, sum up the distribution performance values of all power grid nodes to obtain the total distribution performance value of all power grid nodes, and mark it as FBz; Obtain the total distribution capacity of the current power grid resources and mark it as ZF; Through the formula: Obtain the reasonable resource index ZBx of the power grid node.

6. The AR technology-based intelligent display and analysis system for holographic data of a distribution network according to claim 5, wherein: The method for obtaining the average distribution ratio of the power grid resource capacity is as follows: obtain the historical resource distribution data of the power grid node in multiple historical distribution cycles, where the historical resource distribution data includes the power grid resource distribution capacity allocated by the power grid node in the historical distribution cycle, perform a ratio process on the power grid resource distribution capacity allocated by the power grid node in the historical cycle and the total power grid resource distribution capacity in the corresponding historical cycle to obtain the power grid resource capacity distribution ratio of the power grid node in the historical cycle, obtain the power grid resource capacity distribution ratios of the power grid node in multiple historical cycles, sum them up and take the average value to obtain the average power grid resource capacity distribution ratio of the power grid node in the historical cycle, and mark it as FB.

7. The AR technology-based intelligent display and analysis system for holographic data of a distribution network according to claim 1, wherein: The screening method for the load power grid is as follows: sum up and take the average value of all actual load quantities of the power grid node in the operation cycle to obtain the average load quantity of the power grid node in the operation cycle, and compare the average load quantity of the power grid node in the operation cycle with the reasonable resource index of the corresponding power grid node: If the average load quantity is greater than or equal to the reasonable resource index of the power grid node, mark the power grid node as a load power grid node; If the average load amount is less than the reasonable resource index of the power grid node, then mark the power grid node as a non-load power grid node.

8. The intelligent display and analysis system for holographic data visualization of a distribution network based on AR technology according to claim 1, wherein: The screening method for the idle power grid nodes and balanced power grid nodes is as follows: Among the non-load power grid nodes, perform a difference operation on the average load amount of the non-load power grid node and the reasonable resource index of the power grid node, and take the absolute value of the difference to obtain the remaining load amount. Perform a ratio operation on the remaining load amount and the reasonable resource index of the power grid node to obtain the remaining load ratio, and mark it as FH. Obtain the actual load amount of the non-load power grid node during the operation cycle, and mark it in the X-Y coordinate system to obtain the load amount change curve. Obtain the actual load amounts of all peak points of the load amount change curve, and sum and average them to obtain the maximum actual load amount average value of the non-load power grid node during the operation cycle. Perform a difference operation on the maximum actual load amount average value and the reasonable resource index of the non-load power grid node, and take the absolute value of the difference to obtain the minimum remaining load amount. Perform a ratio operation on the minimum remaining load amount and the reasonable resource index of the non-load power grid node to obtain the minimum remaining load ratio, and mark it as ZF. Perform data processing on the remaining load ratio FH and the minimum remaining load ratio ZF of the non-load power grid node, and obtain the load evaluation value JK through the formula: JK = a1 * ZF + a2 * FH, where a1 and a2 are both preset proportionality coefficients. Compare the load evaluation value JK with the load evaluation threshold: If the load evaluation value JK is greater than or equal to the load evaluation threshold, then mark the non-load power grid node as an idle power grid node; If the load evaluation value JK is less than the load evaluation threshold, then mark the non-load power grid node as a balanced power grid node.

9. The intelligent display and analysis system for holographic data visualization of a distribution network based on AR technology according to claim 1, wherein: The generation method of the load power grid node sorting table is as follows: Perform a difference operation on the average load amount of the load power grid node and the corresponding reasonable resource index, and perform a ratio operation on the difference and the reasonable resource index to obtain the resource overrun ratio. Sort the load power grid nodes from largest to smallest according to the resource overrun ratio to obtain the load power grid node sorting table.

10. The intelligent display and analysis system for holographic data visualization of a distribution network based on AR technology according to claim 1, wherein: The generation method of the idle power grid node sorting table is as follows: Sum up the remaining load amounts of all idle power grid nodes to obtain the total remaining load amount value. Perform a ratio operation on the remaining load amount of the idle power grid node and the total remaining load amount value to obtain the remaining load capacity performance value of the idle power grid node, and mark it as BXD; Sum up the remaining load capacity performance value BXD of the idle power grid node and the load evaluation value JK to obtain the allocation priority value FY; Sort the idle power grid nodes from largest to smallest according to the allocation priority value FY to obtain the idle power grid node sorting table.

Images