Auxiliary control data analysis system and method of intelligent substation

Abstract

The invention relates to the technical field of data analysis, in particular to an auxiliary control data analysis system and method for an intelligent substation, and the system comprises an information monitoring module, a database, a position analysis module, a data processing module and an information display module. Monitoring video data acquired by a camera through the information monitoring module; storing all the collected video data through the database; analyzing a moving track of an object through the position analysis module, and judging the object entering the monitoring area; the data processing module is used for processing data according to different monitoring objects; the information after data processing is displayed through the information display module; the category of the monitored object is judged by analyzing the moving track of the object, classification processing is carried out, suspicious personnel are prevented from entering the transformer substation, and meanwhile the phenomenon that the transformer substation is damaged due to invasion of foreign matter is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular to an auxiliary control data analysis system and method for an intelligent substation. Background Art

[0002] With the rapid development of computer technology and network communication technology, automation technology, the latest computer technology, network communication technology, video equipment technology and control technology are widely used in the security prevention of substations to monitor and reliably control substations online and transmit them remotely to the monitoring center or dispatching center.

[0003] The intelligent auxiliary control system takes the substation video monitoring system as its core. In order to meet the safe production of the power system, in the existing technology, the intelligent auxiliary control system mainly performs all-weather status monitoring of the installation locations of key equipment in the entire station and the surrounding environment. However, it is impossible to analyze the category of the monitored object through monitoring data. At the same time, there are loopholes in the monitoring of non-human external force damage, such as plastic film and kites.

[0004] Therefore, people need an auxiliary control data analysis system and method for smart substations to solve the above problems. By analyzing the movement trajectory of the object, the category of the monitored object can be determined and classified. While preventing suspicious persons from entering the substation, the phenomenon of damage to the substation caused by the intrusion of foreign objects is avoided. Summary of the invention

[0005] The purpose of the present invention is to provide an auxiliary control data analysis system and method for an intelligent substation to solve the problems raised in the above background technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: an auxiliary control data analysis system for a smart substation, the system comprising: an information monitoring module, a database, a location analysis module, a data processing module and an information display module;

[0007] The output end of the information monitoring module is connected to the input end of the database, the output end of the database is connected to the input end of the position analysis module, the output end of the position analysis module is connected to the input end of the data processing module, and the output end of the data processing module is connected to the input end of the information display module;

[0008] Monitoring the video data collected by the camera through the information monitoring module;

[0009] Storing all collected video data via the database;

[0010] Analyze the moving trajectory of the object through the position analysis module to determine the object entering the monitoring area;

[0011] Performing data processing according to different monitoring objects through the data processing module;

[0012] The information after data processing is displayed through the information display module.

[0013] Furthermore, the information monitoring module includes a video acquisition unit, a video processing unit, an image fusion unit and an object judgment unit;

[0014] The video acquisition unit is used to use a camera to collect video monitoring data in real time and send the data to the video processing unit, wherein the camera is a camera for monitoring key equipment in the substation; the video processing unit is used to use a time wheel algorithm to regularly capture video images; the image fusion unit is used to overlap the captured video images using a coincidence algorithm, and send the overlapped image data to the object judgment unit; the object judgment unit is used to judge whether the object moves: if movement occurs, it means that a suspicious object has entered the substation, and the data is sent to the database; conversely, if there is no movement data, it means that no suspicious object has been found to enter the substation.

[0015] Furthermore, the database is used to store all collected data and the locations of all equipment in the substation;

[0016] Further, the position analysis module includes a position extraction unit, a trajectory analysis unit and an object confirmation unit;

[0017] The position extraction unit obtains the motion trajectory of the moving object by establishing two-dimensional plane coordinates; the trajectory analysis unit is used to analyze the position pattern of the object and send the position analysis data to the object confirmation unit; the object confirmation unit is used to confirm the category of the monitored object according to the position pattern of the object.

[0018] Further, the data processing module includes an alarm unit, a region prediction unit and an object monitoring unit;

[0019] The alarm unit is used to selectively alarm the confirmed monitoring object category; the area prediction unit is used to extract the object area that last appeared in the camera to predict the location of the monitoring object, and send the location information to the inspection robot; the object monitoring unit is used to use the inspection robot to find the monitoring object and monitor it.

[0020] An auxiliary control data analysis system for a smart substation includes the following steps:

[0021] S1: monitors the video data collected by the camera and analyzes whether the object moves;

[0022] S2: stores all collected video data;

[0023] S3: Analyze the moving trajectory of the object and determine the object entering the monitoring area;

[0024] S4: Process data according to different monitoring objects;

[0025] S5: Display the information after data processing.

[0026] Furthermore, in step S1: m cameras are set up in the substation to collect video monitoring data V in real time using the cameras. m = {v q}, q = 1, 2, ..., m;

[0027] Traverse the video surveillance data, process the video data, and analyze whether the object moves. The specific steps are as follows:

[0028] S101: extract the video surveillance data of camera q, and use the time wheel algorithm to take a screenshot of the video data every tms to form n image data sets L = {l s}, s=1, 2, ..., n; wherein the time wheel algorithm has a timing effect, which is a conventional technical means of those skilled in the art, and therefore, no excessive elaboration is made;

[0029] S102: Overlapping the captured image data using a coincidence algorithm, the steps are as follows:

[0030] A1: Establish two-dimensional plane coordinates and extract data l of image s in image dataset L s ={(x i ,y i )}, i = 1, 2, ..., σ; according to the formula: Get the texture features of image s;

[0031] Among them, p represents the pixel point (x i ,y i )’s neighborhood, d p Represents the gray value of the neighborhood pixel, d c Represents the gray value of the center pixel;

[0032] A2: By traversing the image dataset L = {l s}, s = 1, 2, ..., n, the image texture with the same pixel is fused to form a new image data l s+1 ={(x i ,y i )}, i = 1, 2, ..., α;

[0033] S103: Analyze whether the object moves: traverse the image data set L and convert the image data l s={(x i ,y i )}, i = 1, 2, ..., σ and l s+1 ={(x i ,y i )}, i = 1, 2, ..., α, image comparison is performed to analyze whether new pixels are generated: if new pixels exist, it means that the monitored object moves; otherwise, if no new pixels exist, it means that no moving object is detected.

[0034] Furthermore, in step S2: the collected data is stored in a database, and the locations of all equipment in the substation are stored. k ={(m j , n j )}, j = 1, 2, ... k;

[0035] Furthermore, in step S3: analyzing the moving trajectory of the object to determine the object entering the monitoring area, the specific steps are as follows:

[0036] S301: Extracting image data l s+1 The new pixel point set in the object moves to form the point A = {(x i ,y i )}, i=1, 2,...,β, where At this time, each moving point corresponds to a time value, forming a time set T = {it}, i = 1, 2, ..., βms;

[0037] S302: Connect the moving point data according to different times to determine whether the moving object is a person or an object. The steps are as follows:

[0038] B1: Calculate the angle between each two line segment vectors based on the position of the moving point: Difference between two consecutive points to form a line segment vector set: (u x ,u y )={(x i+1 -x i ,y i+1 -y i )}, i = 1, 2, ..., β-1, then the set of angles between every two consecutive line segment vectors is obtained:

[0039] B2: Analyze the regularity of the moving position based on the vector angle to determine whether the moving object is a person or an object: If there is θ i =180°, indicating that there is a straight line area at the moving position point, then it is determined that the moving object is a person, and at this time, the process goes to step S303; otherwise, if there is no θ i=180°, indicating that there is no straight line area at the moving position point, and the moving object is determined to be an object, such as a kite with a broken line, or plastic or other objects that are harmful to the substation, etc. At this time, the process goes to step S401;

[0040] S303: Cluster the discreteness of the mobile location to determine whether the user is an operation and maintenance personnel or other suspicious persons. The steps are as follows:

[0041] C1: Collect object moving point data A = {(x i ,y i )}, i = 1, 2, ..., β, obtain the locations of all devices in the substation stored in the database, and form the device location set C k ={(m j , n j )}, j=1,2,...k, k≤β;

[0042] C2: cluster the mobile data using the device location as the cluster center: by traversing C k ={(m j , n j )}, j=1,2,…k are respectively (m j , n j ) as the center and R as the radius to form a circular area: (xm j ) 2 +(yn j ) 2 =R 2 , the circular areas are independent of each other and do not intersect each other; by traversing the moving point data A = {(x i ,y i )}, i = 1, 2, ..., β, cluster the moving point data in the circular area to form cluster data: Z = {(x i ,y i )}, i = 1, 2, ..., ∈, where,

[0043] C3: Analyze the number of cluster data to determine whether the user is an operation and maintenance personnel or other suspicious personnel: By traversing the cluster data: Z = {(x i ,y i )}, i = 1, 2, ..., ∈;

[0044] Since suspicious persons will move near multiple devices, while operation and maintenance personnel go directly to the target faulty device; at this time, let the number of ∈>μ be ρ, where μ is the set threshold. If ρ>ω, where ω is the set threshold, it is determined that the user belongs to other suspicious persons. At this time, enter step S403. Otherwise, if ρ<ω, it is determined that the user belongs to the operation and maintenance personnel. At this time, enter step S402.

[0045] Furthermore, in step S4: data processing is performed according to different monitoring objects, and the specific steps are as follows:

[0046] S401: If the monitored object is determined to be an article, the steps are as follows:

[0047] D1: The auxiliary control system processes the alarm and sends the alarm signal to relevant personnel;

[0048] D2: Move point A to the object i ={(x i ,y i )}, i = 1, 2, ..., β for straight line fitting: Assume the straight line equation is: Y1 = a + bx; where b is the slope and a is the intercept, according to the formula: Get the new equation of the line

[0049] D3: Collect moving point A i The horizontal coordinate set {x i},i=1,2,…,β, and the time set T={it},i=1,2,…,βms form a new data set {(it,x i )}, i=1,2,…,β, perform straight line fitting as in step D2 to obtain the straight line equation

[0050] D4: According to the equation of the straight line and Predict the moving point position of the object at time (β+1)t, and send the position to the inspection robot of the substation for searching. At this time, enter step S501;

[0051] S402: If it is determined that the monitored object is an operation and maintenance personnel, the operation and maintenance personnel are monitored for misoperation by collecting device data, and then the process proceeds to 502;

[0052] S403: If it is determined that the monitored object is another suspicious person, the auxiliary control system performs an alarm process.

[0053] Further, in step S5, the information after data processing is displayed, and the specific steps are as follows:

[0054] S501: The inspection robot is equipped with a camera device, which records the search information in real time and connects to the auxiliary control system for display;

[0055] S502: Using a camera to monitor and display in real time the operation and maintenance personnel repairing the faulty equipment.

[0056] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0057] The present invention uses a time wheel algorithm to perform a screenshot process on video data at regular intervals, can efficiently utilize thread resources for batch scheduling, and is convenient for subsequent data overlap; by using the overlap algorithm to overlap the captured image data, further analyzing whether the object moves according to whether new pixel points are generated, the accuracy of the data is improved, and it is convenient for subsequent object category analysis; by analyzing the angle of the object's motion trajectory to determine whether there is a straight line in the object's motion trajectory, it is distinguished whether the monitored object is a person or an object, which is conducive to the system performing monitoring and processing according to different categories and improving the efficiency of data processing; by clustering the mobile position to determine whether the user belongs to a suspicious person or an operation and maintenance person, it is conducive to subsequent data processing and improves the security capability of the substation; by performing multiple straight line fitting on the object's motion trajectory, the area where the object falls is predicted, which is convenient for the search of the inspection robot in the substation and improves safety; by analyzing the object's motion trajectory to determine the category of the monitored object and performing classification processing, while preventing suspicious persons from entering the substation, the phenomenon of harm to the substation caused by the invasion of foreign objects is avoided. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0059] Figure 1 It is a structural diagram of an auxiliary control data analysis system of a smart substation of the present invention;

[0060] Figure 2 The present invention is a flowchart of an auxiliary control data analysis method for a smart substation. DETAILED DESCRIPTION

[0061] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0062] See also Figure 1-Figure 2 ,The present invention provides a technical solution: an auxiliary control data analysis system for a smart substation, the system comprising: an information monitoring module, a database, a location analysis module, a data processing module and an information display module;

[0063] The output end of the information monitoring module is connected to the input end of the database, the output end of the database is connected to the input end of the position analysis module, the output end of the position analysis module is connected to the input end of the data processing module, and the output end of the data processing module is connected to the input end of the information display module;

[0064] Monitoring the video data collected by the camera through the information monitoring module;

[0065] The information monitoring module includes a video acquisition unit, a video processing unit, an image fusion unit and an object judgment unit;

[0066] The video acquisition unit is used to use a camera to collect video monitoring data in real time and send the data to the video processing unit, wherein the camera is a camera for monitoring key equipment in the substation; the video processing unit is used to use a time wheel algorithm to regularly capture video images; the image fusion unit is used to overlap the captured video images using a coincidence algorithm, and send the overlapped image data to the object judgment unit; the object judgment unit is used to judge whether the object moves: if movement occurs, it means that a suspicious object has entered the substation, and the data is sent to the database; conversely, if there is no movement data, it means that no suspicious object has been found to enter the substation.

[0067] Storing all collected video data via the database;

[0068] The database is used to store all collected data and also stores the locations of all equipment in the substation.

[0069] Analyze the moving trajectory of the object through the position analysis module to determine the object entering the monitoring area;

[0070] The position analysis module includes a position extraction unit, a trajectory analysis unit and an object confirmation unit;

[0071] The position extraction unit obtains the motion trajectory of the moving object by establishing two-dimensional plane coordinates; the trajectory analysis unit is used to analyze the position pattern of the object and send the position analysis data to the object confirmation unit; the object confirmation unit is used to confirm the category of the monitored object according to the position pattern of the object.

[0072] Performing data processing according to different monitoring objects through the data processing module;

[0073] The data processing module includes an alarm unit, a region prediction unit and an object monitoring unit;

[0074] The alarm unit is used to selectively alarm the confirmed monitoring object category; the area prediction unit is used to extract the object area that last appeared in the camera to predict the location of the monitoring object, and send the location information to the inspection robot; the object monitoring unit is used to use the inspection robot to find the monitoring object and monitor it.

[0075] The information after data processing is displayed through the information display module.

[0076] The information display module includes an item monitoring unit and a user monitoring unit; the item monitoring unit is used to use the inspection robot to record the search information in real time and connect to the auxiliary control system for display; the user monitoring unit is used to use the camera to monitor and display the operation and maintenance personnel repairing the faulty equipment in real time.

[0077] An auxiliary control data analysis system for a smart substation includes the following steps:

[0078] S1: monitors the video data collected by the camera and analyzes whether the object moves;

[0079] In step S1: m cameras are set up in the substation to collect video monitoring data V in real time. m = {v q}, q = 1, 2, ..., m;

[0080] Traverse the video surveillance data, process the video data, and analyze whether the object has moved. The specific steps are as follows:

[0081] S101: extract the video surveillance data of camera q, and use the time wheel algorithm to take a screenshot of the video data every t ms to form n image data sets L = {l s}, s=1, 2, ..., n; wherein the time wheel algorithm has a timing effect, which is a conventional technical means of those skilled in the art, and therefore, no excessive elaboration is made;

[0082] S102: Overlapping the captured image data using a coincidence algorithm, the steps are as follows:

[0083] A1: Establish two-dimensional plane coordinates and extract data l of image s in image dataset L s ={(x i ,y i )}, i = 1, 2, ..., σ; according to the formula: Get the texture features of image s;

[0084] Among them, p represents the pixel point (x i ,y i )’s neighborhood, d pRepresents the gray value of the neighborhood pixel, d c Represents the gray value of the center pixel;

[0085] A2: By traversing the image dataset L = {l s}, s = 1, 2, ..., n, the image texture with the same pixel is fused to form a new image data l s+1 ={(x i ,y i )}, i = 1, 2, ..., α;

[0086] S103: Analyze whether the object moves: traverse the image data set L and convert the image data l s ={(x i ,y i )}, i = 1, 2, ..., σ and l s+1 ={(x i ,y i )}, i = 1, 2, ..., α, image comparison is performed to analyze whether new pixels are generated: if new pixels exist, it means that the monitored object moves; otherwise, if no new pixels exist, it means that no moving object is detected.

[0087] S2: stores all collected video data;

[0088] In step S2: the collected data is stored in the database, and the locations of all equipment in the substation are stored. k ={(m j , n j )}, j = 1, 2, ... k;

[0089] S3: Analyze the moving trajectory of the object and determine the object entering the monitoring area;

[0090] In step S3: analyze the moving trajectory of the object and determine the object entering the monitoring area. The specific steps are as follows:

[0091] S301: Extracting image data l s+1 The new pixel point set in the object moves to form the point A = {(x i ,y i )}, i=1,2,…,β, where At this time, each moving point corresponds to a time value, forming a time set T = {it}, i = 1, 2, ..., βms;

[0092] S302: Connect the moving point data according to different times to determine whether the moving object is a person or an object. The steps are as follows:

[0093] B1: Calculate the angle between each two line segment vectors based on the position of the moving point: Difference between two consecutive points to form a line segment vector set: (u x ,u y )={(x i+1 -x i ,y i+1 -y i )}, i = 1, 2, ..., β-1, then the set of angles between every two consecutive line segment vectors is obtained:

[0094] B2: Analyze the regularity of the moving position based on the vector angle to determine whether the moving object is a person or an object: If there is θ i =180°, indicating that there is a straight line area at the moving position point, then it is determined that the moving object is a person, and at this time, the process goes to step S303; otherwise, if there is no θ i =180°, indicating that there is no straight line area at the moving position point, and the moving object is determined to be an object, such as a kite with a broken line, or plastic or other objects that are harmful to the substation, etc. At this time, the process goes to step S401;

[0095] S303: Cluster the discreteness of the mobile location to determine whether the user is an operation and maintenance personnel or other suspicious persons. The steps are as follows:

[0096] C1: Collect object moving point data A = {(x i ,y i )}, i = 1, 2, ..., β, obtain the locations of all devices in the substation stored in the database, and form the device location set C k ={(m j , n j )}, j=1,2,...k, k≤β;

[0097] C2: cluster the mobile data using the device location as the cluster center: by traversing C k ={(m j , n j )}, j=1,2,…k are respectively (m j , n j ) as the center and R as the radius to form a circular area: (xm j ) 2 +(yn j ) 2 =R 2 , the circular areas are independent of each other and do not intersect each other; by traversing the moving point data A = {(x i ,y i )}, i = 1, 2, ..., β, cluster the moving point data in the circular area to form cluster data: Z = {(xi ,y i )}, i = 1, 2, ..., ∈, where,

[0098] C3: Analyze the number of cluster data to determine whether the user is an operation and maintenance personnel or other suspicious personnel: By traversing the cluster data: Z = {(x i ,y i )}, i = 1, 2, ..., ∈;

[0099] Since suspicious persons will move near multiple devices, while operation and maintenance personnel go directly to the target faulty device; at this time, let the number of ∈>μ be ρ, where μ is the set threshold. If ρ>ω, where ω is the set threshold, it is determined that the user belongs to other suspicious persons. At this time, enter step S403. Otherwise, if ρ<ω, it is determined that the user belongs to the operation and maintenance personnel. At this time, enter step S402.

[0100] S4: Process data according to different monitoring objects;

[0101] In step S4: data processing is performed according to different monitoring objects. The specific steps are as follows:

[0102] S401: If the monitored object is determined to be an article, the steps are as follows:

[0103] D1: The auxiliary control system processes the alarm and sends the alarm signal to relevant personnel;

[0104] D2: Move point A to the object i ={(x i ,y i )}, i = 1, 2, ..., β for straight line fitting: Assume the straight line equation is: Y1 = a + bx; where b is the slope and a is the intercept, according to the formula: Get the new equation of the line

[0105] D3: Collect moving point A i The horizontal coordinate set {x i},i=1,2,…,β, and the time set T={it},i=1,2,…,βms form a new data set {(it,x i )}, i=1,2,…,β, perform straight line fitting as in step D2 to obtain the straight line equation

[0106] D4: According to the equation of the straight line and Predict the moving point position of the object at time (β+1)t, and send the position to the inspection robot of the substation for searching. At this time, enter step S501;

[0107] S402: If it is determined that the monitored object is an operation and maintenance personnel, the operation and maintenance personnel are monitored for misoperation by collecting device data, and then the process proceeds to 502;

[0108] S403: If it is determined that the monitored object is another suspicious person, the auxiliary control system performs an alarm process.

[0109] S5: Display the information after data processing.

[0110] In step S5, the information after data processing is displayed, and the specific steps are as follows:

[0111] S501: The inspection robot is equipped with a camera device, which records the search information in real time and connects to the auxiliary control system for display;

[0112] S502: Using a camera to monitor and display in real time the operation and maintenance personnel repairing the faulty equipment.

[0113] Embodiment 1:

[0114] In step S1: 100 cameras are set up in the substation to collect video monitoring data V in real time. m = {v q}, q = 1, 2, ..., 100;

[0115] Traverse the video surveillance data, process the video data, and analyze whether the object moves. The specific steps are as follows:

[0116] S101: extract the video surveillance data of camera q, and use the time wheel algorithm to take a screenshot of the video data every 1ms to form 50 image data sets L = {l s}, s = 1, 2, ..., 50;

[0117] S102: Overlapping the captured image data using a coincidence algorithm, the steps are as follows:

[0118] A1: Establish two-dimensional plane coordinates and extract data l of image s in image dataset L s ={(x i ,y i )}, i = 1, 2, ..., σ; according to the formula: Get the texture features of image s;

[0119] Among them, p represents the pixel point (x i ,y i )’s neighborhood, d p Represents the gray value of the neighborhood pixel, d c Represents the gray value of the center pixel;

[0120] A2: By traversing the image dataset L = {l s}, s = 1, 2, ..., 50, the image texture with the same pixel is pixel-fused to form a new image data l s+1 ={(x i ,y i )}, i = 1, 2, ..., 1000;

[0121] S103: Analyze whether the object moves: traverse the image data set L and convert the image data l s ={(x i ,y i )}, i = 1, 2, ..., 1000 and l s+1 ={(x i ,y i )}, i=1,2,…,1050 for image comparison, at which time, new pixels are generated: the existence of new pixels indicates that the object is detected to be moving.

[0122] In step S2: the collected data is stored in the database, and the locations of all equipment in the substation are stored. k ={(m j , n j )}, j = 1, 2, ... 45;

[0123] In step S3: analyze the moving trajectory of the object and determine the object entering the monitoring area. The specific steps are as follows:

[0124] S301: Extracting image data l s+1 The new pixel point set in the object moves to form the point A = {(x i ,y i )}, i = 1, 2, ..., 50, where At this time, each moving point corresponds to a time value, forming a time set T = {i}, i = 1, 2, ..., 50ms;

[0125] S302: Connect the moving point data according to different times to determine whether the moving object is a person or an object. The steps are as follows:

[0126] B1: Calculate the angle between each two line segment vectors based on the position of the moving point: Difference between two consecutive points to form a line segment vector set: (u x ,u y )={(x i+1 -x i ,y i+1 -y i )}, i = 1, 2, ..., 49, then the set of angles between every two consecutive line segment vectors is obtained:

[0127] B2: Analyze the regularity of the moving position based on the vector angle to determine whether the moving object is a person or an object: There is no θ i =180°, indicating that there is no straight line area at the moving position point, and the moving object is determined to be a kite with a broken string. At this time, the process goes to step S401.

[0128] S401: If the monitored object is determined to be an article, the steps are as follows:

[0129] D1: The auxiliary control system processes the alarm and sends the alarm signal to relevant personnel;

[0130] D2: Move point A to the object i ={(x i ,y i )}, i = 1, 2, ..., β for straight line fitting: Assume the straight line equation is: Y1 = a + bx; where b is the slope and a is the intercept, according to the formula: Get the new straight line equation Y1=2x;

[0131] D3: Collect moving point A i The horizontal coordinate set {x i}, i = 1, 2, ..., 50, and the time set T = {i}, i = 1, 2, ..., βms form a new data set {(i, x i )}, i = 1, 2, ..., β, perform straight line fitting as in step D2, and obtain the straight line equation Y2 = x;

[0132] D4: According to the linear equations Y1=2x and Y2=x, the moving point position of the object at the time of 51ms is predicted (51, 102), and the position is sent to the inspection robot of the substation for searching. At this time, enter step S501.

[0133] S501: The inspection robot is equipped with a camera device, which records the search information in real time and connects to the auxiliary control system for display.

[0134] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0135] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An auxiliary control data analysis system for a smart substation, characterized in that: The system includes: an information monitoring module, a database, a location analysis module, a data processing module and an information display module; The output end of the information monitoring module is connected to the input end of the database, the output end of the database is connected to the input end of the position analysis module, the output end of the position analysis module is connected to the input end of the data processing module, and the output end of the data processing module is connected to the input end of the information display module; Monitoring the video data collected by the camera through the information monitoring module; Storing all collected video data via the database; Analyze the moving trajectory of the object through the position analysis module to determine the object entering the monitoring area; Performing data processing according to different monitoring objects through the data processing module; The information after data processing is displayed through the information display module.

2. The auxiliary control data analysis system for a smart substation according to claim 1, characterized in that: The information monitoring module includes a video acquisition unit, a video processing unit, an image fusion unit and an object judgment unit; The video acquisition unit is used to use a camera to collect video surveillance data in real time and send the data to the video processing unit; the video processing unit is used to use a time wheel algorithm to capture video images at a fixed time; the image fusion unit is used to overlap the captured video images using a coincidence algorithm and send the overlapped image data to the object judgment unit; the object judgment unit is used to judge whether the object has moved.

3. The auxiliary control data analysis system for a smart substation according to claim 1 is characterized in that: The position analysis module includes a position extraction unit, a trajectory analysis unit and an object confirmation unit; The position extraction unit obtains the motion trajectory of the moving object by establishing two-dimensional plane coordinates; the trajectory analysis unit is used to analyze the position pattern of the object and send the position analysis data to the object confirmation unit; the object confirmation unit is used to confirm the category of the monitored object according to the position pattern of the object.

4. The auxiliary control data analysis system for a smart substation according to claim 1, characterized in that: The data processing module includes an alarm unit, a region prediction unit and an object monitoring unit; The alarm unit is used to selectively alarm the confirmed monitoring object category; the area prediction unit is used to extract the object area that appeared last time in the camera to predict the location of the monitoring object, and send the location information to the inspection robot; The object monitoring unit is used to use the inspection robot to find the monitoring object and perform monitoring.

5. An auxiliary control data analysis system for a smart substation, characterized in that: The following steps are involved: S1: monitors the video data collected by the camera and analyzes whether the object moves; S2: stores all collected video data; S3: Analyze the moving trajectory of the object and determine the object entering the monitoring area; S4: Process data according to different monitoring objects; S5: Display the information after data processing.

6. The auxiliary control data analysis system of a smart substation according to claim 5 is characterized in that: In step S1: m cameras are set up in the substation to collect video monitoring data V in real time using the cameras m = {v q }, q = 1, 2, ..., m; Traverse the video surveillance data, process the video data, and analyze whether the object has moved. The specific steps are as follows: S101: extract the video surveillance data of camera q, and use the time wheel algorithm to take a screenshot of the video data every t ms to form n image data sets L = {l s }, s = 1, 2, ..., n; S102: Overlapping the captured image data using a coincidence algorithm, the steps are as follows: A1: Establish two-dimensional plane coordinates and extract data l of image s in image dataset L s ={(x i ,y i )}, i = 1, 2, ..., σ; according to the formula: Get the texture features of image s; Among them, p represents the pixel point (x i ,y i )’s neighborhood, d p Represents the gray value of the neighborhood pixel, d c Represents the gray value of the center pixel; A2: By traversing the image dataset L = {l s }, s = 1, 2, ..., n, the image texture with the same pixel is fused to form a new image data l s+1 ={(x i ,y i )}, i = 1, 2, ..., α; S103: Analyze whether the object moves: traverse the image data set L and convert the image data l s ={(x i ,y i )}, i = 1, 2, ..., σ and l s+1 ={(x i ,y i )}, i = 1, 2, ..., α to perform image comparison and analyze whether new pixels are generated: if new pixels exist, it means that the object is detected to move.

7. The auxiliary control data analysis system of a smart substation according to claim 6 is characterized in that: In step S3: analyze the moving trajectory of the object and determine the object entering the monitoring area. The specific steps are as follows: S301: Extracting image data l s+1 The new pixel point set in the object moves to form the point A = {(x i ,y i )}, i=1,2,…,β, where At this time, each moving point corresponds to a time value, forming a time set T = {it}, i = 1, 2, ..., βms; S302: Connect the moving point data according to different times to determine whether the moving object is a person or an object. The steps are as follows: B1: Calculate the angle between each two line segment vectors based on the position of the moving point: Difference between two consecutive points to form a line segment vector set: (u x ,u y )={(x i+1 -x i ,y i+1 -y i )}, i = 1, 2, ..., β-1, then the set of angles between every two consecutive line segment vectors is obtained: B2: Analyze the regularity of the moving position based on the vector angle to determine whether the moving object is a person or an object: If there is θ i =180°, indicating that there is a straight line area at the moving position point, then it is determined that the moving object is a person, and at this time, the process goes to step S303; otherwise, if there is no θ i =180°, indicating that there is no straight line area at the moving position point, and the moving object is determined to be an object, and in this case, the process goes to step S401; S303: Cluster the discreteness of the mobile location to determine whether the user is an operation and maintenance personnel or other suspicious persons. The steps are as follows: C1: Collect object moving point data A = {(x i ,y i )}, i = 1, 2, ..., β, obtain the locations of all devices in the substation stored in the database, and form the device location set C k ={(m j , n j )}, j=1,2,...k, k≤β; C2: cluster the mobile data using the device location as the cluster center: by traversing C k ={(m j , n j )}, j=1,2,…k are respectively (m j , n j ) as the center and R as the radius to form a circular area: (xm j ) 2 +(yn j ) 2 =R 2 ; By traversing the moving point data A = {(x i ,y i )}, i = 1, 2, ..., β, cluster the moving point data in the circular area to form cluster data: Z = {(x i ,y i )}, i = 1, 2, ..., ∈, where, C3: Analyze the number of cluster data to determine whether the user is an operation and maintenance personnel or other suspicious personnel: By traversing the cluster data: Z = {(x i ,y i )}, i=1,2,…,∈, let the number of∈>μ be ρ, where μ is the set threshold, if ρ>ω, where ω is the set threshold, then it is determined that the user belongs to other suspicious persons, and at this time, enter step S403.

8. The auxiliary control data analysis system of a smart substation according to claim 7 is characterized in that: In step S4: data processing is performed according to different monitoring objects. The specific steps are as follows: S401: If the monitored object is determined to be an article, the steps are as follows: D1: The auxiliary control system processes the alarm and sends the alarm signal to relevant personnel; D2: Move point A to the object i ={(x i ,y i )}, i = 1, 2, ..., β for straight line fitting: Assume the straight line equation is: Y1 = a + bx; where b is the slope and a is the intercept, according to the formula: Get the new equation of the line D3: Collect moving point A i The horizontal coordinate set {x i }, i = 1, 2, ..., β, and the time set T = {it}, i = 1, 2, ..., βms form a new data set {(it, x i )), i = 1, 2, ..., β, perform straight line fitting as in step D2, and obtain the straight line equation D4: According to the equation of the straight line and Predict the moving point position of the object at time (β+1)t, and send the position to the inspection robot of the substation for search; S402: If it is determined that the monitored object is an operation and maintenance personnel, monitor whether the operation and maintenance personnel have made an erroneous operation by collecting device data; S403: If it is determined that the monitored object is another suspicious person, the auxiliary control system performs an alarm process.

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