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

Abstract

The present invention discloses a method for calculating the shortest power flow path of a power grid based on drone point clouds, comprising obtaining target power system data information; confirming the direct connection relationship of the equipment; confirming the topology of the directly connected equipment based on the connection relationship between the two ends of the equipment; confirming the topology of the indirectly connected equipment based on iterative association and deduplication operations; confirming the equipment connection metric based on the shortest step length and induction; performing power flow sequencing in combination with the main grid power flow direction of the target power system; and completing the solution of the shortest power flow path of the target power system. The present invention also discloses a data leakage monitoring method including the method for calculating the shortest power flow path of a power grid based on drone point clouds. By obtaining the equipment connection relationship of the target power system and performing corresponding processing and calculation, 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, but also has higher reliability, better accuracy and higher efficiency.

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

[0004] However, provincial power grids have as many as 500,000 topological points, and it's common for a single topological point to be directly connected to more than 10 other points. Under the conventional approach described above, the main grid topology construction and shortest power flow path solution lack a specific constructive calculation method. Furthermore, the computational depth and breadth required are significant, leading to an exponential increase in computational complexity. 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 drone 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. According to the data information obtained in step S1, the device is directly connected to confirm the relationship;

[0010] S3 according to the data information obtained in step S2, based on the connection relationship between each device at both ends, directly connected device topology confirmation;

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

[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. Based on 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 operating data and parameter information of the target power system;

[0017] Using drones and laser point cloud technology, all equipment in the main grid of the target power system is observed and located.

[0018] Step S2, based on the data information obtained in step S1, confirms the direct connection relationship of the devices, and 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 scheduling rights belong, 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, neutral point, busbar, and grounding point;

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

[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 nodes Equipment En i-end topology node and j-end topology nodes There is at least one of the following four connection relationships: relation relation relation

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

[0023] Step S3, based on the data information obtained in step S2 and the connection relationship between the two ends of each device, confirms the topology of the directly connected device, which specifically includes the following steps:

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

[0025] In the specific implementation, a PostgreSQL database is used to convert the device association relationship library after the direct connection relationship of the device 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 Python's Django ORM and Scikit-learn libraries.

[0026] Step S4, based on the data information obtained in step S3, confirms the topology of the indirectly connected devices based on iterative association and deduplication operations, and 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 topological nodes at the i end and the topological nodes at the j end of any two directly connected devices, the association operation is performed to remove the topological points associated with the original devices, and the topological points where the topological points of the associated devices are grounded no longer participate in subsequent associations; the results of the association operation are combined and duplicate removal operations are performed 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, the SQLAlchemy and Pandas libraries in Python are used to connect to the database and perform multi-threaded operations to improve computing efficiency.

[0031] Step S5, based on the data information obtained in step S4, confirms the device connection metric based on the shortest step length and induction, and specifically includes the following steps:

[0032] For the network, each device in the device association database is regarded 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 topology point sets T 1 and T 2 The shortest step length d(T 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 on 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 the 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, the SQLAlchemy and Pandas libraries in Python are used to connect to the database for multi-threaded operations to improve computing efficiency.

[0043] Step S6, based on the data information obtained in step S5 and combined with the main grid power flow direction of the target power system, performs power flow sequencing, which specifically includes the following steps:

[0044] According to the actual operation data of the target power system, the actual flow direction of the equipment is determined and compared with the obtained E m The directions between nodes in the matrix are compared, and the data with different directions between nodes and the actual tidal current direction are removed to obtain the matrix that adapts to the actual tidal current direction.

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

[0046] Step S7, based on the data information obtained in step S6, completes the solution of the shortest power flow path of the target power system, specifically including the following steps:

[0047] The actual tidal direction matrix obtained in step S6 is middle, Each row of data in is a possible path from each starting device topology 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 that t1 and t2 are any two non-grounded points of different equipment topology, and the value of r starts from 1 and keeps increasing. When the set P = (B(t1,r)∩B(t2,r))∪(B(t1,r)∩B(t2,r+1)) is not empty for the first time, the path group (t1,t2) formed by gluing the paths (t1,p) and (p,t2) determined by all points p in the set P is used as the shortest flow path connecting t1 and t2 that adapts to the actual 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(t 1,r), the average value of the distance between B(t1,r) and 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 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 tidal path connecting t1 and t2 that adapts to the actual tidal direction.

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

[0054] S8. According to the obtained trend sequencing results, construct the information entropy space based on the information entropy scheme, and realize data leakage monitoring 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 is adapted to the actual flow direction, from each starting device topological point The possible paths to the final landing point g

[0057] Will All paths in Considered as point x i , all points x i Construct the sample point space X; define the point x in X i The information entropy is H(xi ), 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 judgments:

[0062] If H(x,x1,x2,...,x n ) is greater than the set threshold, it is determined that the corresponding data outflow 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 outflow 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 obtains the equipment connection relationship of the target power system and performs corresponding processing and calculation. It 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, but also has higher reliability, better accuracy and higher efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 Schematic diagram of the flow chart of the calculation method of the present invention.

[0067] Figure 2 Schematic diagram of the monitoring method of the present invention. DETAILED DESCRIPTION

[0068] like Figure 1 The figure shows a flow chart of the calculation method of the present invention: 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:

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

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

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

[0072] S2. Confirm the direct connection relationship between the devices based on the data information obtained in step S1; specifically, the following steps:

[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 scheduling rights belong, 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, neutral point, busbar, and grounding point;

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

[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 nodes Equipment E n i-end topology node and j-end topology nodes There is at least one of the following four connection relationships: relation relation relation

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

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

[0078] According to the device association relationship library after the direct connection relationship of the device is confirmed obtained in step S2, a de-association operation is performed on the direct connection relationship according to the four connection relationships between the i-end topology node and the j-end topology node of any two directly connected devices, and the topology point associated with the original device is removed, and the topology point where the associated device topology point is the grounding point no longer participates in subsequent associations; the result of the de-association operation is subjected to a union and de-duplication 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 using the python psycopg2 and numpy libraries, and the obtained data model is normalized using the Django ORM and Scikit-learn libraries in python;

[0080] S4. Based on the data information obtained in step S3, the topology of the indirectly connected device is confirmed based on iterative association and deduplication operations; specifically, the following steps are included:

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

[0082] Repeat the above steps for the obtained n-th order indirectly connected device topology table (including performing an association operation based on 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 device, and ensuring that the topology points of the associated device topology point are grounded points no longer participate in subsequent associations, and performing a union and deduplication operation on the results of the association operation) to obtain an n+1-th order indirectly connected device topology table; where the value of n ranges from 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, we use Python's SQLAlchemy and Pandas libraries 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, the device connection metric is confirmed; specifically comprising the following steps:

[0086] For the network, each device in the device association database is regarded 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 topology point sets T 1 and T 2 The shortest step length d(T 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 on 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 the 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 the device topology points in B(t, r) are exactly the maximum order indirect connection device topology table E m The r-1 column equipment topology point is connected to E m The set formed by the difference of the set formed by the union of the topological points of the device in columns 1, 2, ..., r-2; 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. Based on 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 flow direction of the equipment is determined and compared with the obtained E m The directions between nodes in the matrix are compared, and the data with different directions between nodes and the actual tidal current direction are removed to obtain the matrix that adapts to the actual tidal current direction.

[0099] Based on the actual tidal flow direction matrix 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. Based on 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 tidal direction matrix obtained in step S6 is middle, Each row of data in is a possible path from each starting device topology 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 in different equipment topology, 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 empty for the first time, the path group (t1,t2) formed by gluing the paths (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. At the same time, this 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(t 1,r), the average value of the distance between B(t1,r) and 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 tidal path connecting t1 and t2 that adapts to the actual tidal direction.

[0107] like Figure 2 The figure shows a flow chart of the monitoring method of the present invention: 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. According to the data information obtained in step S1, the device is directly connected to confirm the relationship;

[0110] S3 according to the data information obtained in step S2, based on the connection relationship between each device at both ends, directly connected device topology confirmation;

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

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

[0115] S8. Based on the obtained power flow sequencing results, construct an information entropy space based on the information entropy scheme, and implement data leakage monitoring based on 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 is adapted to the actual flow direction, from each starting device topological point The possible paths to the final landing point g

[0117] Will All paths in Considered as 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 the 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 judgments:

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

[0124] Complete the monitoring of data leakage.

Claims

1. A method for calculating the shortest power flow path in a power grid based on drone point cloud, comprising the following steps: S1. Obtain data information of the target power system; S2. According to the data information obtained in step S1, the device is directly connected to confirm the relationship; S3 according to the data information obtained in step S2, based on the connection relationship between each device at both ends, directly connected device topology confirmation; S4. According to the data information obtained in step S3, based on iterative association and deduplication operations, the topology of the indirectly connected device is confirmed; S5. According to the data information obtained in step S4, based on the shortest step length and induction, the device connection metric is confirmed; specifically comprising the following steps: For the network, each device in the device association database is regarded 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 topology point sets T 1 and T 2 The shortest step length d(T 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 on 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 in, Z + is a set of positive integers; In Power Set 2 T On the topology, construct a ball B whose shortest connection distance of the 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; S6. Based on 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 Step S2, based on the data information obtained in step S1, confirms the direct connection relationship of the devices, and 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 scheduling rights belong, 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, neutral point, busbar, and grounding point; In specific implementation, all devices E e , according to the unique identifier after the device is instantiated, set the i-end topology endpoint for the device J-side topology endpoint Set device topology points and eventually form a device association database; 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 nodes Equipment E n i-end topology node and j-end topology nodes There is at least one of the following four connection relationships: relation relation relation According to the set rules, the formed device association relationship library is modified to complete the confirmation of the direct connection relationship of the devices.

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 Step S3, based on the data information obtained in step S2 and the connection relationship between the two ends of each device, confirms the topology of the directly connected device, which specifically includes the following steps: According to the device association relationship library after the direct connection relationship of the device is confirmed obtained in step S2, a de-association operation is performed on the direct connection relationship according to the four connection relationships between the i-end topology node and the j-end topology node of any two directly connected devices, and the topology point associated with the original device is removed, and the topology point where the associated device topology point is the grounding point no longer participates in subsequent associations; the result of the de-association operation is subjected to a union and de-duplication 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 Step S4, based on the data information obtained in step S3, confirms the topology of the indirectly connected devices based on iterative association and deduplication operations, and 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 topological nodes at the i end and the topological nodes at the j end of any two directly connected devices, the association operation is performed to remove the topological points associated with the original devices, and the topological points where the topological points of the associated devices are grounded no longer participate in subsequent associations; the results of the association operation are combined and duplicate removal operations are performed 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 Step S6, based on the data information obtained in step S5 and combined with the main grid power flow direction of the target power system, performs power flow sequencing, which specifically includes the following steps: According to the actual operation data of the target power system, the actual flow direction of the equipment is determined and compared with the obtained E m The directions between nodes in the matrix are compared, and the data with different directions between nodes and the actual tidal current direction are removed to obtain the matrix that adapts to the actual tidal current direction. Based on the actual tidal flow direction matrix The corresponding shortest step length quantities, metric topologies, circles and spheres are reconstructed.

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 S7, based on the data information obtained in step S6, completes the solution of the shortest power flow path of the target power system, specifically including the following steps: The actual tidal direction matrix obtained in step S6 is middle, Each row of data in is a possible path from each starting device topology 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 continues to increase. 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 gluing the paths (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.

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 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(t 1,r), the average value of the distance between B(t1,r) and 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 tidal path connecting t1 and t2 that adapts to the actual tidal direction.

8. A data leakage monitoring method comprising the method for calculating the shortest power flow path of a power grid based on drone point cloud according to any one of claims 1 to 7, characterized in that The following steps are also included: S8. According to the obtained trend sequencing results, construct the information entropy space based on the information entropy scheme, and realize data leakage monitoring according to the information entropy results in the information entropy space.

9. The data leakage monitoring method according to claim 8, 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 Considered as 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 the information entropy; is a 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 judgments: If H(x,x1,x2,...,x n ) is greater than the set threshold, it is determined that the corresponding data outflow 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 outflow behavior; Complete the monitoring of data leakage.

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