Path planning method

Abstract

The invention provides a path planning method, relates to the field of electric power climbing operation, and can generate a first path planning result and a second path planning result according to environmental parameters of climbing operation, so that operators can perform high-altitude operation more efficiently and safely. The method comprises the following steps: acquiring environment parameters of climbing operation, wherein the environment parameters at least comprise three-dimensional terrain parameters, obstacle parameters and weather parameters; based on the environment parameters, constructing a target environment model corresponding to the climbing operation; analyzing the target environment model to obtain a safety risk level corresponding to the target environment model; based on the target environment model and the security risk level, a first path planning result and a second path planning result corresponding to the target environment model are determined, and the priority of the first path planning result is higher than that of the second path planning result.

Description

Technical Field

[0001] The present disclosure relates to the field of electric power climbing operations, and in particular to a path planning method. Background Art

[0002] In power climbing operations, operators need to move from one location to another to complete the task. However, the environment of power climbing operations is often complex and changeable, and may be affected by many factors such as weather conditions, equipment status, and operating conditions. How to plan an optimal path in such a complex and changeable situation so that operators can complete their tasks efficiently and safely is an urgent problem that needs to be solved. Summary of the invention

[0003] The present disclosure provides a path planning method, which can generate a first path planning result and a second path planning result according to environmental parameters of the high-altitude operation, so that operators can perform high-altitude operations more efficiently and safely.

[0004] In order to achieve the above objectives, the present invention adopts the following technical solutions: In a first aspect, the present disclosure provides a path planning method, which is applied to electronic devices, including: obtaining environmental parameters for climbing operations, the environmental parameters including at least three-dimensional terrain parameters, obstacle parameters and weather parameters; based on the environmental parameters, constructing a target environment model corresponding to the climbing operation; analyzing the target environment model to obtain a safety risk level corresponding to the target environment model; based on the target environment model and the safety risk level, determining a first path planning result and a second path planning result corresponding to the target environment model, wherein the priority of the first path planning result is higher than that of the second path planning result.

[0005] Based on the path planning method of the first aspect, after obtaining the environmental parameters of the climbing operation, a target environment model can be constructed according to the environmental parameters. Then, the target environment model is analyzed to obtain the safety risk level, and finally, based on the target environment model and the safety risk level, the path planning result corresponding to the target environment model is obtained. In this way, not only can the operating personnel be ensured to work efficiently, but also the safety of the operation can be enhanced.

[0006] In combination with the first aspect, in another possible implementation method, based on the target environment model and the security risk level, determining the first path planning result and the second path planning result corresponding to the target environment model, including: inputting the target environment model and the security risk level into the prediction model, and the prediction model outputting the first path planning result corresponding to the target environment model, wherein the first path planning result includes at least one first time step and at least one first execution action information, and one first time step corresponds to one first execution action information; based on the target environment model, determining the second path planning result corresponding to the target environment model, the second path planning result includes at least one second time step and at least one second execution action information, and one second time step corresponds to one second execution action information.

[0007] In combination with the first aspect, in another possible implementation method, the method also includes: using a sensor to obtain an action feedback result corresponding to at least one execution action; the action feedback result includes whether an obstacle is detected and whether the target position is reached; at least one execution action is an execution action in the first execution action information, or an execution action in the second execution action information; the reinforcement learning algorithm in the prediction model updates the neural network parameters in the prediction model according to the action feedback result corresponding to at least one execution action to obtain an updated prediction model.

[0008] In combination with the first aspect, in another possible implementation method, based on the target environment model, determining a second path planning result corresponding to the target environment model includes: obtaining a path configuration library; the path configuration library includes multiple preset environments and preset paths corresponding to each preset environment; based on the target environment model, dividing the target environment model into at least one environment area; comparing at least one environment area and multiple preset environments to determine a target preset environment that matches the at least one environment area; determining the preset path corresponding to the target preset environment as the second path planning result corresponding to the target environment model.

[0009] In combination with the first aspect, in another possible implementation manner, the environmental parameters may further include device parameters and safety parameters; and the execution action information includes action direction, action angle, and action quantity.

[0010] In combination with the first aspect, in another possible implementation method, the method also includes: monitoring whether the environmental parameters of the altitude operation and the behavior data of the operators exceed the threshold; when the environmental parameters exceed the first threshold or the behavior data of the operators exceeds the second threshold, outputting a warning message, the warning information is used to prompt the operator to operate according to the second path planning result; the warning information includes the second path planning result; the second path planning result is obtained based on historical safe operation cases.

[0011] In combination with the first aspect, in another possible implementation method, a target environment model is constructed based on environmental parameters, including: preprocessing the environmental parameters to obtain processed environmental parameters, the preprocessing including data cleaning, data conversion and data labeling; inputting the processed environmental parameters into a preset engine, and the preset engine outputs the target environment model.

[0012] In a second aspect, an embodiment of the present disclosure provides a path planning device, which can be applied to an electronic device to implement the method in the first aspect. The functions of the path planning device can be implemented by hardware, or by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the above functions, such as an acquisition module, a construction module, and a determination module.

[0013] Among them, the acquisition module is configured to obtain the environmental parameters of the climbing operation, and the environmental parameters include at least three-dimensional terrain parameters, obstacle parameters and weather parameters; the construction module is configured to construct a target environment model corresponding to the climbing operation based on the environmental parameters; the determination module is configured to analyze the target environment model to obtain the safety risk level corresponding to the target environment model; and based on the target environment model and the safety risk level, determine the first path planning result and the second path planning result corresponding to the target environment model, wherein the priority of the first path planning result is higher than the second path planning result.

[0014] In combination with the second aspect, in a possible implementation method, the determination module is also configured to input the target environment model and the safety risk level into the prediction model, and the prediction model outputs a first path planning result corresponding to the target environment model, wherein the first path planning result includes at least one first time step and at least one first execution action information, and one first time step corresponds to one first execution action information; based on the target environment model, determine the second path planning result corresponding to the target environment model, the second path planning result includes at least one second time step and at least one second execution action information, and one second time step corresponds to one second execution action information.

[0015] In combination with the second aspect, in a possible implementation method, the acquisition module is further configured to use a sensor to obtain an action feedback result corresponding to at least one execution action; the action feedback result includes whether an obstacle is detected and whether the target position is reached; at least one execution action is an execution action in the first execution action information, or an execution action in the second execution action information; the determination module is also configured to update the neural network parameters in the prediction model according to the action feedback result corresponding to at least one execution action by the reinforcement learning algorithm in the prediction model to obtain an updated prediction model.

[0016] In combination with the second aspect, in a possible implementation method, the determination module is also configured to obtain a path configuration library; the path configuration library includes multiple preset environments and preset paths corresponding to each preset environment; based on the target environment model, the target environment model is divided into at least one environment area; at least one environment area is compared with multiple preset environments to determine a target preset environment that matches at least one environment area; and the preset path corresponding to the target preset environment is determined as the second path planning result corresponding to the target environment model.

[0017] In combination with the second aspect, in a possible implementation, the environmental parameters may also include device parameters and safety parameters; and the execution action information includes action direction, action angle, and action quantity.

[0018] In conjunction with the second aspect, in a possible implementation, the path planning device further includes a monitoring module and an output module. The monitoring module is configured to monitor whether the environmental parameters of the climbing operation and the behavior data of the operator exceed the threshold; the output module is configured to output warning information when the environmental parameters exceed the first threshold or the behavior data of the operator exceeds the second threshold, and the warning information is used to prompt the operator to operate according to the second path planning result; the warning information includes the second path planning result; the second path planning result is obtained based on historical safe operation cases.

[0019] In combination with the second aspect, in a possible implementation method, the construction module is also configured to preprocess the environmental parameters to obtain processed environmental parameters, and the preprocessing includes data cleaning, data conversion and data labeling; the processed environmental parameters are input into the preset engine, and the preset engine outputs the target environment model.

[0020] In combination with the second aspect, in a possible implementation method, the determination module is also configured to set risk factors based on environmental parameters, the risk factors including the distribution of obstacles, the complexity of the terrain and changes in weather conditions; construct a risk assessment model based on the risk factors; analyze the target environment model based on the risk assessment model to obtain the security risk level corresponding to the target environment model, and display the security risk level corresponding to the target environment model.

[0021] In a third aspect, the present disclosure provides an electronic device, comprising: a memory, a display screen, and one or more processors; the memory, the display screen, and the processor are coupled. The memory is used to store computer program codes, and the computer program codes include computer instructions; when the electronic device is running, the processor is used to execute one or more computer instructions stored in the memory, so that the electronic device executes the path planning method as described in any one of the first aspects above.

[0022] In a fourth aspect, the present disclosure provides a computer storage medium, comprising computer instructions, which, when executed on an electronic device, enables the electronic device to execute the path planning method as described in any one of the first aspects.

[0023] In a fifth aspect, the present disclosure provides a computer program product. When the computer program product is executed on an electronic device, the electronic device executes the path planning method as described in any one of the first aspects.

[0024] In a sixth aspect, a device (for example, the device may be a chip system) is provided, the device including a processor, which is used to support a first device to implement the functions involved in the first aspect. In a possible design, the device also includes a memory, which is used to store program instructions and data necessary for the first device. When the device is a chip system, it may be composed of a chip, or may include a chip and other discrete devices.

[0025] It should be understood that the beneficial effects of the second to sixth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 One of the flowcharts of a path planning method provided in an embodiment of the present disclosure.

[0027] Figure 2 The second flowchart of a path planning method provided in an embodiment of the present disclosure.

[0028] Figure 3 The third flowchart of a path planning method provided in an embodiment of the present disclosure.

[0029] Figure 4 A fourth flowchart of a path planning method provided in an embodiment of the present disclosure.

[0030] Figure 5 The fifth flowchart of a path planning method provided in an embodiment of the present disclosure.

[0031] Figure 6 The sixth flowchart of a path planning method provided in an embodiment of the present disclosure.

[0032] Figure 7 FIG7 is a flow chart of a path planning method provided in an embodiment of the present disclosure.

[0033] Figure 8 A schematic diagram of the structure of a path planning device provided in an embodiment of the present disclosure.

[0034] Fig. 9 A schematic diagram of the structure of an electronic device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0035] In order to enable ordinary persons in the art to better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings.

[0036] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The data involved in the present disclosure may be data authorized by the user or fully authorized by all parties. The implementation methods described in the following exemplary embodiments do not represent all implementation methods consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the attached claims.

[0037] It should also be understood that the term “comprising” indicates the presence of described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements and / or components.

[0038] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for display, data for analysis, etc.) involved in this disclosure are all information and data authorized by the user or fully authorized by all parties.

[0039] In the high-altitude work scenarios of the power industry, workers need to move between different locations to advance their work. However, the high-altitude work environment is full of variables, such as climate fluctuations, changes in the operating status of equipment, etc. Therefore, in these cases, how to design an efficient and safe route for high-altitude workers is an urgent problem to be solved.

[0040] Based on the above problems, the embodiment of the present disclosure provides a path planning method. After understanding the environmental parameters of the climbing operation, a target environment model can be constructed according to the environmental parameters. Then the target environment model is analyzed to obtain the safety risk level. Finally, based on the target environment model and the safety risk level, the path planning result corresponding to the target environment model is obtained. In this way, it can not only ensure the efficient operation of the operators, but also enhance the safety of the operation.

[0041] The wind speed warning method provided by the embodiment of the present disclosure is exemplarily described below: The path planning method provided by the present disclosure can be applied to electronic devices.

[0042] In some embodiments, the electronic device may be a server or a terminal, which is not limited in the present disclosure. The server may be a single server, or a server cluster consisting of multiple servers. In some implementations, the server cluster may also be a distributed cluster. The present disclosure does not limit the specific implementation of the server.

[0043] The terminal may be a mobile phone, tablet computer, desktop, laptop, handheld computer, notebook computer, ultra-mobile personal computer (UMPC), netbook, cellular phone, personal digital assistant (PDA), augmented reality (AR)\virtual reality (VR) device, etc. The present disclosure does not impose any special restrictions on the specific form of the electronic device. It can interact with the user through one or more methods such as keyboard, touch pad, touch screen, remote control, voice interaction or handwriting device.

[0044] The following will be combined with the drawings in the embodiments of the present disclosure to clearly and completely describe the technical solutions in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0045] like Figure 1 As shown, when the path planning method is applied to an electronic device, the path planning method may include: Step 101: Obtain environmental parameters for altitude work.

[0046] Among them, the environmental parameters include at least three-dimensional terrain parameters, obstacle parameters and weather parameters. For example, the three-dimensional terrain parameters include terrain height parameters and slope parameters. Obstacle parameters include the location of obstacles, the size of obstacles and the shape of obstacles. Weather parameters include wind speed, temperature and air temperature in the high-altitude operation area.

[0047] In some examples, the process of the electronic device acquiring the environmental parameters of the climbing operation may include: the electronic device uses a collection device to collect the environmental parameters of the climbing operation. The collection device may include an image sensor (such as a camera), a radar, a laser radar, and a global positioning system, etc., which are used to detect the environmental parameters of the climbing operation.

[0048] Environmental parameters may include image data, radar data, lidar data, and positioning data. Image data, radar data, lidar data, and positioning data may be used to determine the three-dimensional terrain parameters, obstacle parameters, and weather parameters of the altitude operation.

[0049] Electronic devices can also determine the high-altitude work area through positioning data, and then use weather stations or third parties to obtain weather parameters of the high-altitude work area.

[0050] Exemplarily, the camera can continuously capture images of the surrounding environment of the aerial work (e.g., the terrain of the aerial work) to obtain image data. Radar obtains radar data by emitting electromagnetic waves and receiving reflected waves. When the electromagnetic waves encounter an object, they are reflected back. The radar calculates the terrain height, slope, etc. based on the time and frequency changes of the reflected waves. LiDAR uses a laser beam to scan the surrounding environment, which can provide very accurate distance measurement and object contour information. By quickly rotating and emitting a laser beam, LiDAR data of the surrounding environment (e.g., a three-dimensional point cloud map) can be generated. By analyzing the LiDAR data, the location of the obstacle, the size of the obstacle, and the shape of the obstacle can be obtained.

[0051] In some examples, the electronic device may also obtain required parameters for aerial work, such as work height, work illumination, work space size, work temperature, and work humidity.

[0052] In some examples, environmental parameters may also include equipment parameters and safety parameters. Equipment parameters include equipment type, equipment performance indicators, and equipment status. Safety parameters include protective equipment, safety equipment, and safety operations.

[0053] Step 102: construct a target environment model corresponding to the altitude operation based on the environmental parameters.

[0054] After obtaining the environmental parameters of the altitude operation, a target environment model corresponding to the altitude operation can be constructed based on the environmental parameters.

[0055] Optional, such as Figure 2 As shown, in step 102, based on the environmental parameters, constructing a target environment model corresponding to the climbing operation includes: Step 201: pre-process the environmental parameters to obtain processed environmental parameters, where the pre-processing includes data cleaning, data conversion and data labeling.

[0056] In some examples, when the environmental parameters are three-dimensional terrain parameters, data cleaning of the three-dimensional terrain parameters may include: checking whether there are missing values ​​in the three-dimensional terrain parameters; if there are missing values, the average value of the terrain parameters of the adjacent points may be used to fill them. For example, if the terrain parameters of a point are missing, and the terrain parameters of the four adjacent points around it are h1, h2, h3, and h4, respectively, then the filling value of the point is (h1+h2+h3+h4) / 4.

[0057] After data cleaning of environmental parameters, data conversion can be performed on the environmental parameters. For example, if three-dimensional terrain parameters are recorded in meters, while other data are recorded in feet, the units of the three-dimensional terrain parameters can be unified with those of other data through data conversion.

[0058] After data cleaning of the environmental parameters, the data of the environmental parameters can be further labeled. For example, the three-dimensional terrain parameters include a terrain height map. On the terrain height map, areas with special features are labeled, such as mountain tops, valleys, cliffs, etc. Different characters can be used for marking so that these key areas can be quickly identified in building the target environment model.

[0059] It is understandable that when the environmental parameters are other parameters, such as slope parameters, obstacle location, obstacle size and obstacle shape, wind speed, temperature, air temperature, etc. in the climbing work area, the environmental parameters can also be processed by referring to the above-mentioned data cleaning, data conversion and data labeling methods, which will not be elaborated here.

[0060] Step 202: Input the processed environmental parameters into a preset engine, and the preset engine outputs a target environmental model.

[0061] Exemplarily, the preset engine includes at least one of Unity3D, Unreal Engine or GIS software.

[0062] In some examples, the process of inputting the processed environmental parameters into the preset engine and the preset engine outputting the target environmental model may be: starting the preset engine and inputting the processed environmental parameters into the preset engine. The preset engine constructs the target environmental model based on the processed environmental parameters.

[0063] After obtaining the target environment model, the target environment model can also be simulated. Through simulation, the impact of different weather conditions (such as changes in wind speed and direction, different rainfall forms, such as heavy rain, drizzle, snow, etc.), different equipment states (such as normal operation, minor faults, serious faults, etc. of the equipment) and different obstacles on the target environment model can be determined. Then, the first path planning result and the second path planning result corresponding to the target environment model can be adjusted according to the simulation results.

[0064] Step 103: Analyze the target environment model to obtain the security risk level corresponding to the target environment model.

[0065] After obtaining the target environment model, the target environment model can be analyzed to obtain the security risk level corresponding to the target environment model.

[0066] Optional, such as Figure 3 As shown, in step 103, the target environment model is analyzed to obtain the security risk level corresponding to the target environment model, including: Step 301: Set risk factors according to environmental parameters.

[0067] Among them, risk factors include the distribution of obstacles, the complexity of the terrain and changes in weather conditions.

[0068] In some examples, after obtaining the environmental parameters of the climbing operation, the distribution of obstacles can be determined according to the environmental parameters. For electric power climbing operations, obstacles may include electric poles, high-voltage line towers, and buildings.

[0069] Specifically, the high-altitude operation area can be divided into multiple grids according to the three-dimensional terrain parameters and obstacle parameters in the environmental parameters, and the obstacles in each grid as well as the number and type of obstacles can be determined to obtain the obstacle distribution in the risk factors.

[0070] The complexity of the terrain can also be obtained according to the slope in the three-dimensional terrain parameter in the environmental parameter. For example, the complexity of the terrain includes multiple slope intervals of the terrain slope, such as a low slope of 0 to 10 degrees, a medium slope of 10 to 30 degrees, a high slope of 30 to 60 degrees, and an extreme slope of more than 60 degrees.

[0071] The changes in weather conditions can also be derived based on the weather parameters in the environmental parameters. Different weather conditions affect equipment and operators in different ways. For example, rainy days can make the working surface slippery, increasing the risk of workers slipping; strong winds can cause the climbing equipment to shake, affecting stability. At the same time, weather conditions can also affect the electrical and mechanical properties of the equipment. For example, humid weather can cause electrical equipment to short-circuit.

[0072] That is to say, after obtaining the environmental parameters, the risk factors can be set according to the environmental parameters.

[0073] Step 302: Construct a risk assessment model based on risk factors.

[0074] Exemplarily, the risk assessment model can be a logistic regression model or a random forest algorithm. The logistic regression model is a linear model used for binary classification problems. The logistic regression model can control the complexity of the model by adjusting parameters. Random forest is an ensemble learning algorithm composed of multiple decision trees with high accuracy and stability.

[0075] According to the risk factors, the process of constructing a risk assessment model may include: first, collecting sample data through sensors, then selecting an initial risk assessment model, and training the initial risk assessment model using the sample data and risk factors to obtain a risk assessment model.

[0076] The process of training the initial risk assessment model using sample data and risk factors to obtain the risk assessment model may include: dividing the sample data into training set data and test set data, training the initial risk assessment model using the training set data and risk factors to adjust the parameters of the initial risk assessment model to obtain the risk assessment model. Then, the risk assessment model is evaluated using the test set data to derive the accuracy and generalization ability of the risk assessment model. The sample data includes sample distribution data of obstacles (such as the location, quantity, type, etc. of obstacles), terrain sample data (such as slope, undulation, landform type, etc.), and weather sample data (such as wind speed, humidity, temperature, etc.) Step 303: Analyze the target environment model according to the risk assessment model, obtain the security risk level corresponding to the target environment model, and display the security risk level corresponding to the target environment model.

[0077] After obtaining the risk assessment model, the target environment model can be input into the risk assessment model so that the risk assessment model outputs the security risk level corresponding to the target environment model. Exemplarily, the security risk level may include multiple levels, such as low risk, medium risk, and high risk.

[0078] After obtaining the safety risk level corresponding to the target environment model, the safety risk level corresponding to the target environment model can be displayed in the target environment model. In this way, for high-risk areas, measures such as adding safety equipment, strengthening personnel training, and adjusting operation time can be taken to reduce operation risks. For low-risk areas, safety measures can be appropriately simplified to improve operation efficiency. Through steps 301-303, the safety risks in the operating environment can be effectively identified and evaluated, so as to formulate reasonable safety strategies to ensure the life safety of operators and the normal operation of equipment.

[0079] Step 104: Based on the target environment model and the security risk level, determine a first path planning result and a second path planning result corresponding to the target environment model, wherein the priority of the first path planning result is higher than the second path planning result.

[0080] After obtaining the target environment model and the security risk level corresponding to the target environment model, a first path planning result and a second path planning result corresponding to the target environment model can be obtained according to the target environment model and the security risk level corresponding to the target environment model.

[0081] In some examples, the first path planning result and the second path planning result corresponding to the target environment model can be determined based on the target environment model and the safety risk level. After obtaining the first path planning result and the second path planning result, the operator can perform the operation according to the first path planning result or the second path planning result.

[0082] In other examples, the first path planning result has a higher priority than the second path planning result. After obtaining the first path planning result and the second path planning result, the operator can perform the operation according to the first path planning result first. If an abnormality occurs during the operation according to the first path planning result, the operator can perform the operation according to the second path planning result.

[0083] Optional, such as Figure 4 As shown, in step 104, based on the target environment model and the security risk level, a first path planning result corresponding to the target environment model is determined, including: Step 401: Input the target environment model and the safety risk level into the prediction model, and the prediction model outputs a first path planning result corresponding to the target environment model.

[0084] The first path planning result includes at least one first time step and at least one first execution action information, and one first time step corresponds to one first execution action information.

[0085] The first time step may be a plurality of time segments in the process of the climbing operation. For example, a time step may be one minute. The first execution action information is an action instruction to be executed within each time step.

[0086] In some examples, the prediction model is a model that has been trained and optimized with sample data. For example, the prediction model can be a model composed of a deep learning model, a generative model, and a reinforcement learning algorithm. The deep learning model can be a convolutional neural network (CNN), a recurrent neural network (RNN), or a graph neural network (GNN). The generative model can be a generative adversarial network (GAN), a variational autoencoder (VAE), etc. The reinforcement learning algorithm can be Q-learning, a deep Q network (DQN), a policy gradient algorithm, etc.

[0087] The target environment model and the safety risk level are input into the prediction model, and the prediction model can extract features of the target environment model.

[0088] After obtaining the characteristics of the target environment model, the generative model can be used to generate a first path planning result based on the characteristics of the target environment model and the safety risk level. Afterwards, the operator can perform the climbing operation according to the first path planning result.

[0089] In some examples, the first execution action information includes a first action direction, a first action angle, and a first action quantity. The first action direction is used to characterize the direction in which the operator or the climbing equipment moves during the climbing operation. The first action angle is related to the action direction, and the action angle is used to characterize the deflection angle in the action direction. The first action quantity is used to characterize a quantitative index of movement, for example, moving forward 5 meters, raising the climbing equipment by 10 centimeters, etc.

[0090] Step 402: Based on the target environment model, determine a second path planning result corresponding to the target environment model.

[0091] The second path planning result includes at least one second time step and at least one second execution action information, and one second time step corresponds to one second execution action information.

[0092] After obtaining the target environment model, the second path planning result corresponding to the target environment model can be obtained according to the target environment model. It can be understood that the content of the second time step can refer to the first time step, and the second execution action information can refer to the first execution action information, which will not be repeated here.

[0093] In some examples, the process of determining a second path planning result corresponding to the target environment model based on the target environment model may include: determining the second path planning result corresponding to the target environment model based on the target environment model and historical safety operation cases.

[0094] In some examples, the first path planning result may be the same as or different from the second path planning result.

[0095] Optional, such as Figure 5 As shown, step 402 determines the second path planning result corresponding to the target environment model based on the target environment model, including: Step 501: Obtain a path configuration library.

[0096] The path configuration library includes a plurality of preset environments and preset paths corresponding to each preset environment.

[0097] In some examples, the path configuration library may be a pre-established database. The preset environments in the path configuration library may be classified according to terrain, obstacles, and weather conditions. For each type of classified preset environment, there is a corresponding preset path. The preset path may be generated by analyzing historical high-altitude operation cases and according to a successful execution path.

[0098] Step 502: Based on the target environment model, divide the target environment model into at least one environment area.

[0099] After obtaining the target environment model, the target environment model can be divided into at least one environment area in a variety of ways. For example, the target environment model can be divided into at least one environment area according to the similarity of terrain features. Alternatively, the target environment model can be divided into at least one environment area according to the distribution of obstacles. Alternatively, the target environment model can be divided into at least one environment area according to the degree of influence of weather conditions on different areas.

[0100] Step 503: Compare at least one environment area with a plurality of preset environments to determine a target preset environment that matches the at least one environment area.

[0101] After obtaining at least one environment region, each environment region is compared with multiple preset environments, and if the similarity exceeds a preset threshold, the preset environment is considered to be a target preset environment that matches the at least one environment region. For example, the preset threshold is 80%.

[0102] For example, if the terrain type, slope, undulation and other characteristics in at least one environment area are consistent with 80% of the terrain characteristics of a preset environment, the preset environment is determined as the target preset environment.

[0103] Step 504: determine the preset path corresponding to the target preset environment as the second path planning result corresponding to the target environment model.

[0104] After the target preset environment is obtained, based on the path configuration library, a preset path corresponding to the target preset environment in the path configuration library is determined as a second path planning result.

[0105] Optional, such as Figure 6 As shown, after determining the first path planning result and the second path planning result corresponding to the target environment model based on the target environment model and the security risk level in step 104, the method further includes: Step 601: Using a sensor, obtain an action feedback result corresponding to at least one execution action.

[0106] The action feedback result includes whether an obstacle is detected and whether the target position is reached. At least one execution action is an execution action in the first execution action information or an execution action in the second execution action information.

[0107] After obtaining the first path planning result and the second path planning result, the operator can perform the operation according to the first path planning result or the second path planning result.

[0108] During the operation of the operator, the sensor can be used to collect the action feedback result corresponding to at least one execution action. For example, the action feedback result is reaching the target position.

[0109] Step 602: The reinforcement learning algorithm in the prediction model updates the neural network parameters in the prediction model according to the action feedback result corresponding to at least one execution action to obtain an updated prediction model.

[0110] After using sensors to collect action feedback results corresponding to at least one execution action, the action feedback results corresponding to at least one execution action can be input into the prediction model, so that the reinforcement learning algorithm in the preset model updates the neural network parameters in the prediction model according to the action feedback results corresponding to at least one execution action to obtain an updated prediction model.

[0111] For example, if the action feedback result corresponding to at least one execution action is the detection of an obstacle, the reinforcement learning algorithm in the preset model will output a negative reward, and adjust the neural network parameters based on the negative reward feedback, so that in this environment, an action to avoid obstacles is output later. For another example, if the action feedback result corresponding to at least one execution action is reaching the target position, the reinforcement learning algorithm in the preset model will output a positive reward, and continue to plan the next execution action based on the positive reward. Through continuous iteration and updating, the prediction model can provide more accurate and efficient path planning results.

[0112] Optional, such as Figure 7 As shown, the method also includes: Step 701: monitor whether the environmental parameters of the altitude operation and the behavior data of the operator exceed the threshold.

[0113] When the operator performs the operation according to the first path planning result or the second path planning result, since the environmental parameters of the high-altitude operation area may change, the environmental parameters of the high-altitude operation and the operator's behavior data can be detected and continuously monitored.

[0114] For example, the threshold of the environmental parameter is the first threshold. The threshold of the operator's behavior data is the second threshold. The behavior data may include the operator's moving speed, the way of operating the climbing equipment, and whether the personal protective equipment is worn correctly.

[0115] Step 702: When the environmental parameter exceeds the first threshold or the operator's behavior data exceeds the second threshold, output warning information.

[0116] Among them, the early warning information is used to prompt the operation according to the second path planning result; the early warning information includes the second path planning result; the second path planning result is obtained based on historical safe operation cases.

[0117] When the monitored environmental parameters exceed the first threshold, it indicates that the safety risk value of the current working environment is high. For example, if the wind speed in the environmental parameters exceeds the set first wind speed threshold, it may cause the climbing equipment to be unstable and increase the risk of falling of the operator.

[0118] When the operator's behavior data exceeds the second threshold, it indicates that the operator's behavior does not meet safety regulations. For example, the operator does not wear personal protective equipment correctly, which may lead to a safety accident.

[0119] In the above situation, the system will output early warning information to use the early warning information to remind operators and managers to pay attention to potential safety risks and take corresponding measures. Since the priority of the first path planning result is higher than the second path planning result, operators usually work according to the first path planning result. Since the second path planning result is based on historical safe operation cases, the safety level of the second path planning result is higher than that of the first path planning result. Therefore, in this case, the early warning information can be used to remind operators to work according to the second path planning result, thereby ensuring the safety of the operators.

[0120] Combination of the above Figure 1-Figure 7 The method provided by the embodiment of the present disclosure is described in detail. In order to achieve the above functions, the path planning device includes hardware structures and / or software modules corresponding to the execution of each function, and these hardware structures and / or software modules corresponding to the execution of each function can constitute a path planning device. It should be easily appreciated by those skilled in the art that, in combination with the algorithm steps of each example described in the embodiments disclosed herein, the present disclosure can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present disclosure.

[0121] The embodiments of the present disclosure may divide the path planning device into functional modules according to the above method examples. For example, the path planning device may divide each functional module corresponding to each function, or may integrate two or more functions into one processing module. The above integrated modules may be implemented in the form of hardware or in the form of software functional modules. It should be noted that the division of modules in the embodiments of the present disclosure is schematic and is only a logical functional division. There may be other division methods in actual implementation.

[0122] The following, combined Figure 8 The path planning device provided by the embodiment of the present disclosure is described in detail. It should be understood that the description of the device embodiment corresponds to the description of the method embodiment, so the contents not described in detail can be referred to the method embodiment above, and for the sake of brevity, they will not be repeated here.

[0123] Figure 8 is a logical structure block diagram of a path planning device according to an exemplary embodiment. Figure 8 The path planning device includes: an acquisition module 810, a construction module 820 and a determination module 830.

[0124] An acquisition module 810 is configured to acquire environmental parameters of the climbing operation, where the environmental parameters at least include three-dimensional terrain parameters, obstacle parameters and weather parameters; A construction module 820 is configured to construct a target environment model corresponding to the climbing operation based on the environmental parameters; The determination module 830 is configured to analyze the target environment model to obtain the security risk level corresponding to the target environment model; and based on the target environment model and the security risk level, determine the first path planning result and the second path planning result corresponding to the target environment model, wherein the priority of the first path planning result is higher than the second path planning result.

[0125] Optionally, the determination module 830 is also configured to input the target environment model and the safety risk level into the prediction model, and the prediction model outputs a first path planning result corresponding to the target environment model, wherein the first path planning result includes at least one first time step and at least one first execution action information, and one first time step corresponds to one first execution action information; based on the target environment model, determine the second path planning result corresponding to the target environment model, the second path planning result includes at least one second time step and at least one second execution action information, and one second time step corresponds to one second execution action information.

[0126] Optionally, the acquisition module 810 is further configured to use a sensor to obtain an action feedback result corresponding to at least one execution action; the action feedback result includes whether an obstacle is detected and whether the target position is reached; at least one execution action is an execution action in the first execution action information, or an execution action in the second execution action information; the determination module is further configured to update the neural network parameters in the prediction model according to the action feedback result corresponding to at least one execution action by the reinforcement learning algorithm in the prediction model to obtain an updated prediction model.

[0127] Optionally, the determination module 830 is also configured to obtain a path configuration library; the path configuration library includes multiple preset environments and preset paths corresponding to each preset environment; based on the target environment model, the target environment model is divided into at least one environment area; at least one environment area is compared with multiple preset environments to determine a target preset environment that matches the at least one environment area; and the preset path corresponding to the target preset environment is determined as the second path planning result corresponding to the target environment model.

[0128] Optionally, the environmental parameters may also include equipment parameters and safety parameters; the execution action information includes the action direction, action angle and action quantity.

[0129] Optionally, the path planning device further includes a monitoring module 840 and an output module 850. The monitoring module 840 is configured to monitor whether the environmental parameters of the climbing operation and the behavior data of the operator exceed the threshold value; the output module 850 is configured to output warning information when the environmental parameters exceed the first threshold value or the behavior data of the operator exceeds the second threshold value, and the warning information is used to prompt the operation according to the second path planning result; the warning information includes the second path planning result; the second path planning result is obtained based on historical safe operation cases.

[0130] Optionally, construction module 820 is further configured to preprocess the environmental parameters to obtain processed environmental parameters, the preprocessing including data cleaning, data conversion and data labeling; the processed environmental parameters are input into a preset engine, and the preset engine outputs a target environment model.

[0131] Optionally, the determination module 830 is further configured to set risk factors based on environmental parameters, the risk factors including the distribution of obstacles, the complexity of the terrain and changes in weather conditions; construct a risk assessment model based on the risk factors; analyze the target environment model based on the risk assessment model to obtain the security risk level corresponding to the target environment model, and display the security risk level corresponding to the target environment model.

[0132] Of course, the path selection device provided by the embodiment of the present disclosure includes but is not limited to the above modules, for example, the path selection device may also include a storage module 860. The storage module 860 may be used to store the program code of the write path selection device, and may also be used to store data generated by the write path selection device during operation, such as data in a write request, etc.

[0133] Fig. 9 FIG. 1 shows a possible structural diagram of the electronic device involved in the above embodiment. Fig. 9 As shown, the electronic device 90 includes a processor 901 and a memory 902 .

[0134] Understandably, Fig. 9 The electronic device 90 shown can implement all the functions of the above-mentioned speech recognition model generation method. The functions of each module in the above-mentioned path planning device can be implemented in the processor 901 of the electronic device 90. The storage module of the path planning device is equivalent to the memory 902 of the electronic device 90.

[0135] The processor 901 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 90 may include an application processor (AP), a modem processor, a graphics processor (GPU), an image signal processor (ISP), a controller, a memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural-network processing unit (NPU), etc. Different processing units may be independent devices or integrated in one or more processors.

[0136] The memory 902 may include one or more computer-readable storage media, which may be non-transitory. The memory 902 may also include a high-speed random access memory, and a non-volatile memory, such as one or more disk storage devices, flash memory storage devices. In some embodiments, the non-transitory computer-readable storage medium in the memory 902 is used to store at least one instruction, which is used to be executed by the processor 901 to implement the speech recognition model generation method provided by the embodiment of the method of the present disclosure.

[0137] In some embodiments, the electronic device 90 may further optionally include: a peripheral device interface 903 and at least one peripheral device. The processor 901, the memory 902 and the peripheral device interface 903 may be connected via a bus or a signal line. Each peripheral device may be connected to the peripheral device interface 903 via a bus, a signal line or a circuit board. Specifically, the peripheral device includes: at least one of a radio frequency circuit 904, a touch display screen 905, a camera 906, an audio circuit 907, a positioning component 908 and a power supply 909.

[0138] The peripheral device interface 903 may be used to connect at least one peripheral device related to I / O (Input / Output) to the processor 901 and the memory 902. In some embodiments, the processor 901, the memory 902, and the peripheral device interface 903 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 901, the memory 902, and the peripheral device interface 903 may be implemented on a separate chip or circuit board, which is not limited in this embodiment.

[0139] The radio frequency circuit 904 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The radio frequency circuit 904 communicates with the communication network and other communication devices through electromagnetic signals. The radio frequency circuit 904 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals into electrical signals. Optionally, the radio frequency circuit 404 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a user identity module card, etc. The radio frequency circuit 904 can communicate with other path planning devices through at least one wireless communication protocol. The wireless communication protocol includes but is not limited to: a metropolitan area network, various generations of mobile communication networks (2G, 3G, 4G and 6G), a wireless local area network and / or a Wi-Fi (Wireless Fidelity) network. In some embodiments, the radio frequency circuit 904 may also include circuits related to NFC (Near Field Communication), which is not limited in the present disclosure.

[0140] The display screen 905 is used to display the UI (User Interface). The UI may include graphics, text, icons, videos and any combination thereof. When the display screen 905 is a touch display screen, the display screen 905 also has the ability to collect touch signals on the surface or above the surface of the display screen 905. The touch signal can be input to the processor 901 as a control signal for processing. At this time, the display screen 905 can also be used to provide virtual buttons and / or virtual keyboards, also known as soft buttons and / or soft keyboards. In some embodiments, the display screen 905 can be one, and the front panel of the electronic device 90 is set; the display screen 905 can be made of materials such as LCD (Liquid Crystal Display), OLED (Organic Light-Emitting Diode) and the like.

[0141] The camera assembly 906 is used to collect images or videos. Optionally, the camera assembly 906 includes a front camera and a rear camera. Usually, the front camera is set on the front panel of the path planning device, and the rear camera is set on the back of the path planning device. The audio circuit 907 may include a microphone and a speaker. The microphone is used to collect sound waves from the user and the environment, and convert the sound waves into electrical signals and input them into the processor 901 for processing, or input them into the radio frequency circuit 404 to achieve voice communication. For the purpose of stereo acquisition or noise reduction, there can be multiple microphones, which are respectively arranged at different parts of the electronic device 90. The microphone can also be an array microphone or an omnidirectional acquisition microphone. The speaker is used to convert the electrical signal from the processor 901 or the radio frequency circuit 904 into sound waves. The speaker can be a traditional film speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can not only convert the electrical signal into a sound wave audible to humans, but also convert the electrical signal into a sound wave that cannot be heard by humans for purposes such as ranging. In some embodiments, the audio circuit 907 may also include a headphone jack.

[0142] The positioning component 908 is used to locate the current geographic location of the electronic device 90 to implement navigation or LBS (Location Based Service). The positioning component 908 can be a positioning component based on the US GPS (Global Positioning System), China's Beidou system, Russia's Grenas system or the European Union's Galileo system.

[0143] The power supply 909 is used to power various components in the electronic device 90. The power supply 909 can be an alternating current, a direct current, a disposable battery, or a rechargeable battery. When the power supply 909 includes a rechargeable battery, the rechargeable battery can support wired charging or wireless charging. The rechargeable battery can also be used to support fast charging technology.

[0144] In some embodiments, the electronic device 90 further includes one or more sensors 910. The one or more sensors 910 include, but are not limited to: an acceleration sensor, a gyroscope sensor, a pressure sensor, a fingerprint sensor, an optical sensor, and a proximity sensor.

[0145] The acceleration sensor can detect the magnitude of acceleration on the three coordinate axes of the coordinate system established with the electronic device 90. The gyroscope sensor can detect the body direction and rotation angle of the electronic device 90, and the gyroscope sensor can cooperate with the acceleration sensor to collect the user's 3D actions on the electronic device 90. The pressure sensor can be set on the side frame of the electronic device 90 and / or the lower layer of the touch display screen 906. When the pressure sensor is set on the side frame of the electronic device 90, the user's holding signal of the electronic device 90 can be detected. The fingerprint sensor is used to collect the user's fingerprint. The optical sensor is used to collect the ambient light intensity. The proximity sensor, also known as the distance sensor, is usually set on the front panel of the electronic device 90. The proximity sensor is used to collect the distance between the user and the front of the electronic device 90.

[0146] The present disclosure also provides a computer-readable storage medium having instructions stored thereon. When the instructions in the storage medium are executed by a processor of a path planning device, the path planning device is enabled to execute the speech recognition model generation method provided by the present disclosure.

[0147] The embodiment of the present disclosure also provides a computer program product containing instructions, which, when executed on a path planning device, enables the path planning device to execute the speech recognition model generation method provided by the present disclosure.

[0148] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. The present disclosure is intended to cover any variations, uses or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The description and examples are intended to be exemplary only, and the true scope and spirit of the present disclosure are indicated by the following claims.

[0149] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A path planning method, characterized in that: The method comprises: Acquiring environmental parameters for the altitude operation, wherein the environmental parameters include at least three-dimensional terrain parameters, obstacle parameters, and weather parameters; Based on the environmental parameters, a target environment model corresponding to the altitude operation is constructed; Analyze the target environment model to obtain a security risk level corresponding to the target environment model; Based on the target environment model and the security risk level, a first path planning result and a second path planning result corresponding to the target environment model are determined, wherein the first path planning result has a higher priority than the second path planning result.

2. The method according to claim 1, characterized in that The determining, based on the target environment model and the security risk level, a first path planning result and a second path planning result corresponding to the target environment model includes: Inputting the target environment model and the security risk level into a prediction model, the prediction model outputting the first path planning result corresponding to the target environment model, wherein the first path planning result includes at least one first time step and at least one first execution action information, and one first time step corresponds to one first execution action information; Based on the target environment model, a second path planning result corresponding to the target environment model is determined, wherein the second path planning result includes at least one second time step and at least one second execution action information, and one second time step corresponds to one second execution action information.

3. The method according to claim 2, characterized in that The method further comprises: Using a sensor, obtaining an action feedback result corresponding to at least one execution action; the action feedback result includes whether an obstacle is detected and whether a target position is reached; the at least one execution action is an execution action in the first execution action information or an execution action in the second execution action information; The reinforcement learning algorithm in the prediction model updates the neural network parameters in the prediction model according to the action feedback result corresponding to the at least one execution action to obtain an updated prediction model.

4. The method according to claim 2, characterized in that: The determining, based on the target environment model, a second path planning result corresponding to the target environment model includes: Obtain a path configuration library; the path configuration library includes multiple preset environments and preset paths corresponding to each preset environment; Based on the target environment model, dividing the target environment model into at least one environment area; Comparing the at least one environmental area with the plurality of preset environments, and determining a target preset environment matching the at least one environmental area; The preset path corresponding to the target preset environment is determined as the second path planning result corresponding to the target environment model.

5. The method according to claim 1, characterized in that The environmental parameters may also include equipment parameters and safety parameters; the execution action information may include action direction, action angle and action quantity.

6. The method according to any one of claims 1 to 5, characterized in that The method further comprises: Monitor whether the environmental parameters of the climbing operation and the behavior data of the operator exceed the threshold; When the environmental parameter exceeds a first threshold or the operator's behavior data exceeds a second threshold, a warning message is output, wherein the warning message is used to prompt the operator to perform operations according to the second path planning result; the warning message includes the second path planning result; and the second path planning result is obtained based on historical safe operation cases.

7. The method according to claim 1, characterized in that The step of constructing a target environment model based on the environment parameters includes: Preprocessing the environmental parameters to obtain processed environmental parameters, wherein the preprocessing includes data cleaning, data conversion and data labeling; The processed environmental parameters are input into a preset engine, and the preset engine outputs the target environmental model.

8. The method according to claim 1, characterized in that The analyzing the target environment model to obtain the security risk level corresponding to the target environment model includes: According to the environmental parameters, risk factors are set, wherein the risk factors include the distribution of obstacles, the complexity of the terrain, and the change of weather conditions; Constructing a risk assessment model based on the risk factors; The target environment model is analyzed according to the risk assessment model to obtain a security risk level corresponding to the target environment model, and the security risk level corresponding to the target environment model is displayed.

9. A path planning device, characterized in that: The device comprises: An acquisition module is configured to acquire environmental parameters of the climbing operation, wherein the environmental parameters at least include three-dimensional terrain parameters, obstacle parameters and weather parameters; A construction module is configured to construct a target environment model corresponding to the climbing operation based on the environmental parameters; A determination module is configured to analyze the target environment model to obtain a security risk level corresponding to the target environment model; and based on the target environment model and the security risk level, determine a first path planning result and a second path planning result corresponding to the target environment model, wherein the priority of the first path planning result is higher than that of the second path planning result.

10. An electronic device, characterized in that: include: A touch screen, comprising a touch sensor and a display screen; one or more processors; Memory; Wherein, one or more computer programs are stored in the memory, and the one or more computer programs include instructions. When the instructions are executed by the electronic device, the electronic device executes a path planning method as described in any one of claims 1 to 8.

11. A computer-readable storage medium having computer program instructions stored thereon; characterized in that: When the computer program instructions are executed by an electronic device, the electronic device implements a path planning method as claimed in any one of claims 1 to 8.

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