A method and system for safety warning in electric power operations

Abstract

The present invention discloses a power operation safety warning method and system, which relates to the technical field of operation safety warning. When receiving the point cloud data to be analyzed collected by a lidar, multiple historical point cloud data corresponding to the point cloud data to be analyzed are obtained. The point cloud data to be analyzed is input into a preset radius denoising function to generate static intrusion point cloud data. According to the point cloud data to be analyzed, the historical point cloud data, and a preset dynamic intrusion distance, the corresponding dynamic intrusion point cloud data is determined. The static intrusion point cloud data and the dynamic intrusion point cloud data are fused to generate fused intrusion point cloud data. Based on the comparison result of the pre-acquired static point cloud data and the fused intrusion point cloud data, it is determined whether to activate the alarm. The technical problem that at present, due to the environmental interference at the operation site at night, it is difficult for non-staff to clearly see the safety warning signs placed and timely give warnings, reducing the safety of power operations, is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of operation safety warning, and particularly to a power operation safety warning method and system. Background Art

[0002] The continuous development of the economy and society has promoted the gradual improvement of people's material living standards, and people's demand for electricity is also increasing day by day. Due to the influence of various objective factors, there are still many deficiencies in the safety management of power operations. Therefore, in order to avoid situations such as electric shock, falling from height, and non-staff entering the operation site by mistake, it is crucial to strengthen safety management.

[0003] Currently, it is mainly through placing safety warning signs at the power operation site to remind non-staff to enter the power operation area. However, at night, due to the interference of the operation site environment, non-staff are difficult to see the placed safety warning signs clearly, and cannot be warned in time, reducing the safety of power operations. Summary of the Invention

[0004] The present invention provides a power operation safety warning method and system, which solves the technical problem that the existing power operation safety warning mainly reminds non-staff to enter the power operation area by placing safety warning signs at the power operation site. However, at night, due to the interference of the operation site environment, non-staff are difficult to see the placed safety warning signs clearly, and cannot be warned in time, reducing the safety of power operations.

[0005] A power operation safety warning method provided in the first aspect of the present invention is applied to a processing unit of a virtual fence device and a lidar and an alarm connected to the processing unit, and includes:

[0006] When receiving the to-be-analyzed point cloud data collected by the lidar, obtaining a plurality of historical point cloud data corresponding to the to-be-analyzed point cloud data;

[0007] Inputting the to-be-analyzed point cloud data into a preset radius denoising function to generate static intrusion point cloud data;

[0008] Determining corresponding dynamic intrusion point cloud data according to the to-be-analyzed point cloud data, the historical point cloud data, and a preset dynamic intrusion distance;

[0009] Performing fusion processing on the static intrusion point cloud data and the dynamic intrusion point cloud data to generate fused intrusion point cloud data;

[0010] Based on the comparison result between the pre-acquired static point cloud data and the fused intrusion point cloud data, determining whether to activate the alarm.

[0011] Optionally, the radius denoising function is specifically:

[0012]

[0013] S = {p ∈ S cut | Count(p, R s ) > K s};

[0014] Among them, S cut is the intermediate point cloud data, S is the static intrusion point cloud data, S raw is the point cloud data to be analyzed, p is the point to be analyzed in the point cloud data to be analyzed, x is the abscissa of the point to be analyzed, y is the ordinate of the point to be analyzed, R cut is the interception radius, R s is the screening radius, K s is the number of screening points.

[0015] Optionally, the step of determining the corresponding dynamic intrusion point cloud data according to the point cloud data to be analyzed, the historical point cloud data and a preset dynamic intrusion distance includes:

[0016] Using the point cloud data to be analyzed and the historical point cloud data as dynamic point cloud data; among them, the dynamic point cloud data includes a plurality of dynamic points;

[0017] Based on the dynamic points, a preset dynamic intrusion distance and the dynamic point cloud data, determining the corresponding dynamic intrusion points;

[0018] Using all the dynamic intrusion points as the dynamic intrusion point cloud data.

[0019] Optionally, the step of determining the corresponding dynamic intrusion points based on the dynamic points, a preset dynamic intrusion distance and the dynamic point cloud data includes:

[0020] Indexing the nearest point corresponding to the dynamic point from the dynamic point cloud data;

[0021] Calculating the distance value between the nearest point and the dynamic point;

[0022] Judging whether the distance value is greater than a preset dynamic intrusion distance;

[0023] When the distance value is greater than the dynamic intrusion distance, determining the dynamic point as a dynamic intrusion point.

[0024] Optionally, the step of judging whether to activate the alarm based on the comparison result of the pre-acquired static point cloud data and the fused intrusion point cloud data includes:

[0025] Calculating the number of different points between the pre-acquired static point cloud data and the fused intrusion point cloud data;

[0026] Determine whether the number of difference points is greater than a preset warning quantity threshold;

[0027] If the number of difference points is greater than the warning quantity threshold, activate the alarm;

[0028] If the number of difference points is less than or equal to the warning quantity threshold, do not activate the alarm.

[0029] A power operation safety warning system provided by the second aspect of the present invention is applied to a processing unit of a virtual fence device, a lidar and an alarm connected to the processing unit, and includes:

[0030] An acquisition module, configured to obtain a plurality of historical point cloud data corresponding to the to-be-analyzed point cloud data when receiving the to-be-analyzed point cloud data collected by the lidar;

[0031] A denoising module, configured to input the to-be-analyzed point cloud data into a preset radius denoising function to generate static intrusion point cloud data;

[0032] A dynamic intrusion point cloud acquisition module, configured to determine corresponding dynamic intrusion point cloud data according to the to-be-analyzed point cloud data, the historical point cloud data and a preset dynamic intrusion distance;

[0033] A fusion module, configured to perform fusion processing on the static intrusion point cloud data and the dynamic intrusion point cloud data to generate fusion intrusion point cloud data;

[0034] An analysis module, configured to determine whether to activate the alarm based on a comparison result between pre-acquired static point cloud data and the fusion intrusion point cloud data.

[0035] Optionally, the radius denoising function is specifically:

[0036]

[0037] S = {p ∈ S cut |Count(p, R s ) > K s};

[0038] Wherein, S cut is intermediate point cloud data, S is static intrusion point cloud data, S raw is the to-be-analyzed point cloud data, p is a to-be-analyzed point of the to-be-analyzed point cloud data, x is the abscissa of the to-be-analyzed point, y is the ordinate of the to-be-analyzed point, R cut is an interception radius, R s is a screening radius, and K s is the number of screening points.

[0039] Optionally, the dynamic intrusion point cloud acquisition module includes:

[0040] A dynamic point cloud acquisition sub-module, configured to use the point cloud data to be analyzed and the historical point cloud data as dynamic point cloud data; wherein, the dynamic point cloud data includes a plurality of dynamic points;

[0041] A dynamic intrusion point acquisition sub-module, configured to determine corresponding dynamic intrusion points based on the dynamic points, a preset dynamic intrusion distance, and the dynamic point cloud data;

[0042] A dynamic intrusion point cloud sub-module, configured to use all the dynamic intrusion points as dynamic intrusion point cloud data.

[0043] Optionally, the dynamic intrusion point acquisition sub-module includes:

[0044] An indexing unit, configured to index the nearest point corresponding to the dynamic point from the dynamic point cloud data;

[0045] A first analysis unit, configured to calculate a distance value between the nearest point and the dynamic point;

[0046] A second analysis unit, configured to determine whether the distance value is greater than a preset dynamic intrusion distance;

[0047] When the distance value is greater than the dynamic intrusion distance, the dynamic point is determined as a dynamic intrusion point.

[0048] Optionally, the analysis module includes:

[0049] A first analysis sub-module, configured to calculate the number of different points between the pre-acquired static point cloud data and the fused intrusion point cloud data;

[0050] A second analysis sub-module, configured to determine whether the number of different points is greater than a preset warning quantity threshold;

[0051] If the number of different points is greater than the warning quantity threshold, the alarm is activated;

[0052] If the number of different points is less than or equal to the warning quantity threshold, the alarm is not activated.

[0053] From the above technical solutions, it can be seen that the present invention has the following advantages:

[0054] When receiving the point cloud data to be analyzed collected by the lidar, obtain multiple historical point cloud data corresponding to the point cloud data to be analyzed, input the point cloud data to be analyzed into a preset radius denoising function to generate static intrusion point cloud data, determine the corresponding dynamic intrusion point cloud data according to the point cloud data to be analyzed, the historical point cloud data, and a preset dynamic intrusion distance, perform fusion processing on the static intrusion point cloud data and the dynamic intrusion point cloud data to generate fused intrusion point cloud data, and determine whether to activate the alarm based on the comparison result between the previously obtained static point cloud data and the fused intrusion point cloud data. This solves the technical problem that at present, due to the environmental interference at the operation site at night, it is difficult for non-staff to clearly see the safety warning signs placed, and warnings cannot be given in a timely manner, reducing the safety of power operations. By combining and analyzing the static intrusion point cloud data and the dynamic intrusion point cloud data, false alarms of the device caused by external interference are avoided, and the reliability of the virtual fence device operation is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0056] Figure 1 It is a flowchart of the steps of a power operation safety warning method provided in Embodiment 1 of the present invention;

[0057] Figure 2 It is a flowchart of the steps of a power operation safety warning method provided in Embodiment 2 of the present invention;

[0058] Figure 3 It is a schematic structural diagram of the virtual fence device provided in Embodiment 2 of the present invention;

[0059] Figure 4 It is a block diagram of the structure of a power operation safety warning system provided in Embodiment 3 of the present invention;

[0060] Among them, the meanings of the reference numerals are as follows:

[0061] 1. Processing unit; 2. Lidar; 3. Alarm; 4. Power supply. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0062] An embodiment of the present invention provides a power operation safety warning method and system, which are used to solve the technical problem that the existing power operation safety warning mainly reminds non-staff to enter the power operation area by placing safety warning signs at the power operation site. However, at night, due to the environmental interference at the operation site, non-staff are difficult to clearly see the placed safety warning signs and cannot be warned in time, reducing the safety of power operations.

[0063] In order to make the invention purpose, features, and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the embodiments described below are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work belong to the scope of protection of the present invention.

[0064] Please refer to Figure 1 , Figure 1 which is a step flowchart of a power operation safety warning method provided in Embodiment 1 of the present invention.

[0065] A power operation safety warning method provided by the present invention is applied to the processing unit 1 of the virtual fence device and the lidar 2 and the alarm 3 connected to the processing unit 1, and includes:

[0066] Step 101: When receiving the point cloud data to be analyzed collected by the lidar, obtain a plurality of historical point cloud data corresponding to the point cloud data to be analyzed.

[0067] The point cloud data to be analyzed refers to the real-time point cloud data generated by the scanning information of the lidar 2 in the virtual fence device at the current moment.

[0068] The historical point cloud data refers to the first k frames of point cloud data collected by the lidar 2 at the previous moment.

[0069] In the embodiment of the present invention, when receiving the real-time point cloud data collected by the lidar 2, obtain the first k frames of point cloud data temporarily stored in the processing unit 1.

[0070] Step 102: Input the point cloud data to be analyzed into a preset radius denoising function to generate static intrusion point cloud data.

[0071] In the embodiment of the present invention, input the point cloud data to be analyzed into a preset radius denoising function, and screen the point cloud data in the point cloud data to be analyzed through the radius denoising function to generate static intrusion point cloud data.

[0072] In another embodiment of the present invention, perform radius filtering processing on the point cloud data to be analyzed to generate static intrusion point cloud data.

[0073] Step 103: Determine the corresponding dynamic intrusion point cloud data according to the point cloud data to be analyzed, historical point cloud data, and a preset dynamic intrusion distance.

[0074] In an embodiment of the present invention, a kd-tree is constructed using the point cloud data to be analyzed and historical point cloud data. The data points of the point cloud data to be analyzed and historical point cloud data are input into the kd-tree, and the data point closest to the indexed data point is used as the nearest point. The distance value between the data point and the nearest point is calculated. By comparing the distance value with the preset dynamic intrusion distance, it is determined whether to add the data point to the dynamic intrusion point cloud data.

[0075] Step 104: Perform fusion processing on the static intrusion point cloud data and the dynamic intrusion point cloud data to generate fused intrusion point cloud data.

[0076] In an embodiment of the present invention, the static intrusion point cloud data and the dynamic intrusion point cloud data are fused to generate fused intrusion point cloud data, which is specifically represented by the formula: D fuse = S ∪ D dynanic , where D fuse is the fused intrusion point cloud data, D dynamic is the dynamic intrusion point cloud data, and S is the static intrusion point cloud data.

[0077] Step 105: Based on the comparison result between the pre-acquired static point cloud data and the fused intrusion point cloud data, determine whether to activate the alarm.

[0078] The static point cloud data refers to the point cloud data Z in the current environment where there is no intrusion object. The static point cloud data is obtained by cropping and denoising the point cloud data Z.

[0079] In an embodiment of the present invention, the number of differential points of the fused intrusion point cloud data relative to the pre-acquired static point cloud data is calculated. When the number of differential points is greater than a preset number threshold, the alarm 3 of the virtual fence device is activated for alarm.

[0080] It should be noted that each time the virtual fence device is activated, the processing unit 1 controls the lidar 2 to perform k scans on the horizontal plane. Each scan obtains a point cloud S i (i = 1, 2,..., k). The k point clouds are fused to obtain the initial static point cloud data S raw = S1 ∪ S2... ∪ S k . Then, the initial static point cloud data is cropped and denoised to obtain the static point cloud data Z.

[0081] In an embodiment of the present invention, when receiving the point cloud data to be analyzed collected by the lidar 2, a plurality of historical point cloud data corresponding to the point cloud data to be analyzed are obtained. The point cloud data to be analyzed is input into a preset radius denoising function to generate static intrusion point cloud data. According to the point cloud data to be analyzed, the historical point cloud data, and a preset dynamic intrusion distance, the corresponding dynamic intrusion point cloud data is determined. The static intrusion point cloud data and the dynamic intrusion point cloud data are fused to generate fused intrusion point cloud data. Based on the comparison result of the previously obtained static point cloud data and the fused intrusion point cloud data, it is determined whether to activate the alarm 3. This solves the technical problem that at present, due to the environmental interference at the operation site at night, it is difficult for non-staff to clearly see the safety warning signs placed, and warnings cannot be given in time, reducing the safety of power operations. By combining and analyzing the static intrusion point cloud data and the dynamic intrusion point cloud data, false alarms of the device caused by external interference are avoided, and the reliability of the virtual fence device operation is improved.

[0082] Please refer to Figure 2 , Figure 2 which is a flowchart of the steps of a power operation safety warning method provided in the second embodiment of the present invention.

[0083] A power operation safety warning method provided by the present invention is applied to the processing unit 1 of the virtual fence device and the lidar 2 and the alarm 3 connected to the processing unit 1, and includes:

[0084] Step 201: When receiving the point cloud data to be analyzed collected by the lidar, a plurality of historical point cloud data corresponding to the point cloud data to be analyzed are obtained.

[0085] In an embodiment of the present invention, when receiving the point cloud data to be analyzed collected by the lidar 2, the historical point cloud data collected by the lidar 2 at the previous moment is obtained.

[0086] Step 202: The point cloud data to be analyzed is input into a preset radius denoising function to generate static intrusion point cloud data.

[0087] In an embodiment of the present invention, after adjusting the screening radius and the number of screening points of the radius denoising function, the point cloud data to be analyzed is input into the preset radius denoising function to generate the corresponding static intrusion point cloud data.

[0088] It should be noted that the radius denoising function is specifically:

[0089]

[0090] S = {p ∈ S cut | Count(p, R s ) > K s};

[0091] Among them, Scut is the intermediate point cloud data, S is the static intrusion point cloud data, S raw is the point cloud data to be analyzed, p is the point to be analyzed in the point cloud data to be analyzed, x is the abscissa of the point to be analyzed, y is the ordinate of the point to be analyzed, R cut is the interception radius, R s is the screening radius, K s is the number of screening points.

[0092] It should be noted that the settings of the screening radius and the number of screening points are the optimal values obtained by substituting various different interference images (the interference images include but are not limited to leaf interference images, small animal interference images, and personnel intrusion images, etc.) into the radius denoising function for multiple iterative tests.

[0093] Step 203: Use the point cloud data to be analyzed and the historical point cloud data as the dynamic point cloud data; wherein, the dynamic point cloud data includes multiple dynamic points.

[0094] In the embodiment of the present invention, the point cloud data to be analyzed and the historical point cloud data are used as the dynamic point cloud data, and each point in the dynamic point cloud data is determined as a dynamic point.

[0095] Step 204: Determine the corresponding dynamic intrusion points based on the dynamic points, the preset dynamic intrusion distance, and the dynamic point cloud data.

[0096] Further, step 204 includes the following sub-steps:

[0097] S11: Index the nearest point corresponding to the dynamic point from the dynamic point cloud data.

[0098] In the embodiment of the present invention, for each dynamic point in the dynamic point cloud data, the center point in the dynamic point cloud data is selected as the origin to establish a three-dimensional coordinate system, and an index library is constructed using the dynamic point cloud data. The dynamic point is input into the index library to index the corresponding nearest point.

[0099] S12: Calculate the distance value between the nearest point and the dynamic point.

[0100] In the embodiment of the present invention, according to the coordinates of the nearest point and the coordinates of the dynamic point, the distance value between the nearest point and the dynamic point is calculated.

[0101] S13: Determine whether the distance value is greater than the preset dynamic intrusion distance.

[0102] In the embodiment of the present invention, it is determined whether the distance value is greater than the preset dynamic intrusion distance.

[0103] S14: When the distance value is greater than the dynamic intrusion distance, the dynamic point is determined as a dynamic intrusion point.

[0104] In an embodiment of the present invention, if the distance value is greater than the dynamic intrusion distance, the dynamic point is determined as a dynamic intrusion point; if the distance value is less than or equal to the dynamic intrusion distance, the dynamic point is removed.

[0105] S11 - S14. In another embodiment of the present invention, a kd - tree is created based on the dynamic point cloud data for quickly finding the nearest point of the dynamic point. Traverse each dynamic point in the first n times, obtain the distance value d from each dynamic point to the nearest point in the kd - tree, and encapsulate it into a specific formula, specifically:

[0106]

[0107] O = {q ∈ W | Count(q, R s ) > K s};

[0108] where W is the dynamic intrusion point data, O is the dynamic intrusion point cloud data, q x is the abscissa of the dynamic point, q y is the ordinate of the dynamic point, q near*x is the abscissa of the nearest point, q near*y is the ordinate of the nearest point, d d is the dynamic intrusion distance, is the dynamic point cloud data, and q is the dynamic point.

[0109] Step 205: Use all dynamic intrusion points as the dynamic intrusion point cloud data.

[0110] In an embodiment of the present invention, all dynamic intrusion points are used as the dynamic intrusion point cloud data.

[0111] It is worth mentioning that the obtained dynamic intrusion point cloud data can also be processed using a radius denoising function to delete outlier dynamic intrusion points.

[0112] Step 206: Perform fusion processing on the static intrusion point cloud data and the dynamic intrusion point cloud data to generate fused intrusion point cloud data.

[0113] In an embodiment of the present invention, the static intrusion point cloud data and the dynamic intrusion point cloud data are fused to obtain fused intrusion point cloud data.

[0114] It should be noted that before fusing the static intrusion point cloud data and the dynamic intrusion point cloud data, point cloud pre - processing such as cropping and filtering is performed on the static intrusion point cloud data and the dynamic intrusion point cloud data.

[0115] Step 207: Based on the comparison result of the pre - obtained static point cloud data and the fused intrusion point cloud data, determine whether to activate the alarm.

[0116] Further, step 207 includes the following sub - steps:

[0117] S21. Calculate the number of different points between the pre-acquired static point cloud data and the fused intrusion point cloud data.

[0118] In the embodiment of the present invention, according to the constructed three-dimensional coordinate system, the number of different points between the pre-acquired static point cloud data and the fused intrusion point cloud data is obtained.

[0119] S22. Determine whether the number of different points is greater than a preset warning quantity threshold.

[0120] In the embodiment of the present invention, determine whether the number of different points is greater than a preset warning quantity threshold.

[0121] S23. If the number of different points is greater than the warning quantity threshold, activate the alarm 3.

[0122] In the embodiment of the present invention, when the number of different points is greater than the warning quantity threshold, it is determined that there is an external object intrusion currently, and the alarm 3 of the virtual fence device is activated.

[0123] S24. If the number of different points is less than or equal to the warning quantity threshold, do not activate the alarm 3.

[0124] In the embodiment of the present invention, when the number of different points is less than or equal to the warning quantity threshold, it is determined that there is no external object intrusion currently, and the alarm 3 is not activated.

[0125] It should be noted that the processing unit 1 is communicatively connected to the control terminal, and the point cloud processed in real time can be used for visualization according to the instructions of the control terminal. The static intrusion point cloud data and the fused intrusion point cloud data are sent to the control terminal through the UDP protocol, and then the rendering component in the control terminal renders the static intrusion point cloud data and the fused intrusion point cloud data, and displays them in different colors on the page of the control terminal.

[0126] Please refer to Figure 3 , the virtual fence device includes a lidar 2, a processing unit 1, a power supply 4, a main body, and an alarm 3. The lidar 2 is located on the top of the main body, and the lidar 2 is rotatably connected to the main body. The objects on the horizontal 360° rotation scanning plane are scanned continuously, and the scanning information is sent to the processing unit 1 continuously. The processing unit 1 is fixedly installed inside the main body and is connected to the lidar 2 and the alarm 3. The power supply 4 is connected to the processing unit 1 and supplies power to the lidar 2 and the alarm 3 through the processing unit 1.

[0127] In an embodiment of the present invention, when receiving the point cloud data to be analyzed collected by the lidar 2, multiple historical point cloud data corresponding to the point cloud data to be analyzed are obtained, the point cloud data to be analyzed is input into a preset radius denoising function to generate static intrusion point cloud data, and corresponding dynamic intrusion point cloud data is determined according to the point cloud data to be analyzed, the historical point cloud data, and a preset dynamic intrusion distance. The static intrusion point cloud data and the dynamic intrusion point cloud data are fused to generate fused intrusion point cloud data, and based on the comparison result between the pre-acquired static point cloud data and the fused intrusion point cloud data, it is determined whether to activate the alarm 3. This solves the technical problem that currently at night, due to the environmental interference at the operation site, it is difficult for non-staff to clearly see the safety warning signs placed, and warnings cannot be given in a timely manner, reducing the safety of power operations. By combining and analyzing the static intrusion point cloud data and the dynamic intrusion point cloud data, false alarms of the device caused by external interference are avoided, and the reliability of the virtual fence device operation is improved.

[0128] Please refer to Figure 4 , Figure 4 which is a structural block diagram of a power operation safety warning system provided in Embodiment 3 of the present invention.

[0129] A power operation safety warning system provided by the present invention is applied to the processing unit 1 of the virtual fence device and the lidar 2 and the alarm 3 connected to the processing unit 1, and includes:

[0130] An acquisition module 301, configured to obtain multiple historical point cloud data corresponding to the point cloud data to be analyzed when receiving the point cloud data to be analyzed collected by the lidar 2;

[0131] A denoising module 302, configured to input the point cloud data to be analyzed into a preset radius denoising function to generate static intrusion point cloud data;

[0132] A dynamic intrusion point cloud acquisition module 303, configured to determine corresponding dynamic intrusion point cloud data according to the point cloud data to be analyzed, the historical point cloud data, and a preset dynamic intrusion distance;

[0133] A fusion module 304, configured to fuse the static intrusion point cloud data and the dynamic intrusion point cloud data to generate fused intrusion point cloud data;

[0134] An analysis module 305, configured to determine whether to activate the alarm 3 based on the comparison result between the pre-acquired static point cloud data and the fused intrusion point cloud data.

[0135] Further, the radius denoising function is specifically:

[0136]

[0137] S = {p ∈ S cut |Count(p, Rs ) > K s};

[0138] Among them, S cut is the intermediate point cloud data, S is the static intrusion point cloud data, S raw is the point cloud data to be analyzed, p is the point to be analyzed in the point cloud data to be analyzed, x is the abscissa of the point to be analyzed, y is the ordinate of the point to be analyzed, R cut is the interception radius, R s is the screening radius, K s is the number of screening points.

[0139] Furthermore, the dynamic intrusion point cloud acquisition module includes:

[0140] The dynamic point cloud acquisition sub-module is used to use the point cloud data to be analyzed and the historical point cloud data as the dynamic point cloud data; among them, the dynamic point cloud data includes multiple dynamic points;

[0141] The dynamic intrusion point acquisition sub-module is used to determine the corresponding dynamic intrusion points based on the dynamic points, the preset dynamic intrusion distance, and the dynamic point cloud data;

[0142] The dynamic intrusion point cloud sub-module is used to use all the dynamic intrusion points as the dynamic intrusion point cloud data.

[0143] Furthermore, the dynamic intrusion point acquisition sub-module includes:

[0144] The indexing unit is used to index the nearest point corresponding to the dynamic point from the dynamic point cloud data;

[0145] The first analysis unit is used to calculate the distance value between the nearest point and the dynamic point;

[0146] The second analysis unit is used to judge whether the distance value is greater than the preset dynamic intrusion distance;

[0147] When the distance value is greater than the dynamic intrusion distance, the dynamic point is determined as the dynamic intrusion point.

[0148] Furthermore, the analysis module includes:

[0149] The first analysis sub-module is used to calculate the number of different points between the pre-acquired static point cloud data and the fused intrusion point cloud data;

[0150] The second analysis sub-module is used to judge whether the number of different points is greater than the preset warning quantity threshold;

[0151] If the number of different points is greater than the warning quantity threshold, the alarm 3 is activated;

[0152] If the number of different points is less than or equal to the warning quantity threshold, the alarm 3 is not activated.

[0153] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0154] In several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be in electrical, mechanical, or other forms.

[0155] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0156] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments or equivalently replace some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A power operation safety warning method, characterized in that, A processing unit applied to a virtual fence device, a lidar and an alarm connected to the processing unit, comprising: When receiving the point cloud data to be analyzed collected by the lidar, obtaining a plurality of historical point cloud data corresponding to the point cloud data to be analyzed; Inputting the point cloud data to be analyzed into a preset radius denoising function to generate static intrusion point cloud data; Determining corresponding dynamic intrusion point cloud data according to the point cloud data to be analyzed, the historical point cloud data and a preset dynamic intrusion distance; Performing fusion processing on the static intrusion point cloud data and the dynamic intrusion point cloud data to generate fusion intrusion point cloud data; Judging whether to activate the alarm based on the comparison result between the previously obtained static point cloud data and the fusion intrusion point cloud data; The radius denoising function is specifically: ； ； Among them, is the middle point cloud data, is the static intrusion point cloud data, is the point cloud data to be analyzed, is the point to be analyzed in the point cloud data to be analyzed, is the abscissa of the point to be analyzed, is the ordinate of the point to be analyzed, is the intercept radius, is the screening radius, is the number of screening points.

2. The power operation safety warning method according to claim 1, wherein The step of determining corresponding dynamic intrusion point cloud data according to the point cloud data to be analyzed, the historical point cloud data and a preset dynamic intrusion distance includes: Using the point cloud data to be analyzed and the historical point cloud data as dynamic point cloud data; wherein, the dynamic point cloud data includes a plurality of dynamic points; Determining corresponding dynamic intrusion points based on the dynamic points, a preset dynamic intrusion distance and the dynamic point cloud data; Using all the dynamic intrusion points as dynamic intrusion point cloud data.

3. The power operation safety warning method according to claim 2, wherein The step of determining corresponding dynamic intrusion points based on the dynamic points, a preset dynamic intrusion distance and the dynamic point cloud data includes: Indexing the nearest point corresponding to the dynamic point from the dynamic point cloud data; Calculating the distance value between the nearest point and the dynamic point; Judging whether the distance value is greater than a preset dynamic intrusion distance; When the distance value is greater than the dynamic intrusion distance, determining the dynamic point as a dynamic intrusion point.

4. The power operation safety warning method according to claim 1, wherein The step of judging whether to activate the alarm based on the comparison result between the previously obtained static point cloud data and the fusion intrusion point cloud data includes: Calculating the number of different points between the previously obtained static point cloud data and the fusion intrusion point cloud data; Judging whether the number of different points is greater than a preset warning quantity threshold; If the number of different points is greater than the warning quantity threshold, activating the alarm; If the number of different points is less than or equal to the warning quantity threshold, not activating the alarm.

5. A power operation safety warning system, characterized in that, A processing unit applied to a virtual fence device, a lidar and an alarm connected to the processing unit, comprising: An acquisition module, configured to obtain a plurality of historical point cloud data corresponding to the point cloud data to be analyzed when receiving the point cloud data to be analyzed collected by the lidar; A denoising module, configured to input the point cloud data to be analyzed into a preset radius denoising function to generate static intrusion point cloud data; A dynamic intrusion point cloud acquisition module, configured to determine corresponding dynamic intrusion point cloud data according to the point cloud data to be analyzed, the historical point cloud data and a preset dynamic intrusion distance; A fusion module, configured to perform fusion processing on the static intrusion point cloud data and the dynamic intrusion point cloud data to generate fusion intrusion point cloud data; An analysis module, configured to determine whether to activate the alarm based on the comparison result between the pre-acquired static point cloud data and the fused intrusion point cloud data; The radius denoising function is specifically: ； ； Among them, is the middle point cloud data, is the static intrusion point cloud data, is the point cloud data to be analyzed, is the point to be analyzed in the point cloud data to be analyzed, is the abscissa of the point to be analyzed, is the ordinate of the point to be analyzed, is the intercept radius, is the screening radius, is the number of screening points.

6. The electric operation safety warning system according to claim 5, wherein The dynamic intrusion point cloud acquisition module includes: A dynamic point cloud acquisition sub-module, configured to use the point cloud data to be analyzed and the historical point cloud data as dynamic point cloud data; wherein, the dynamic point cloud data includes a plurality of dynamic points; A dynamic intrusion point acquisition sub-module, configured to determine corresponding dynamic intrusion points based on the dynamic points, a preset dynamic intrusion distance, and the dynamic point cloud data; A dynamic intrusion point cloud sub-module, configured to use all the dynamic intrusion points as dynamic intrusion point cloud data.

7. The power operation safety warning system according to claim 6, wherein The dynamic intrusion point acquisition sub-module includes: An indexing unit, configured to index the nearest point corresponding to the dynamic point from the dynamic point cloud data; A first analysis unit, configured to calculate the distance value between the nearest point and the dynamic point; A second analysis unit, configured to determine whether the distance value is greater than a preset dynamic intrusion distance; When the distance value is greater than the dynamic intrusion distance, the dynamic point is determined as a dynamic intrusion point.

8. The electric power operation safety warning system according to claim 5, wherein The analysis module includes: A first analysis sub-module, configured to calculate the number of different points between the pre-acquired static point cloud data and the fused intrusion point cloud data; A second analysis sub-module, configured to determine whether the number of different points is greater than a preset warning quantity threshold; If the number of different points is greater than the warning quantity threshold, the alarm is activated; If the number of different points is less than or equal to the warning quantity threshold, the alarm is not activated.

Images