Tension Clamp Detection and Control Method and Device for an Intelligent Robot

Abstract

The present invention relates to the technical field of robot applications, and specifically discloses a detection and control method and device for strain clamps of an intelligent robot. The method includes obtaining the installation position of the strain clamp, clustering the strain clamps according to the installation position to obtain a detection area; performing an enumerative arrangement on the detection area, taking each arrangement scheme as a detection scheme, and synchronously generating a quantity sequence for each detection scheme; obtaining the movement path of each detection scheme, calculating the energy consumption, and selecting an optimal scheme according to the energy consumption and the quantity sequence; receiving the detection data fed back by the robot, generating a control instruction based on the optimal scheme and the detection data fed back by the robot, and sending it to the robot; the detection data is an image containing the shooting time. The present invention optimizes the robot path planning process and data uploading process, greatly reducing the energy consumption of the robot, improving the battery life, and enabling it to detect the strain clamps in the actual use stage faster.

Description

Technical Field

[0001] The present invention relates to the technical field of robot applications, and specifically to a detection and control method and device for strain clamps of intelligent robots. Background Art

[0002] A strain clamp is a type of electrical hardware used in overhead power lines, mainly for fixing conductors and withstanding tension to ensure the stability and reliability of power lines. It is commonly used at the terminals, corners, crossings, and branch positions of power transmission and distribution lines.

[0003] In addition to being detected during the production stage, strain clamps also need to be detected during actual use. Since their installation positions are all on high-voltage lines, the detection process during actual use is extremely dangerous. However, due to the progress of robot technology, the danger level of the detection process during actual use has been greatly reduced. Therefore, in the prior art, robots can be used to detect strain clamps. When using a robot to detect strain clamps, the battery life problem is the biggest issue. If the battery life can be improved, then the extra power can be used to increase the speed, thereby increasing the detection speed and performing one or more additional detection cycles. Summary of the Invention

[0004] The purpose of the present invention is to provide a detection and control method and device for strain clamps of intelligent robots to solve the problems raised in the above background art.

[0005] To achieve the above purpose, the present invention provides the following technical solutions:

[0006] A detection and control method for strain clamps of an intelligent robot, the method comprising:

[0007] Obtain the installation position of the strain clamp, cluster the strain clamps according to the installation position to obtain a detection area; the clustering process is a three-dimensional clustering process;

[0008] Enumeratively arrange the detection areas, take each arrangement scheme as a detection scheme, and synchronously generate a quantity sequence for each detection scheme; the dimension of the quantity sequence is the same as the total number of detection areas, and each element value in the quantity sequence represents the number of clamps in the corresponding detection area in the arrangement scheme;

[0009] Obtain the movement path of each detection scheme, calculate the energy consumption, and select the optimal scheme according to the energy consumption and the quantity sequence; wherein, the energy consumption is the total energy consumption of the robot working along the movement path;

[0010] Receive the detection data fed back by the robot, generate a control instruction based on the optimal scheme and the detection data fed back by the robot, and send it to the robot; the detection data is an image containing the shooting time.

[0011] As a further solution of the present invention, the steps of obtaining the installation position of the strain clamp, clustering the strain clamps according to the installation position, and obtaining the detection area include:

[0012] Query the installation position of the strain clamp in the filing database;

[0013] Calculate the spatial distance between any two strain clamps;

[0014] Query the power parameters of the lines where any two strain clamps are located, and adjust the spatial distance according to the difference in power parameters;

[0015] Cluster the strain clamps according to the adjusted spatial distance to construct the detection area.

[0016] As a further solution of the present invention, the steps of obtaining the movement path of each detection scheme, calculating the energy consumption, and selecting the optimal scheme according to the energy consumption and the quantity sequence include:

[0017] Obtain the movement path of each detection scheme based on a preset path planning algorithm;

[0018] Obtain the total height change and the total path length of the movement path, and calculate the energy consumption according to the total height change and the total path length; the energy consumption is proportional to the total height change, and the energy consumption is proportional to the total path length;

[0019] Read the quantity sequence of the detection scheme, calculate the inverse ordinal number of the quantity sequence, and calculate the final score of the detection scheme according to the energy consumption and the inverse ordinal number;

[0020] Select the detection scheme with the highest final score as the optimal scheme;

[0021] The calculation process of the final score is:

[0022] ; where represents the final score, and are preset constants, both positive values, and are preset correction coefficients, both positive values, is the total number of coordinate points in the movement path, represents the th coordinate point in the movement path, ; represents the absolute value of the height difference corresponding to the th coordinate point in the movement path, takes the value: when , ; when , ; represents the height corresponding to the th coordinate point, represents the height corresponding to the previous coordinate point of the th coordinate point; represents the total path length; represents the inversion number.

[0023] As a further solution of the present invention: The steps of generating a control instruction based on the optimal solution, sending it to the robot, and receiving the detection data fed back by the robot include:

[0024] Sending the optimal solution to the robot, and obtaining the position of the robot according to a preset time period; after receiving the optimal solution, the robot locally adjusts its movement direction based on the optimal solution;

[0025] Comparing the position with the optimal solution, generating a superior control instruction, and sending it to the robot; the time period is a dynamic period and is related to the comparison result of the position and the optimal solution;

[0026] Receiving the detection data fed back by the robot, identifying the detection data, and generating a speed control instruction for the robot according to the identification result and sending it to the robot.

[0027] As a further solution of the present invention: The steps of receiving the detection data fed back by the robot, generating a control instruction based on the optimal solution and the detection data fed back by the robot, and sending it to the robot include:

[0028] Receiving the detection data fed back by the robot, and calculating the feedback frequency according to the shooting time of the detection data;

[0029] Performing superior identification on the detection data. When the superior identification result is that there is no abnormality, determining a speed adjustment coefficient according to the feedback frequency as a speed control instruction and sending it to the robot;

[0030] When the superior identification result is that there is an abnormality, querying the position of the robot at the shooting time, and generating a superior control instruction with the robot position as the target point and sending it to the robot;

[0031] Receiving the images fed back by the robot at the passing points. When the number of images reaches a preset number threshold, generating a continue detection instruction and sending it to the robot;

[0032] Among them, during the movement of the robot, local recognition is performed on the detection data, and it is determined whether to feedback an image according to the local recognition result; the local recognition adopts a convolutional recognition scheme, and the convolutional kernel adopts the convolutional features of the image of the strain clamp under a preset standard state; the upper-level recognition adopts a neural network model, the features of the samples of the neural network model are the images of the strain clamp, and the labels of the samples are the abnormal types; the order of magnitude of the number of samples is not less than the preset magnitude threshold.

[0033] As a further solution of the present invention: the method further includes:

[0034] On the robot side, the endurance duration is obtained in real time. When the endurance duration is less than the preset endurance threshold, other robots are queried within a preset distance range. After inserting its own identifier into the feedback result, the feedback result is fed back to other robots; when the identifier of the feedback result received by the master end is different from the sender, a warning message pointing to the robot corresponding to the identifier is generated.

[0035] The technical solution of the present invention also provides a detection control device for the strain clamp of an intelligent robot, and the device includes:

[0036] A detection area creation module, configured to obtain the installation position of the strain clamp, and cluster the strain clamps according to the installation position to obtain a detection area; the clustering process is a three-dimensional clustering process;

[0037] A scheme enumeration module, configured to perform an enumerative arrangement on the detection area, use each arrangement scheme as a detection scheme, and synchronously generate a quantity sequence for each detection scheme; the dimension of the quantity sequence is the same as the total number of detection areas, and each element value in the quantity sequence represents the number of clamps in the detection area corresponding to the position in the arrangement scheme;

[0038] A scheme selection module, configured to obtain the movement path of each detection scheme, calculate the energy consumption, and select the optimal scheme according to the energy consumption and the quantity sequence; wherein, the energy consumption is the total energy consumption of the robot working along the movement path;

[0039] An instruction generation and sending module, configured to receive the detection data fed back by the robot, generate a control instruction based on the optimal scheme and the detection data fed back by the robot, and send it to the robot; the detection data is an image containing the shooting time.

[0040] As a further solution of the present invention: the detection area creation module includes:

[0041] A position query unit, configured to query the installation position of the strain clamp in the record database;

[0042] A distance calculation unit, configured to calculate the spatial distance between any two strain clamps;

[0043] A distance update unit for querying the power parameters of the lines where any two strain clamps are located and adjusting the spatial distance according to the differences in the power parameters;

[0044] A clustering execution unit for clustering the strain clamps according to the adjusted spatial distance and constructing a detection area.

[0045] As a further solution of the present invention: The scheme selection module includes:

[0046] A path determination unit for obtaining the movement path of each detection scheme based on a preset path planning algorithm;

[0047] An energy consumption calculation unit for obtaining the total height change and the total path length of the movement path and calculating the energy consumption according to the total height change and the total path length; the energy consumption is proportional to the total height change and the energy consumption is proportional to the total path length;

[0048] A score calculation unit for reading the number sequence of the detection scheme, calculating the inversion number of the number sequence, and calculating the final score of the detection scheme according to the energy consumption and the inversion number;

[0049] A score application unit for selecting the detection scheme with the highest final score as the optimal scheme;

[0050] The calculation process of the final score is as follows:

[0051] ; where represents the final score, and are preset constants, both positive values, and are preset correction factors, both positive values, is the total number of coordinate points in the movement path, represents the th coordinate point in the movement path, ; represents the absolute value of the height difference corresponding to the th coordinate point in the movement path, takes the value: when , ; when , ; represents the height corresponding to the th coordinate point, represents the height corresponding to the previous coordinate point of the th coordinate point; represents the total path length; represents the inversion number.

[0052] As a further solution of the present invention: The instruction generation and sending module includes:

[0053] A solution sending unit, configured to send the optimal solution to the robot and obtain the position of the robot according to a preset time period; after receiving the optimal solution, the robot locally adjusts its moving direction based on the optimal solution;

[0054] A comparison and adjustment unit, configured to compare the position with the optimal solution, generate a higher-level control instruction, and send it to the robot; the time period is a dynamic period and is related to the comparison result of the position and the optimal solution;

[0055] A speed adjustment unit, configured to receive the detection data fed back by the robot, identify the detection data, generate a speed control instruction for the robot according to the identification result, and send it to the robot.

[0056] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention optimizes the robot path planning process and data uploading process, greatly reducing the energy consumption of the robot, improving the battery life, and enabling it to detect the strain clamp in the actual use stage faster. Description of the Drawings

[0057] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention.

[0058] Figure 1 It is a flow block diagram of the detection control method for the strain clamp of an intelligent robot.

[0059] Figure 2 It is a block diagram of the composition structure of the detection control device for the strain clamp of an intelligent robot. Detailed Embodiments

[0060] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention more clearly understood, the following further details the present invention in conjunction with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0061] Figure 1 It is a flow block diagram of the detection control method for the strain clamp of an intelligent robot. In the embodiments of the present invention, a detection control method for the strain clamp of an intelligent robot, the method includes:

[0062] Step S100: Obtain the installation position of the strain clamp, cluster the strain clamp according to the installation position to obtain a detection area; the clustering process is a three-dimensional clustering process;

[0063] The strain clamp belongs to the infrastructure of the power line, and its installation location is pre-filed. Obtain the installation location of the strain clamp, and cluster the strain clamps according to the installation location, so that the similar strain clamps can be classified into one category, and then a detection area is constructed. The purpose of this process is that the detection process of this application is an image-based detection process. Taking one image may include multiple strain clamps. Therefore, clustering the strain clamps before taking the detection image can reduce the number of images taken to a certain extent. In this application, when the number of images taken becomes smaller, the battery life of the robot will become longer.

[0064] It should be noted that since the strain clamp is installed in the air, its installation location is a three-dimensional coordinate, and the clustering process is also a clustering process in three-dimensional space.

[0065] Step S200: Enumeratively arrange the detection areas, take each arrangement scheme as a detection scheme, and synchronously generate the quantity sequence of each detection scheme; the dimension of the quantity sequence is the same as the total number of detection areas, and each element value in the quantity sequence represents the number of clamps in the detection area corresponding to the position in the arrangement scheme.

[0066] For an intelligent robot, the number of detection areas within its inspection range is limited. By enumeratively arranging the detection areas, multiple detection schemes can be obtained. Generally speaking, a detection scheme is the order in which the detection areas are detected. Each arrangement scheme is a detection scheme. The arrangement scheme and the detection scheme are two names for the same content. At the same time, for each detection scheme, it is the sequential arrangement of the detection areas. Query the number of clamps in each detection area, and arrange the number of clamps in the order of the detection areas to obtain the quantity sequence. From the above content, it can be seen that a detection scheme also corresponds to a quantity sequence.

[0067] Step S300: Obtain the movement path of each detection scheme, calculate the energy consumption, and select the optimal scheme according to the energy consumption and the quantity sequence; where the energy consumption is the total energy consumption of the robot working along the movement path.

[0068] Furthermore, obtaining the movement path of each detection scheme is not complicated. It is the path passing through each detection area, and the existing navigation service can meet this requirement. On this basis, calculate the energy consumption of each detection scheme. Combining these two parameters, the energy consumption and the quantity sequence, the detection schemes can be evaluated, and finally the optimal scheme is selected from multiple detection schemes; where the energy consumption is the total energy consumption of the robot working along the movement path. In addition, the "optimal" in the optimal scheme of this application refers to the optimal under certain conditions. The optimal under different conditions is different, and it is not an absolute optimal either, but only a relative optimal that meets certain conditions.

[0069] Step S400: Receive the detection data fed back by the robot, generate a control instruction based on the optimal solution and the detection data fed back by the robot, and send it to the robot; the detection data is an image containing the shooting time.

[0070] During the movement of the robot, it will obtain detection data. There are two architectures for the acquisition process. One is to pre-determine the shooting points according to the positions of the strain clamps in the detection area. When the robot reaches these points, the camera is activated to capture images. The other is to turn on the camera in real time, first record videos under low-definition conditions, and detect in real time whether there are strain clamps. If there are, then improve the accuracy. Although this method has a slightly higher energy consumption, it can record the working process of the robot, and when special situations occur, the staff can conduct a retrospective analysis afterwards.

[0071] After receiving the detection data fed back by the robot, generate a control instruction jointly based on the optimal solution and the detection data fed back by the robot, and send it to the robot to control the robot to complete the detection work of the strain clamps.

[0072] Regarding step S100, the steps of obtaining the installation positions of the strain clamps and clustering the strain clamps according to the installation positions to obtain the detection area include:

[0073] Query the installation positions of the strain clamps in the record database.

[0074] Calculate the spatial distance between any two strain clamps.

[0075] Query the power parameters of the lines where any two strain clamps are located, and adjust the spatial distance according to the differences in the power parameters.

[0076] Cluster the strain clamps according to the adjusted spatial distance to construct the detection area.

[0077] The above content describes the creation process of the detection area. Query the installation locations of strain clamps in the record database, calculate the spatial distance between any two strain clamps. At the same time, this application will also query the electrical parameters of the lines where any two strain clamps are located. The electrical parameters are a superordinate concept, and some easily measurable electrical parameters can be selected, such as DC resistance, inductance, line impedance, DC resistance, AC resistance, inductance, and capacitance, etc. One or several of these parameters can also be selected, which is specifically determined by the staff according to the situation. In the technical solution of the present invention, calculate the difference in the electrical parameters of the lines where two strain clamps are located, and then adjust the spatial distance. The greater the difference, the greater the adjusted spatial distance. Finally, cluster the strain clamps according to the adjusted spatial distance. The clustering scheme can adopt the DBSCAN clustering scheme, etc. In addition, there is one more step from the clustering process to the construction of the detection area. The clustering process classifies the strain clamps into one category and creates an area containing the same type of strain clamps, which is the detection area. The simplest construction process of the detection area is to create a circular area containing the same type of strain clamps, and the diameter is A times the maximum distance among the strain clamps of the same type, where A is greater than one to ensure that the circular area can cover all the same type of strain clamps.

[0078] Regarding step S300, the steps of obtaining the movement path of each detection scheme, calculating the energy consumption, and selecting the optimal scheme according to the energy consumption and the quantity sequence include:

[0079] Obtain the movement path of each detection scheme based on a preset path planning algorithm;

[0080] Obtain the total height change and the total path length of the movement path, and calculate the energy consumption according to the total height change and the total path length. The energy consumption is proportional to the total height change, and the energy consumption is proportional to the total path length;

[0081] Read the quantity sequence of the detection scheme, calculate the inverse ordinal number of the quantity sequence, and calculate the final score of the detection scheme according to the energy consumption and the inverse ordinal number;

[0082] Select the detection scheme with the highest final score as the optimal scheme;

[0083] In an example of the technical solution of the present invention, the selection process of all enumerated detection schemes is described. For any detection scheme, the detection scheme is the arrangement of detection areas. By using an existing path planning algorithm to obtain the path passing through each detection area, the detection scheme is converted into a movement path. For the movement path of each detection scheme, obtain the total height change and the total path length of the movement path, calculate the energy consumption according to the total height change and the total path length. At the same time, read the quantity sequence of the detection scheme, calculate the inverse ordinal number of the quantity sequence, and calculate the final score of the detection scheme according to the energy consumption and the inverse ordinal number. Select the detection scheme with the highest final score as the optimal scheme.

[0084] The calculation process of the final score is:

[0085] ; In the formula, Indicates the final rating. and are preset constants, all of which are positive values. and is the preset correction coefficient, which is a positive value. is the total number of coordinate points in the motion path, Indicates the first The characteristic value of the coordinate point, ; Indicates the first The absolute value of the height difference corresponding to the coordinate points, The value of is: hour, ;when , ; Indicates The height corresponding to the coordinate point, Indicates The height corresponding to the previous coordinate point of the coordinate point; Indicates the total length of the path; Indicates a reverse number.

[0086] The final score is inversely proportional to the total height change and the total path length. The total height change is the total number of times the height change sign changes during the motion path, corresponding to The sign change of the height change indicates how many times the robot has switched uphill and downhill. The uphill and downhill switching indicates that the robot needs to adjust the mode, which brings a certain amount of energy consumption. Therefore, the greater the total height change, the higher the energy consumption and the lower the score.

[0087] Correspondingly, the longer the total path length is, the higher the energy consumption is, and the lower the score is; further, the reverse number represents the quantity characteristics of the tension clamps. The meaning of the scheme with the largest reverse number is that the arrangement order of the detection areas is the descending order of the number of tension clamps, and the number decreases successively. In the technical scheme of the present invention, it is believed that the more the number of tension clamps is, the more important the corresponding detection area is, and the more it needs to be detected first. That is, the larger the reverse number is, the more the detection areas with more numbers are closer to the front and the higher the score is.

[0088] Regarding step S400, the steps of generating control instructions based on the optimal solution, sending the instructions to the robot, and receiving the detection data fed back by the robot include:

[0089] Send the optimal solution to the robot and obtain the position of the robot according to a preset time period; after receiving the optimal solution, the robot locally adjusts its movement direction based on the optimal solution.

[0090] Compare the position with the optimal solution, generate a higher-level control instruction, and send it to the robot; the time period is a dynamic period and is related to the comparison result of the position and the optimal solution.

[0091] In an example of the technical solution of the present invention, the recognition application process of the detection data and the control process of the robot are defined. The optimal solution is sent to the robot. After receiving the optimal solution, the robot locally adjusts its movement direction based on the optimal solution. At the same time, the master terminal obtains the position of the robot according to a preset time period, that is, obtains the position of the robot once every once in a while. This requires the help of a locator built into the robot. The locator belongs to the basic equipment of the robot. The master terminal compares the position with the optimal solution, can determine whether there is an offset, and then generates a higher-level control instruction to adjust the local control process of the robot; in this process, the robot does not need to upload its position in real time, reducing energy consumption and improving battery life.

[0092] In addition, the time period adopts a dynamic period, which is related to the comparison result of the position and the optimal solution. Specifically, it is related to the generation interval of the higher-level control instruction. The smaller the generation interval, the smaller the dynamic period.

[0093] Furthermore, the master terminal also needs to receive the detection data fed back by the robot, identify the detection data, and generate a speed control instruction for the robot according to the identification result and send it to the robot.

[0094] The steps of receiving the detection data fed back by the robot, generating a control instruction based on the optimal solution and the detection data fed back by the robot, and sending it to the robot include:

[0095] Receive the detection data fed back by the robot and calculate the feedback frequency according to the shooting time of the detection data;

[0096] Perform a higher-level identification on the detection data. When the higher-level identification result is that there is no abnormality, determine a speed adjustment coefficient according to the feedback frequency as the speed control instruction and send it to the robot;

[0097] When the higher-level identification result is that there is an abnormality, query the position of the robot at the shooting time, use the position of the robot as the target point to generate a higher-level control instruction, and send it to the robot;

[0098] Receive the image fed back by the robot at the waypoint. When the number of images reaches a preset number threshold, generate a continue detection instruction and send it to the robot.

[0099] Specifically, during the movement of the robot, local recognition is performed on the detection data, and it is determined whether to feedback an image according to the local recognition result. The local recognition adopts a convolutional recognition scheme, and the convolutional kernel adopts the convolutional features of the image of the strain clamp under a preset standard state. When the robot captures an image, convolutional recognition is performed on the image. The way of convolutional recognition is to traverse the image according to the preset convolutional kernel. When the convolutional kernel successfully matches a certain area in the image, it is considered that the strain clamp is in the standard state. It should be noted that only when the strain clamp is completely in the standard state, the robot will determine it as standard through the image. If there are some phenomena such as occlusion or unclear, it will be determined as non-standard, and the image (detection data) will be uploaded to the master terminal.

[0100] Receive the detection data fed back by the robot, calculate the feedback frequency according to the shooting time of the detection data. The shooting time is a label of the detection data, which is generated and inserted into the image when the robot acquires the image. The master terminal performs upper-level recognition on the detection data. When the upper-level recognition result shows no abnormality, determine the speed adjustment coefficient according to the feedback frequency as the speed control instruction and send it to the robot. The upper-level recognition adopts a neural network model. The features of the samples of the neural network model are the images of the strain clamp, and the labels of the samples are the types of abnormalities. The order of magnitude of the number of samples is not less than the preset magnitude threshold, and the recognition depth and recognition accuracy of the neural network model are higher.

[0101] In addition, when the upper-level recognition result is recognized and shows no abnormality, the speed adjustment coefficient also needs to be determined according to the feedback frequency. The speed adjustment coefficient is inversely proportional to the feedback frequency. The lower the feedback frequency, the more images that the robot locally recognizes as completely standard. At this time, the robot does not need to perform multiple interaction processes with the master terminal, has lower energy consumption and longer battery life, can increase the speed, and complete the detection task faster, that is, the speed adjustment coefficient is larger.

[0102] Furthermore, when the upper-level recognition result shows an abnormality, query the position of the robot at the shooting time to determine which position is abnormal. At this time, use the robot position as the target point to generate an upper-level control instruction and send it to the robot to control the robot to acquire more images. When the number of images is sufficient, generate a continue detection instruction and send it to the robot. The continue detection instruction is used to control the robot to move according to the original rules.

[0103] As a preferred embodiment of the technical solution of the present invention, the method further includes:

[0104] On the robot side, the remaining battery life is obtained in real time. When the remaining battery life is less than the preset battery threshold, other robots are queried within the preset distance range. After inserting its own identifier into the feedback result, the feedback result is sent back to other robots. When the identifier of the feedback result received by the master end is different from the sender, a warning message pointing to the robot corresponding to the identifier is generated.

[0105] In an example of the technical solution of the present invention, an emergency plan is introduced. When the battery life of a certain robot is low, its detection data can be sent to the nearest robot, and the nearest robot uploads the data on its behalf (to the master end). There is no need to upload the data by itself. Sending it to the nearest robot can use a short-range connection channel such as Bluetooth, with lower energy consumption. When the identifier of the feedback result received by the master end is different from the sender, a warning message pointing to the robot corresponding to the identifier is generated to inform the administrator of the problematic robot.

[0106] It is worth mentioning that the above solution is an emergency plan. The probability of a robot having a problem is very low, and the probability of a certain robot and its receiving robot having problems at the same time is even lower. However, this probability still exists. For this, the image receiving port of the problematic machine is closed so that it cannot receive the images forwarded by other robots. When other robots fail to forward, they will select another robot as the receiver.

[0107] Figure 2 It is a block diagram of the composition structure of the strain clamp detection control device for an intelligent robot. In an embodiment of the present invention, a strain clamp detection control device for an intelligent robot, the device 10 includes:

[0108] A detection area creation module 11, configured to obtain the installation position of the strain clamp, cluster the strain clamps according to the installation position to obtain a detection area; the clustering process is a three-dimensional clustering process;

[0109] A scheme enumeration module 12, configured to perform an enumerative arrangement on the detection area, regard each arrangement scheme as a detection scheme, and synchronously generate a quantity sequence for each detection scheme; the dimension of the quantity sequence is the same as the total number of detection areas, and each element value in the quantity sequence represents the number of clamps in the detection area corresponding to the position in the arrangement scheme;

[0110] A scheme selection module 13, configured to obtain the movement path of each detection scheme, calculate the energy consumption, and select the optimal scheme according to the energy consumption and the quantity sequence; wherein, the energy consumption is the total energy consumption of the robot working along the movement path;

[0111] An instruction generation and sending module 14, configured to receive the detection data fed back by the robot, generate a control instruction based on the optimal scheme and the detection data fed back by the robot, and send it to the robot; the detection data is an image containing the shooting time.

[0112] Furthermore, the detection area creation module 11 includes:

[0113] A location query unit for querying the installation location of strain clamps in the record database;

[0114] A distance calculation unit for calculating the spatial distance between any two strain clamps;

[0115] A distance update unit for querying the power parameters of the lines where any two strain clamps are located and adjusting the spatial distance according to the difference in power parameters;

[0116] A clustering execution unit for clustering the strain clamps according to the adjusted spatial distance and constructing a detection area.

[0117] Specifically, the scheme selection module 13 includes:

[0118] A path determination unit for obtaining the movement path of each detection scheme based on a preset path planning algorithm;

[0119] An energy consumption calculation unit for obtaining the total height change and the total path length of the movement path and calculating the energy consumption according to the total height change and the total path length; the energy consumption is proportional to the total height change, and the energy consumption is proportional to the total path length;

[0120] A score calculation unit for reading the number sequence of the detection scheme, calculating the inverse ordinal number of the number sequence, and calculating the final score of the detection scheme according to the energy consumption and the inverse ordinal number;

[0121] A score application unit for selecting the detection scheme with the highest final score as the optimal scheme;

[0122] The calculation process of the final score is as follows:

[0123] ; where represents the final score, and are preset constants, both positive values, and are preset correction coefficients, both positive values, is the total number of coordinate points in the movement path, represents the th coordinate point in the movement path, ; represents the absolute value of the height difference corresponding to the th coordinate point in the movement path, The value of is: when ; when , ; represents the height corresponding to the th coordinate point, represents the height corresponding to the previous coordinate point of the th coordinate point; represents the total path length; represents the number of inversions.

[0124] Furthermore, the instruction generation and sending module 14 includes:

[0125] A solution sending unit, configured to send the optimal solution to the robot and obtain the position of the robot according to a preset time period; after receiving the optimal solution, the robot locally adjusts its movement direction based on the optimal solution;

[0126] A comparison and adjustment unit, configured to compare the position with the optimal solution, generate a superior control instruction, and send it to the robot; the time period is a dynamic period and is related to the comparison result of the position and the optimal solution;

[0127] A speed adjustment unit, configured to receive the detection data fed back by the robot, identify the detection data, and generate a speed control instruction for the robot according to the identification result, and send it to the robot.

[0128] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A detection and control method for strain clamps of an intelligent robot, characterized in that, The method includes: Obtaining the installation positions of strain clamps, clustering the strain clamps according to the installation positions to obtain a detection area; the clustering process is a three-dimensional clustering process; Enumeratively arranging the detection area, taking each arrangement scheme as a detection scheme, and synchronously generating a quantity sequence for each detection scheme; the dimension of the quantity sequence is the same as the total number of detection areas, and each element value in the quantity sequence represents the number of clamps in the detection area corresponding to the position in the arrangement scheme; Obtaining the movement path of each detection scheme, calculating the energy consumption, and selecting the optimal scheme according to the energy consumption and the quantity sequence; wherein, the energy consumption is the total energy consumption of the robot working along the movement path; Receiving the detection data fed back by the robot, generating a control instruction based on the optimal scheme and the detection data fed back by the robot, and sending it to the robot; the detection data is an image containing the shooting time.

2. The detection and control method of the strain clamp for the intelligent robot according to claim 1, wherein The steps of obtaining the installation positions of strain clamps, clustering the strain clamps according to the installation positions to obtain a detection area include: Querying the installation positions of strain clamps in the filing database; Calculating the spatial distance between any two strain clamps; Querying the power parameters of the lines where any two strain clamps are located, and adjusting the spatial distance according to the difference in power parameters; Clustering the strain clamps according to the adjusted spatial distance to construct a detection area.

3. The detection and control method of the strain clamp for the intelligent robot according to claim 1, characterized in that, The steps of obtaining the movement path of each detection scheme, calculating the energy consumption, and selecting the optimal scheme according to the energy consumption and the quantity sequence include: Obtaining the movement path of each detection scheme based on a preset path planning algorithm; Obtaining the total height change and the total path length of the movement path, and calculating the energy consumption according to the total height change and the total path length; the energy consumption is proportional to the total height change, and the energy consumption is proportional to the total path length; Reading the quantity sequence of the detection scheme, calculating the reverse order number of the quantity sequence, and calculating the final score of the detection scheme according to the energy consumption and the reverse order number; Selecting the detection scheme with the highest final score as the optimal scheme; The calculation process of the final score is: ; where, represents the final score, and are preset constants, both being positive values, and are preset correction factors, both being positive values, is the total number of coordinate points in the motion path, represents the -th eigenvalue of the coordinate points in the motion path, ; represents the absolute value of the height difference corresponding to the -th coordinate point in the motion path, takes values as: when , ; when , ; represents the height corresponding to the -th coordinate point, represents the height corresponding to the coordinate point before the -th coordinate point; represents the total path length; represents the number of inversions.

4. The detection and control method of the strain clamp for the intelligent robot according to claim 1, wherein The steps of generating a control instruction based on the optimal scheme, sending it to the robot, and receiving the detection data fed back by the robot include: Sending the optimal scheme to the robot, and obtaining the position of the robot according to a preset time period; after receiving the optimal scheme, the robot locally adjusts the movement direction based on the optimal scheme; Comparing the position with the optimal scheme, generating a superior control instruction, and sending it to the robot; the time period is a dynamic period and is related to the comparison result of the position and the optimal scheme; Receiving the detection data fed back by the robot, identifying the detection data, and generating a speed control instruction for the robot according to the identification result, and sending it to the robot.

5. The detection and control method of the strain clamp for the intelligent robot according to claim 4, characterized in that, The steps of receiving the detection data fed back by the robot, generating a control instruction based on the optimal scheme and the detection data fed back by the robot, and sending it to the robot include: Receiving the detection data fed back by the robot, and calculating the feedback frequency according to the shooting time of the detection data; Performing superior identification on the detection data, and when the superior identification result is that there is no abnormality, determining a speed adjustment coefficient according to the feedback frequency as the speed control instruction and sending it to the robot; When the upper-level recognition result indicates an anomaly, query the robot's position at the shooting time, generate an upper-level control instruction with the robot's position as the target point, and send it to the robot; Receive the images fed back by the robot at the waypoints. When the number of images reaches the preset quantity threshold, generate a continue detection instruction and send it to the robot; Among them, during the movement of the robot, local recognition is performed on the detection data, and it is determined whether to feed back images according to the local recognition result; the local recognition adopts a convolutional recognition scheme, and the convolutional kernel adopts the convolutional features of the image of the strain clamp under the preset standard state; the upper-level recognition adopts a neural network model, the feature of the sample of the neural network model is the image of the strain clamp, and the label of the sample is the anomaly type; the order of magnitude of the number of samples is not less than the preset order-of-magnitude threshold.

6. The detection and control method of the strain clamp for an intelligent robot according to claim 1, characterized in that, The method further includes: On the robot side, the battery life is obtained in real time. When the battery life is less than the preset battery-life threshold, other robots are queried within a preset distance range. After inserting its own identifier into the feedback result, the feedback result is fed back to other robots; when the identifier of the feedback result received by the master end is different from the sender, a warning message pointing to the robot corresponding to the identifier is generated.

7. A detection and control device for a strain clamp of an intelligent robot, characterized in that, The device includes: A detection area creation module, configured to obtain the installation position of the strain clamp, cluster the strain clamps according to the installation position to obtain a detection area; the clustering process is a three-dimensional clustering process; A scheme enumeration module, configured to perform an enumerative arrangement on the detection area, regard each arrangement scheme as a detection scheme, and synchronously generate a quantity sequence for each detection scheme; the dimension of the quantity sequence is the same as the total number of detection areas, and each element value in the quantity sequence represents the number of strain clamps in the detection area at the corresponding position in the arrangement scheme; A scheme selection module, configured to obtain the movement path of each detection scheme, calculate the energy consumption, and select the optimal scheme according to the energy consumption and the quantity sequence; wherein, the energy consumption is the total energy consumption of the robot working along the movement path; An instruction generation and sending module, configured to receive the detection data fed back by the robot, generate a control instruction based on the optimal scheme and the detection data fed back by the robot, and send it to the robot; the detection data is an image containing the shooting time.

8. The tension clamp detection and control device for the intelligent robot according to claim 7, characterized in that, The detection area creation module includes: A position query unit, configured to query the installation position of the strain clamp in the record database; A distance calculation unit, configured to calculate the spatial distance between any two strain clamps; A distance update unit, configured to query the power parameters of the lines where any two strain clamps are located, and adjust the spatial distance according to the difference in the power parameters; A clustering execution unit, configured to cluster the strain clamps according to the adjusted spatial distance to construct a detection area.

9. The tension clamp detection and control device for the intelligent robot according to claim 7, characterized in that, The scheme selection module includes: A path determination unit, configured to obtain the movement path of each detection scheme based on a preset path planning algorithm; An energy consumption calculation unit, configured to obtain the total height change amount and the total path length of the movement path, and calculate the energy consumption according to the total height change amount and the total path length; the energy consumption is proportional to the total height change amount, and the energy consumption is proportional to the total path length; A scoring calculation unit, configured to read the quantity sequence of the detection solutions, calculate the inversion number of the quantity sequence, and calculate the final score of the detection solutions according to the energy consumption and the inversion number; A scoring application unit, configured to select the detection solution with the highest final score as the optimal solution; The calculation process of the final score is as follows: ; where, represents the final score, and are preset constants, both being positive values, and are preset correction factors, both being positive values, is the total number of coordinate points in the motion path, represents the th eigenvalue of the coordinate point in the motion path, ; represents the absolute value of the height difference corresponding to the th coordinate point in the motion path, takes values as follows: when , ; when , ; represents the height corresponding to the th coordinate point, represents the height corresponding to the previous coordinate point of the th coordinate point; represents the total path length; represents the number of inversions.

10. The tension clamp detection and control device for an intelligent robot according to claim 7, characterized in that, The instruction generation and sending module includes: A solution sending unit, configured to send the optimal solution to the robot and obtain the position of the robot according to a preset time period; after receiving the optimal solution, the robot locally adjusts its moving direction based on the optimal solution; A comparison and adjustment unit, configured to compare the position with the optimal solution, generate a superior control instruction, and send it to the robot; the time period is a dynamic period and is related to the comparison result of the position and the optimal solution; A speed adjustment unit, configured to receive the detection data fed back by the robot, identify the detection data, generate a speed control instruction for the robot according to the identification result, and send it to the robot.

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