Power grid shortest power flow path calculation method and data leakage monitoring method based on unmanned aerial vehicle point cloud

Abstract

The invention discloses a power grid shortest power flow path calculation method based on unmanned aerial vehicle point cloud. The method comprises the following steps: acquiring data information of a target power system; confirming the direct connection relation of the equipment; confirming a direct connection device topology based on a connection relationship between two ends of the device; confirming the topology of the indirect connection equipment on the basis of iterative association and duplicate removal operation; confirming equipment connection measurement based on the shortest step length and induction; performing power flow sequencing in combination with the main network power flow direction of the target power system; and solving the shortest power flow path of the target power system. The invention further discloses a data leakage monitoring method comprising the power grid shortest power flow path calculation method based on the unmanned aerial vehicle point cloud. According to the method, the equipment connection relation of the target power system is acquired, and corresponding processing and calculation are performed, so that calculation of the shortest power flow path of the power system and monitoring of data leakage in the data transmission process are realized, and the reliability, the accuracy and the efficiency are higher.

Description

Technical Field

[0001] The present invention belongs to the field of electrical automation, and specifically relates to a method for calculating the shortest power flow path of a power grid based on an unmanned aerial vehicle point cloud and a method for monitoring data leakage. Background Art

[0002] With the development of economy and technology and the improvement of people's living standards, electricity has become an indispensable secondary energy source in people's production and life, bringing endless convenience to people's production and life. Therefore, ensuring the stable and reliable supply of electricity has become one of the most important tasks of the power system.

[0003] The calculation of the shortest power flow path of the power system is one of the prerequisites for the safe and stable operation of the power system. At present, the shortest power flow path calculation scheme of the power system is based on the calculation of the main grid topology structure. At present, the commonly used main grid topology construction technology is generally to abstract all the devices e on the main grid into the device topology point t with i and j topological endpoints on the grid N, and initially reflect the set E of all devices directly connected to the main grid N and the device e. e Then, combined with power grid flow analysis and other technologies, the shortest flow path can be solved. Under the existing scheme, any two non-isolated devices are selected on the main network. By observing the wiring diagram in the above way, all feasible paths can be screened according to the flow direction, and finally the shortest flow path can be solved.

[0004] However, the number of provincial power grid equipment topology points reaches 500,000, and it is common for a certain equipment topology point to directly connect to more than 10 equipment topology points. Under the above conventional scheme, the main grid topology construction and the shortest power flow path solution lack a specific constructive calculation method, and the calculation traversal depth and breadth are large, resulting in an exponential increase in the amount of calculation. Summary of the invention

[0005] One of the purposes of the present invention is to provide a method for calculating the shortest power flow path of a power grid based on an unmanned aerial vehicle point cloud with high reliability, good accuracy and high efficiency.

[0006] A second object of the present invention is to provide a data leakage monitoring method that includes the power grid shortest power flow path calculation method based on drone point cloud.

[0007] The method for calculating the shortest power flow path of a power grid based on a drone point cloud provided by the present invention comprises the following steps:

[0008] S1. Obtain data information of the target power system;

[0009] S2. Confirm the direct connection relationship between the devices according to the data information obtained in step S1;

[0010] S3. According to the data information obtained in step S2, based on the connection relationship between the two ends of each device, the topology of the directly connected device is confirmed;

[0011] S4. According to the data information obtained in step S3, based on iterative association and deduplication operations, confirm the topology of the indirectly connected device;

[0012] S5. According to the data information obtained in step S4, based on the shortest step length and induction, the device connection metric is confirmed;

[0013] S6. According to the data information obtained in step S5, combined with the main grid flow direction of the target power system, flow sequencing is performed;

[0014] S7. Based on the data information obtained in step S6, the shortest power flow path of the target power system is solved.

[0015] The step S1 of acquiring the data information of the target power system specifically includes the following steps:

[0016] Obtaining operation data and parameter information of the target power system;

[0017] UAVs and laser point cloud technology are used to observe and locate all equipment in the main grid of the target power system.

[0018] The step S2 of confirming the direct connection relationship of the device according to the data information obtained in step S1 specifically includes the following steps:

[0019] According to the data information obtained in step S1, the device topology point is set; the device topology point includes the device name, the device unique identifier, the device topology point unique number, the i-end topology node, the j-end topology node, the attributes of the topology point where the device is located, the department to which the device dispatching right belongs, the device location information and the device voltage level information; the attributes of the topology point where the device is located include the connection point, the neutral point, the busbar and the grounding point;

[0020] In the specific implementation, the python uuid library is used to set all device E e , according to the unique identifier of the device after instantiation, set the i-end topology endpoint for the device j-side topology endpoint Set the device topology points and finally form a device association relationship library;

[0021] Setting rules: If two devices E on the grid m and E n For a directly connected device, device E m i-end topology node and j-end topology node Equipment En i-end topology node and j-end topology node At least one of the following four connection relationships exists: relation relation relation

[0022] According to the set rules, the formed device association library is modified to complete the confirmation of the direct connection relationship of the equipment.

[0023] According to the data information obtained in step S2, the topology of the directly connected device is confirmed based on the connection relationship between the two ends of each device, which specifically includes the following steps:

[0024] According to the device association relationship library after the device direct connection relationship is confirmed obtained in step S2, according to the four connection relationships between the i-end topological node and the j-end topological node of any two directly connected devices, the direct connection relationship is subjected to an association operation, the topological point associated with the original device is removed, and the topological point where the associated device topological point is the grounding point is no longer involved in the subsequent association; the result of the association operation is subjected to a union and deduplication operation to obtain a directly connected device topology table E 1 ;

[0025] In the specific implementation, the PostgreSQL database is used to convert the device association relationship library after the device direct connection relationship obtained in step S2 is confirmed into a data model, and then the obtained data model is converted into a dictionary EQU through python's psycopg2 and numpy libraries, and the obtained data model is normalized using Django ORM and Scikit-learn libraries in python.

[0026] According to the data information obtained in step S3, the topology of the indirectly connected device is confirmed based on iterative association and deduplication operations, which specifically includes the following steps:

[0027] According to the directly connected device topology table E obtained in step S3 1 According to the four connection relationships between the i-end topological node and the j-end topological node of any two directly connected devices, the association operation is performed to remove the topological points associated with the original device, and the topological points whose topological points of the associated device are grounding points no longer participate in subsequent associations; the results of the association operation are combined and deduplicated to obtain the second-order indirect connection device topology table E 2 ;

[0028] Repeat the above steps for the obtained n-order indirect connection device topology table to obtain the n+1-order indirect connection device topology table; wherein the value range of n is 2 to m-1, and m is the maximum order of the set topology table;

[0029] The data content in the m-order indirect connection device topology table is used as the matrix E m ;

[0030] In the specific implementation, SQLAlchemy and Pandas libraries in Python are used to connect to the database and perform multi-threaded operations to improve computing efficiency.

[0031] According to the data information obtained in step S4, the device connection metric is confirmed based on the shortest step length and induction, which specifically includes the following steps:

[0032] For the grid, each device in the device association database is taken as a topological point, the set of all devices is recorded as T, and the power set 2 is constructed. T ;

[0033] Set any two device topological point sets T 1 and T 2 The shortest step length between 1 ,T 2 )for

[0034]

[0035] The shortest step length d(T 1 ,T 2 ) in power set 2 T The following metric topology τ is induced d :

[0036]

[0037] So far, (2 T ,τ d ) constitute a topological space;

[0038] In Power Set 2 T On the topology, the circle B(t,r) with the shortest connection distance r of the device topology point t is constructed as Among them, Z + is a set of positive integers;

[0039] In Power Set 2 T On the topology, construct a ball B whose shortest connection distance of device topology point t is less than r r (t,r) is

[0040]

[0041] set up For E m The set of all device topological points in the rth column of , then B(t,r) and B r (t,r) satisfies and B(t,r) and B r (t,r) can be obtained through E m Calculated;

[0042] In the specific implementation, SQLAlchemy and Pandas libraries in Python are used to connect to the database for multi-threaded operations to improve computing efficiency.

[0043] Step S6, according to the data information obtained in step S5, combines the main grid flow direction of the target power system to perform flow sequencing, which specifically includes the following steps:

[0044] According to the actual operation data of the target power system, the actual power flow direction of the equipment is determined and compared with the obtained E m The inter-node directions in the , and the data with different inter-node directions from the determined actual flow direction are removed to obtain the matrix that adapts to the actual flow direction

[0045] Based on the actual flow direction matrix obtained The corresponding shortest step length quantities, metric topologies, circles and spheres are constructed again.

[0046] Step S7, according to the data information obtained in step S6, completes the solution of the shortest power flow path of the target power system, which specifically includes the following steps:

[0047] The actual power flow direction matrix obtained in step S6 middle, Each row of data in is a possible path from each starting device topological point to the grounding point that adapts to the actual power flow direction; The k-th row of data in is represented as

[0048] Assume t1 and t2 are any two non-grounded points of different equipment topology points, the value of r starts from 1 and increases continuously, when the set P = (B(t1,r)∩B(t2,r))∪(B(t1,r)∩B(t2,r+1)) is not an empty set for the first time, the path group (t1,t2) bonded by the path (t1,p) determined by all points p in the set P and (p,t2) is used as the shortest power flow path connecting t1 and t2 that adapts to the actual power flow direction;

[0049] In specific implementation, the following steps are used to solve the shortest power flow path:

[0050]

[0051] where dis(B(t1,r),B(t2,r)) is the Levenshtein distance between t1 and t2; dis(B(t1,r)∩B(t2,r+1)) is the diameter of B(t1,r)∩B(t2,r+1); avg(dis(B(t1,r),B(t2,r)),dis(B(t1,r)∩B(t2,r+1))) is the diameter of B(t1,r). 1,r), the average value of the distance between B(t1,r), B(t2,r) and the diameter of B(t1,r)∩B(t2,r+1); max(dis(B(t1,r), B(t2,r)), dis(B(t1,r)∩B(t2,r+1))) is the maximum value of the distance between B(t1,r), B(t2,r) and the diameter of B(t1,r)∩B(t2,r+1); m is the set change value; is the calculated value;

[0052] Set the judgment threshold TT: When the value of m starts from 1 and continues to increase, When it first appears, the path group (t1, t2) formed by gluing the paths (t1, p) and (p, t2) determined by all points p in the set P = (B(t1, r) ∩ B(t2, r)) ∪ (B(t1, r) ∩ B(t2, r+1)) P is taken as the shortest flow path connecting t1 and t2 that adapts to the actual flow direction.

[0053] The present invention also provides a data leakage monitoring method including the power grid shortest power flow path calculation method based on drone point cloud, and further includes the following steps:

[0054] S8. According to the obtained flow sequencing results, the information entropy space is constructed based on the information entropy scheme, and the data leakage monitoring is realized according to the information entropy results in the information entropy space.

[0055] The step S8 specifically includes the following steps:

[0056] According to the flow sequencing results and the matrix adapted to the actual flow direction Each row of data in the table is adapted to the actual flow direction and is from each starting device topological point. The possible paths to the final landing point g

[0057] Will All paths in Consider point x i , all points x i Construct sample point space X; define point x in X i The information entropy ofi ), thus constructing the entropy space (X,H(X));

[0058] The information entropy H(x,x1,x2,...,x) involving data content and sample point space is calculated using the following formula: n ):

[0059]

[0060] Where mm is the number of points in the sample point space X; a rr is a point x in X i The weight of information entropy; H xrr (x) is the point x in X i The information entropy of ; x is a point in X;

[0061] For the data content involved in the data outflow behavior, the corresponding information entropy H(x,x1,x2,...,x n ) and make a judgment:

[0062] If H(x,x1,x2,...,x n ) is greater than the set threshold, it is determined that the corresponding data outbound behavior has data leakage;

[0063] If H(x,x1,x2,...,x n ) is less than or equal to the set threshold, it is determined that there is no data leakage in the corresponding data outbound behavior;

[0064] Complete the monitoring of data leakage.

[0065] The method for calculating the shortest power flow path of a power grid and the method for monitoring data leakage based on drone point cloud provided by the present invention not only realizes the calculation of the shortest power flow path of the power system and the monitoring of data leakage in the data transmission process by acquiring the equipment connection relationship of the target power system and performing corresponding processing and calculation, but also has higher reliability, better accuracy and higher efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 The figure is a schematic diagram of the flow chart of the calculation method of the present invention.

[0067] Figure 2 The figure is a schematic diagram of the method flow of the monitoring method of the present invention. DETAILED DESCRIPTION

[0068] like Figure 1 The figure shows a flow chart of the method of the present invention: the method for calculating the shortest power flow path of a power grid based on drone point cloud provided by the present invention comprises the following steps:

[0069] S1. Acquire data information of the target power system; specifically includes the following steps:

[0070] Obtaining operation data and parameter information of the target power system;

[0071] Using drones and laser point cloud technology, all equipment in the main grid of the target power system is observed and located; the equipment includes AC (DC) lines (ends), generators, transformers, circuit breakers, switches, etc.

[0072] S2. According to the data information obtained in step S1, confirm the direct connection relationship of the device; specifically, the steps include:

[0073] According to the data information obtained in step S1, the device topology point is set; the device topology point includes the device name, the device unique identifier, the device topology point unique number, the i-end topology node, the j-end topology node, the attributes of the topology point where the device is located, the department to which the device dispatching right belongs, the device location information and the device voltage level information; the attributes of the topology point where the device is located include the connection point, the neutral point, the busbar and the grounding point;

[0074] In the specific implementation, the python uuid library is used to set all device E e , according to the unique identifier of the device after instantiation, set the i-end topology endpoint for the device j-side topology endpoint Set the device topology points and finally form a device association relationship library;

[0075] Setting rules: If two devices E on the grid m and E n For a directly connected device, device E m i-end topology node and j-end topology node Equipment E n i-end topology node and j-end topology node At least one of the following four connection relationships exists: relation relation relation

[0076] According to the set rules, the formed equipment association relationship library is modified to complete the confirmation of the direct connection relationship of the equipment;

[0077] S3. According to the data information obtained in step S2, based on the connection relationship between the two ends of each device, the topology of the directly connected device is confirmed; specifically comprising the following steps:

[0078] According to the device association relationship library after the device direct connection relationship is confirmed obtained in step S2, according to the four connection relationships between the i-end topological node and the j-end topological node of any two directly connected devices, the direct connection relationship is subjected to an association operation, the topological point associated with the original device is removed, and the topological point where the associated device topological point is the grounding point is no longer involved in the subsequent association; the result of the association operation is subjected to a union and deduplication operation to obtain a directly connected device topology table E 1 ;

[0079] In the specific implementation, a PostgreSQL database is used to convert the device association relationship library after the device direct connection relationship is confirmed in step S2 into a data model, and then the obtained data model is converted into a dictionary EQU through the psycopg2 and numpy libraries of python, and the Django ORM and Scikit-learn libraries in python are used to normalize the obtained data model;

[0080] S4. According to the data information obtained in step S3, based on iterative association and deduplication operations, confirm the topology of the indirectly connected device; specifically, the steps include:

[0081] According to the directly connected device topology table E obtained in step S3 1 According to the four connection relationships between the i-end topological node and the j-end topological node of any two directly connected devices, the association operation is performed to remove the topological points associated with the original device, and the topological points whose topological points of the associated device are grounding points no longer participate in subsequent associations; the results of the association operation are combined and deduplicated to obtain the second-order indirect connection device topology table E 2 ;

[0082] For the obtained n-order indirect connection device topology table, repeat the above steps (including performing association operations according to the four connection relationships between the i-end topology node and the j-end topology node of any two directly connected devices, removing the topology points associated back to the original devices, and making the topology points of the associated device topology points as grounding points no longer participate in subsequent associations, and performing union and deduplication operations on the results of the association operations) to obtain an n+1-order indirect connection device topology table; wherein the value range of n is 2 to m-1, and m is the set maximum order of the topology table;

[0083] The data content in the m-order indirect connection device topology table is used as the matrix E m ;

[0084] In the specific implementation, SQLAlchemy and Pandas libraries in Python are used to connect to the database and perform multi-threaded operations to improve computing efficiency;

[0085] S5. According to the data information obtained in step S4, based on the shortest step length and induction, confirm the device connection metric; specifically comprising the following steps:

[0086] For the grid, each device in the device association database is taken as a topological point, the set of all devices is recorded as T, and the power set 2 is constructed. T ;

[0087] Set any two device topological point sets T 1 and T 2 The shortest step length between 1 ,T 2 )for

[0088]

[0089] The shortest step length d(T 1 ,T 2 ) in power set 2 T The following metric topology τ is induced d :

[0090]

[0091] So far, (2 T ,τ d ) constitute a topological space;

[0092] In Power Set 2 T On the topology, the circle B(t,r) with the shortest connection distance r of the device topology point t is constructed as Among them, Z + is a set of positive integers;

[0093] In Power Set 2 T On the topology, construct a ball B whose shortest connection distance of device topology point t is less than r r (t,r) is

[0094]

[0095] set up For E m The set of all device topological points in the rth column of , then B(t,r) and B r (t,r) satisfies and B(t,r) and B r The relationship satisfied by (t, r) shows that all device topology points in B(t, r) are exactly the maximum order indirect connection device topology table E m The r-1 column of equipment topology points is connected to E m The set formed by the difference of the set formed by the union of the topological points of the 1,2,...,r-2 columns of equipment; Br (t,r) is the union of all topological points of devices after B(t,m) takes 1,2,...,r-1 in m iterations; at the same time, it also shows that B(t,r) and B r (t,r) can be obtained through E m Calculated;

[0096] In the specific implementation, SQLAlchemy and Pandas libraries in Python are used to connect to the database for multi-threaded operations to improve computing efficiency;

[0097] S6. According to the data information obtained in step S5, combined with the main grid flow direction of the target power system, flow sequencing is performed; specifically comprising the following steps:

[0098] According to the actual operation data of the target power system, the actual power flow direction of the equipment is determined and compared with the obtained E m The inter-node directions in the , and the data with different inter-node directions from the determined actual flow direction are removed to obtain the matrix that adapts to the actual flow direction

[0099] Based on the actual flow direction matrix obtained According to the same technical solution in step S5, the corresponding shortest step length d(T 1 ,T 2 ), metric topology τ d , circle B(t,r) and sphere B r (t,r);

[0100] S7. According to the data information obtained in step S6, the shortest power flow path of the target power system is solved; specifically, the steps include:

[0101] The actual power flow direction matrix obtained in step S6 middle, Each row of data in is a possible path from each starting device topological point to the grounding point that adapts to the actual power flow direction; The k-th row of data in is represented as

[0102] Assume that t1 and t2 are any two non-grounded points of different equipment topology points, and the value of r starts from 1 and increases continuously. When the set P = (B(t1,r)∩B(t2,r))∪(B(t1,r)∩B(t2,r+1)) is not an empty set for the first time, the path group (t1,t2) formed by the path (t1,p) determined by all points p in the set P and the path (p,t2) glued together is used as the shortest power flow path connecting t1 and t2 that adapts to the actual power flow direction; at the same time, the shortest power flow path is not unique;

[0103] In specific implementation, the following steps are used to solve the shortest power flow path:

[0104]

[0105] where dis(B(t1,r),B(t2,r)) is the Levenshtein distance between t1 and t2; dis(B(t1,r)∩B(t2,r+1)) is the diameter of B(t1,r)∩B(t2,r+1); avg(dis(B(t1,r),B(t2,r)),dis(B(t1,r)∩B(t2,r+1))) is the diameter of B(t1,r). 1,r), the average value of the distance between B(t1,r), B(t2,r) and the diameter of B(t1,r)∩B(t2,r+1); max(dis(B(t1,r), B(t2,r)), dis(B(t1,r)∩B(t2,r+1))) is the maximum value of the distance between B(t1,r), B(t2,r) and the diameter of B(t1,r)∩B(t2,r+1); m is the set change value; is the calculated value;

[0106] Set the judgment threshold TT: When the value of m starts from 1 and increases continuously, When it first appears, the path group (t1, t2) formed by gluing the paths (t1, p) and (p, t2) determined by all points p in the set P = (B(t1, r) ∩ B(t2, r)) ∪ (B(t1, r) ∩ B(t2, r+1)) P is taken as the shortest flow path connecting t1 and t2 that adapts to the actual flow direction.

[0107] like Figure 2 The method flow diagram of the monitoring method of the present invention is shown as follows: the data leakage monitoring method disclosed in the present invention, which includes the power grid shortest power flow path calculation method based on drone point cloud, comprises the following steps:

[0108] S1. Obtain data information of the target power system;

[0109] S2. Confirm the direct connection relationship between the devices according to the data information obtained in step S1;

[0110] S3. According to the data information obtained in step S2, based on the connection relationship between the two ends of each device, the topology of the directly connected device is confirmed;

[0111] S4. According to the data information obtained in step S3, based on iterative association and deduplication operations, confirm the topology of the indirectly connected device;

[0112] S5. According to the data information obtained in step S4, based on the shortest step length and induction, the device connection metric is confirmed;

[0113] S6. According to the data information obtained in step S5, combined with the main grid flow direction of the target power system, flow sequencing is performed;

[0114] S7. According to the data information obtained in step S6, the shortest power flow path of the target power system is solved;

[0115] S8. According to the obtained power flow sequencing results, an information entropy space is constructed based on the information entropy scheme, and data leakage monitoring is implemented according to the information entropy results in the information entropy space; specifically, the steps include:

[0116] According to the flow sequencing results and the matrix adapted to the actual flow direction Each row of data in the table is adapted to the actual flow direction and is from each starting device topological point. The possible paths to the final landing point g

[0117] Will All paths in Consider point x i , all points x i Construct the sample point space X; define the information entropy of the points in X as H(x i ), thus constructing the entropy space (X,H(X));

[0118] The information entropy H(x,x1,x2,...,x) involving data content and sample point space is calculated using the following formula: n ):

[0119]

[0120] Where mm is the number of points in the sample point space X; a rr is a point x in X i The weight of information entropy; is a point x in X i The information entropy of ; x is a point in X;

[0121] For the data content involved in the data outflow behavior, the corresponding information entropy H(x,x1,x2,...,x n ) and make a judgment:

[0122] If H(x,x1,x2,...,x n ) is greater than the set threshold, it is determined that the corresponding data outbound behavior has data leakage;

[0123] If H(x,x1,x2,...,x n ) is less than or equal to the set threshold, it is determined that there is no data leakage in the corresponding data outbound behavior;

[0124] Complete the monitoring of data leakage.

Claims

1. A method for calculating the shortest power flow path of a power grid based on drone point cloud, comprising the following steps: S1. Obtain data information of the target power system; S2. Confirm the direct connection relationship between the devices according to the data information obtained in step S1; S3. According to the data information obtained in step S2, based on the connection relationship between the two ends of each device, the topology of the directly connected device is confirmed; S4. According to the data information obtained in step S3, based on iterative association and deduplication operations, confirm the topology of the indirectly connected device; S5. According to the data information obtained in step S4, based on the shortest step length and induction, the device connection metric is confirmed; S6. According to the data information obtained in step S5, combined with the main grid flow direction of the target power system, flow sequencing is performed; S7. Based on the data information obtained in step S6, the shortest power flow path of the target power system is solved.

2. The method for calculating the shortest power flow path of a power grid based on drone point cloud according to claim 1 is characterized in that The step S2 of confirming the direct connection relationship of the device according to the data information obtained in step S1 specifically includes the following steps: According to the data information obtained in step S1, the device topology point is set; the device topology point includes the device name, the device unique identifier, the device topology point unique number, the i-end topology node, the j-end topology node, the attributes of the topology point where the device is located, the department to which the device dispatching right belongs, the device location information and the device voltage level information; the attributes of the topology point where the device is located include the connection point, the neutral point, the busbar and the grounding point; In specific implementation, all devices E e , according to the unique identifier of the device after instantiation, set the i-end topology endpoint for the device j-side topology endpoint Set the device topology points and finally form a device association relationship library; Setting rules: If two devices E on the grid m and E n For a directly connected device, device E m i-end topology node and j-end topology node Equipment E n i-end topology node and j-end topology node At least one of the following four connection relationships exists: relation relation relation According to the set rules, the formed device association library is modified to complete the confirmation of the direct connection relationship of the equipment.

3. The method for calculating the shortest power flow path of a power grid based on drone point cloud according to claim 2 is characterized in that According to the data information obtained in step S2, the topology of the directly connected device is confirmed based on the connection relationship between the two ends of each device, which specifically includes the following steps: According to the device association relationship library after the device direct connection relationship is confirmed obtained in step S2, according to the four connection relationships between the i-end topological node and the j-end topological node of any two directly connected devices, the direct connection relationship is subjected to an association operation, the topological point associated with the original device is removed, and the topological point where the associated device topological point is the grounding point is no longer involved in the subsequent association; the result of the association operation is subjected to a union and deduplication operation to obtain a directly connected device topology table E 1 .

4. The method for calculating the shortest power flow path of a power grid based on drone point cloud according to claim 3 is characterized in that According to the data information obtained in step S3, the topology of the indirectly connected device is confirmed based on iterative association and deduplication operations, which specifically includes the following steps: According to the directly connected device topology table E obtained in step S3 1 According to the four connection relationships between the i-end topological node and the j-end topological node of any two directly connected devices, the association operation is performed to remove the topological points associated with the original device, and the topological points whose topological points of the associated device are grounding points no longer participate in subsequent associations; the results of the association operation are combined and deduplicated to obtain the second-order indirect connection device topology table E 2 ; Repeat the above steps for the obtained n-order indirect connection device topology table to obtain the n+1-order indirect connection device topology table; wherein the value range of n is 2 to m-1, and m is the maximum order of the set topology table; The data content in the m-order indirect connection device topology table is used as the matrix E m .

5. The method for calculating the shortest power flow path of a power grid based on drone point cloud according to claim 4 is characterized in that According to the data information obtained in step S4, the device connection metric is confirmed based on the shortest step length and induction, which specifically includes the following steps: For the grid, each device in the device association database is taken as a topological point, the set of all devices is recorded as T, and the power set 2 is constructed. T ; Set any two device topological point sets T 1 and T 2 The shortest step length between 1 ,T 2 )for The shortest step length d(T 1 ,T 2 ) in power set 2 T The following metric topology τ is induced d : So far, (2 T ,τ d ) constitute a topological space; In Power Set 2 T On the topology, the circle B(t,r) with the shortest connection distance r of the device topology point t is constructed as Among them, Z + is a set of positive integers; In Power Set 2 T On the topology, construct a ball B whose shortest connection distance of device topology point t is less than r r (t,r) is set up For E m The set of all device topological points in the rth column of , then B(t,r) and B r (t,r) satisfies and B(t,r) and B r (t,r) can be obtained through E m Calculated.

6. The method for calculating the shortest power flow path of a power grid based on drone point cloud according to claim 5 is characterized in that Step S6, according to the data information obtained in step S5, combines the main grid flow direction of the target power system to perform flow sequencing, which specifically includes the following steps: According to the actual operation data of the target power system, the actual power flow direction of the equipment is determined and compared with the obtained E m The inter-node directions in the , and the data with different inter-node directions from the determined actual flow direction are removed to obtain the matrix that adapts to the actual flow direction Based on the actual flow direction matrix obtained The corresponding shortest step length quantities, metric topologies, circles and spheres are constructed again.

7. The method for calculating the shortest power flow path of a power grid based on drone point cloud according to claim 6 is characterized in that Step S7, according to the data information obtained in step S6, completes the solution of the shortest power flow path of the target power system, which specifically includes the following steps: The actual power flow direction matrix obtained in step S6 middle, Each row of data in is a possible path from each starting device topological point to the grounding point that adapts to the actual power flow direction; The k-th row of data in is represented as Assume that t1 and t2 are any two non-grounded points of different equipment topology points, the value of r starts from 1 and increases continuously, when the set P = (B(t1,r)∩B(t2,r))∪(B(t1,r)∩B(t2,r+1)) is not an empty set for the first time, the path group (t1,t2) glued by the path (t1,p) determined by all points p in the set P and (p,t2) is used as the shortest flow path connecting t1 and t2 that adapts to the actual flow direction.

8. The method for calculating the shortest power flow path of a power grid based on drone point cloud according to claim 7 is characterized in that The step S7 specifically includes the following steps: In specific implementation, the following steps are used to solve the shortest power flow path: where dis(B(t1,r),B(t2,r)) is the Levenshtein distance between t1 and t2; dis(B(t1,r)∩B(t2,r+1)) is the diameter of B(t1,r)∩B(t2,r+1); avg(dis(B(t1,r),B(t2,r)),dis(B(t1,r)∩B(t2,r+1))) is the diameter of B(t1,r). 1,r), the average value of the distance between B(t1,r), B(t2,r) and the diameter of B(t1,r)∩B(t2,r+1); max(dis(B(t1,r), B(t2,r)), dis(B(t1,r)∩B(t2,r+1))) is the maximum value of the distance between B(t1,r), B(t2,r) and the diameter of B(t1,r)∩B(t2,r+1); m is the set change value; is the calculated value; Set the judgment threshold TT: When the value of m starts from 1 and increases continuously, When it first appears, the path group (t1, t2) formed by gluing the paths (t1, p) and (p, t2) determined by all points p in the set P = (B(t1, r) ∩ B(t2, r)) ∪ (B(t1, r) ∩ B(t2, r+1)) P is taken as the shortest flow path connecting t1 and t2 that adapts to the actual flow direction.

9. A data leakage monitoring method comprising the power grid shortest power flow path calculation method based on drone point cloud according to any one of claims 1 to 8, characterized in that The following steps are also included: S8. According to the obtained flow sequencing results, the information entropy space is constructed based on the information entropy scheme, and the data leakage monitoring is realized according to the information entropy results in the information entropy space.

10. The data leakage monitoring method according to claim 9, characterized in that The step S8 specifically includes the following steps: According to the flow sequencing results and the matrix adapted to the actual flow direction Each row of data in the table is adapted to the actual flow direction and is from each starting device topological point. The possible paths to the final landing point g Will All paths in Consider point x i , all points x i Construct the sample point space X; define the information entropy of the points in X as H(x i ), thus constructing the entropy space (X,H(X)); The information entropy H(x,x1,x2,...,x) involving data content and sample point space is calculated using the following formula: n ): Where mm is the number of points in the sample point space X; a rr is a point x in X i The weight of information entropy; H xrr (x) is the point x in X i The information entropy of ; x is a point in X; For the data content involved in the data outflow behavior, the corresponding information entropy H(x,x1,x2,...,x n ) and make a judgment: If H(x,x1,x2,...,x n ) is greater than the set threshold, it is determined that the corresponding data outbound behavior has data leakage; If H(x,x1,x2,...,x n ) is less than or equal to the set threshold, it is determined that there is no data leakage in the corresponding data outbound behavior; Complete the monitoring of data leakage.

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