Automatic control method, device, electronic equipment and storage medium for ship loader

Abstract

The present application provides an automatic control method, device, electronic device and storage medium of a ship loader, which relates to the field of ship loaders. In the method, acquisition data of the hull is obtained, and the acquisition data includes the operation area, the hull position and posture data, the loading time, the hull operation parameters, the operation environment data, the material information and the safety device status; the operation area, the hull position and posture data, the loading time, the hull operation parameters, the operation environment data, the material information and the safety device status are processed to obtain modeling data, and the preset modeling model is controlled to perform 3D modeling of the hull on the modeling data to obtain a 3D model; multi-dimensional feature extraction is performed on the modeling data to obtain multi-dimensional key features; according to the 3D model, feature analysis is performed on the multi-dimensional key features to obtain analysis results; and loading operation instructions are generated according to the analysis results, which can be automatically controlled to improve operation efficiency.

Description

Technical Field

[0001] The present application relates to the technical field of ship loaders, and in particular to an automated control method, device, electronic equipment and storage medium for a ship loader. Background Art

[0002] Ship loader is a large bulk material machine used in ship loading operations. During the engineering operation, the ship loader needs to cooperate with the ship loader equipment and its ancillary facilities to achieve the loading operation. At present, most of the bulk cargo loading and unloading in domestic and foreign ports uses manually operated loading and unloading equipment to load and unload goods. The scope of loading operations and the movement of each mechanism of the ship loader are controlled by the driver through the operating console. The operation efficiency and safety of the operation are completely dependent on the proficiency and tacit cooperation between the driver and the hatch commander. If there are blind spots in manual command or the coordination is not proficient enough, it is easy to cause collisions between objects, affecting the operation efficiency. Summary of the invention

[0003] The present application provides an automated control method, device, electronic equipment and storage medium for a ship loader, which can perform automated control and improve operating efficiency.

[0004] The technical solution of the embodiment of the present application is as follows:

[0005] In a first aspect, an embodiment of the present application provides an automatic control method for a ship loader, the method comprising:

[0006] Acquire the collected data of the hull, the collected data including the operation area, hull position and posture data, loading time, hull operation parameters, operation environment data, material information and safety device status;

[0007] Processing the operation area, the ship position and posture data, the loading time, the ship operation parameters, the operation environment data, the material information and the safety device status to obtain modeling data, and controlling a preset modeling model to perform 3D modeling of the ship based on the modeling data to obtain a 3D model;

[0008] Performing multi-dimensional feature extraction on the modeling data to obtain multi-dimensional key features;

[0009] Predicting the loading and unloading operations of the ship according to the 3D model to obtain a first prediction control parameter;

[0010] Performing action prediction on the multi-dimensional key features to obtain second prediction and control parameters, wherein the second prediction and control parameters include a predicted safety state and a remaining time for loading;

[0011] Determining the predicted safety state, and generating an alarm message when the predicted safety state is not within a preset state range, wherein the alarm message is used to indicate an abnormal parameter in the second prediction and control parameter;

[0012] According to the alarm information and the remaining loading time, after adjusting the second prediction and control parameter by using the first prediction and control parameter, the step of judging the predicted safety state is performed, and when the predicted safety state is within a preset state range, an analysis result is obtained;

[0013] A loading operation instruction is generated according to the analysis result, and the loading operation instruction is used to automatically control the loading operation.

[0014] In the above technical scheme, the collected data of the hull is obtained, and the collected data includes the operation area, the hull position and posture data, the loading time, the hull operation parameters, the operation environment data, the material information and the safety device status. By obtaining relatively comprehensive collected data, data support is provided for subsequent automatic and accurate control; the operation area, the hull position and posture data, the loading time, the hull operation parameters, the operation environment data, the material information and the safety device status are processed to obtain modeling data. By processing the above data, the accuracy of the data is improved, so that the subsequent automatic control is more accurate, collisions are avoided, and the operation efficiency is improved. The preset modeling model is controlled to perform 3D modeling of the hull on the modeling data to obtain a 3D model, so that the loading and unloading prediction and analysis can be performed through the 3D model to generate accurate operation instructions; multi-dimensional feature extraction is performed on the modeling data to obtain multi-dimensional key features, so that the extracted features are more comprehensive and accurate, which is conducive to automatic control operations; based on According to the 3D model, the loading and unloading operations of the hull are predicted to obtain the first prediction and control parameters; the action prediction of the multi-dimensional key features is performed to obtain the second prediction and control parameters, and the second prediction and control parameters include the predicted safety status and the remaining loading time; the predicted safety status is judged, and when the predicted safety status is not within the preset state range, an alarm message is generated, and the alarm message is used to indicate the abnormal parameters in the second prediction and control parameters; according to the alarm message and the remaining loading time, after adjusting the second prediction and control parameters using the first prediction and control parameters, the step of judging the predicted safety status is executed, and when the predicted safety status is within the preset state range, an analysis result is obtained. Through the parameter adjustment in the above process, collision of objects can be avoided during the automated control process, making the automated control more accurate; loading operation instructions are generated according to the analysis results, and the loading operation instructions are used to automatically control the loading operation, and automated control is performed by executing the operation instructions to improve work efficiency.

[0015] In some embodiments of the present application, the hull operation parameters include hull operation parameters, port size, hull depth, hull layer number and hull angle;

[0016] The controlling the preset modeling model to perform 3D modeling of the hull on the modeling data to obtain a 3D model includes:

[0017] Inputting the modeling data into the modeling model, controlling the modeling model to perform modeling according to the hull operation parameters, the port size, the hull depth, the number of hull layers and the hull angle, and deriving modeling parameters;

[0018] The 3D model is constructed according to the modeling parameters.

[0019] In some embodiments of the present application, generating a loading operation instruction according to the analysis result includes:

[0020] In the case where the material information is a contaminated item, generating a cleaning measure instruction;

[0021] The loading operation instruction is generated according to the analysis result and the cleaning measure instruction.

[0022] In some embodiments of the present application, the multi-dimensional feature extraction is performed on the modeling data to obtain multi-dimensional key features, including:

[0023] Extracting features of the working environment data in the modeling data to obtain environmental features;

[0024] Performing feature extraction on the operation area, the ship position and posture data, the loading time, and the ship operation parameters in the modeling data to obtain area features corresponding to the operation area, time features corresponding to the loading time, position and posture features corresponding to the ship position and posture data, and parameter features corresponding to the ship operation parameters;

[0025] Calculating the correlation relationship between the environmental feature, the regional feature, the time feature, the position and posture feature, and the parameter feature to obtain a correlation feature;

[0026] Performing feature extraction on the material information and the safety device status in the modeling data to obtain material features corresponding to the material information and safety features corresponding to the safety device status;

[0027] The environmental features, the regional features, the time features, the position and posture features, the parameter features, the association features, the material features and the security features constitute the multi-dimensional key features.

[0028] In some embodiments of the present application, the data processing of the operation area, the hull position and posture data, the hull operation parameters, the loading time, the operation environment data, the material information and the safety device status to obtain the modeling data includes:

[0029] Analyzing the operating environment data and the loading time to obtain influencing factors;

[0030] According to the influencing factors, data compensation is performed on the operating area, the hull position and posture data, the hull operating parameters, the material information and the safety device status respectively to obtain compensated area data corresponding to the operating area, compensated position and posture data corresponding to the hull position and posture data, compensated parameter data corresponding to the hull operating parameters, compensated material data corresponding to the material information and compensated safety data corresponding to the safety device status;

[0031] The compensation area data, the compensation position and posture data, the compensation parameter data, the operating environment data, the loading time, the compensation material data and the compensation safety data are fused to obtain the modeling data.

[0032] In some embodiments of the present application, according to the influencing factors, data compensation is performed on the operating area, the hull position and posture data, the hull operating parameters, the material information and the safety device status, respectively, to obtain the compensated area data corresponding to the operating area, the compensated position and posture data corresponding to the hull position and posture data, the compensated parameter data corresponding to the hull operating parameters, the compensated material data corresponding to the material information and the compensated safety data corresponding to the safety device status, including:

[0033] By using a preset compensation algorithm, data compensation is performed on the operating area, the hull position and posture data, the hull operating parameters, the material information and the safety device status according to the influencing factors, so as to obtain the compensated area data corresponding to the operating area, the compensated position and posture data corresponding to the hull position and posture data, the compensated parameter data corresponding to the hull operating parameters, the compensated material data corresponding to the material information and the compensated safety data corresponding to the safety device status.

[0034] In some embodiments of the present application, after generating a loading operation instruction according to the analysis result, wherein the loading operation instruction is used to automatically control the loading operation, the method further includes:

[0035] The loading operation instruction is transmitted to the operation terminal, and the parameters and status corresponding to the loading operation instruction are displayed on the operation terminal, so that adjustments are made according to the displayed parameters and status.

[0036] In a second aspect, an embodiment of the present application provides an automatic control device for a ship loader, the device comprising:

[0037] A data acquisition module is used to acquire the collected data of the hull, wherein the collected data includes the operation area, the hull position and posture data, the loading time, the hull operation parameters, the operation environment data, the material information and the safety device status;

[0038] A data processing module is used to process the operation area, the ship position and posture data, the loading time, the ship operation parameters, the operation environment data, the material information and the safety device status to obtain modeling data, and control a preset modeling model to perform 3D modeling of the ship based on the modeling data to obtain a 3D model;

[0039] A feature extraction module is used to extract multi-dimensional features from the modeling data to obtain multi-dimensional key features;

[0040] A data analysis module is used to predict the loading and unloading operation of the hull according to the 3D model to obtain a first prediction and control parameter; predict the action of the multi-dimensional key feature to obtain a second prediction and control parameter, wherein the second prediction and control parameter includes a predicted safety state and a remaining loading time; judge the predicted safety state, and generate an alarm message when the predicted safety state is not within a preset state range, wherein the alarm message is used to indicate an abnormal parameter in the second prediction and control parameter; and perform the step of judging the predicted safety state after adjusting the second prediction and control parameter using the first prediction and control parameter according to the alarm message and the remaining loading time, and obtain an analysis result when the predicted safety state is within a preset state range;

[0041] An instruction generation module is used to generate a loading operation instruction according to the analysis result, and the loading operation instruction is used to automatically control the loading operation.

[0042] In a third aspect, an embodiment of the present application provides an electronic device, comprising a processor, a memory, a user interface, a communication bus and a network interface, wherein the processor, the memory, the user interface and the network interface are respectively connected to the communication bus, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes any one of the methods provided in the first aspect.

[0043] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions, and when the instructions are executed, any one of the methods provided in the first aspect is executed.

[0044] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0045] 1. The method first acquires the collected data of the hull, including the operation area, the position and posture data of the hull, the loading time, the operation parameters of the hull, the operation environment data, the material information and the status of the safety device. By acquiring relatively comprehensive collected data, data support is provided for subsequent automatic and accurate control. The operation area, the position and posture data of the hull, the loading time, the operation parameters of the hull, the operation environment data, the material information and the status of the safety device are processed to obtain modeling data. By processing the above data, the accuracy of the data is improved, so that the subsequent automatic control is more accurate, collision is avoided, and the operation efficiency is improved. The preset modeling model is controlled to perform 3D modeling of the hull on the modeling data to obtain a 3D model, so that the loading and unloading prediction and analysis can be performed through the 3D model to generate accurate operation instructions. Multi-dimensional feature extraction is performed on the modeling data to obtain multi-dimensional key features, so that the extracted features are more comprehensive and accurate, which is conducive to automatic control operations. According to the 3D model, the loading and unloading operation of the ship is predicted to obtain the first prediction and control parameter; the action prediction of the multi-dimensional key features is performed to obtain the second prediction and control parameter, and the second prediction and control parameter includes the predicted safety state and the remaining time for loading; the predicted safety state is judged, and when the predicted safety state is not within the preset state range, an alarm message is generated, and the alarm message is used to indicate the abnormal parameters in the second prediction and control parameter; according to the alarm message and the remaining time for loading, the second prediction and control parameter is adjusted by the first prediction and control parameter, and the step of judging the predicted safety state is performed, and when the predicted safety state is within the preset state range, the analysis result is obtained. Through the parameter adjustment in the above process, the collision of objects can be avoided during the automatic control process, making the automatic control more accurate; the loading operation instruction is generated according to the analysis result, and the loading operation instruction is used to automatically control the loading operation, and the automatic control is performed by executing the operation instruction to improve the operation efficiency. Therefore, it effectively solves the problem that there are blind spots in human command or insufficient cooperation proficiency in the related technology, which is prone to collision of objects and affects the operation efficiency.

[0046] 2. By performing correlation analysis and compensation on the data, the accuracy of the collected data can be increased, thereby achieving more accurate automatic control. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1It is a flow chart of an automatic control method of a ship loader provided by an embodiment of the present application;

[0048] Figure 2 yes Figure 1 A schematic flow chart of a sub-step of step S300;

[0049] Figure 3 yes Figure 1 A schematic flow chart of a sub-step of step S1000;

[0050] Figure 4 It is a structural schematic diagram of an automatic control device of a ship loader provided by an embodiment of the present application;

[0051] Figure 5 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0052] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0053] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0054] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0055] The embodiment of the present application provides an automated control method, device, electronic device and readable storage medium for a ship loader. The automated control method for the ship loader first obtains collected data of the hull, the collected data including the operating area, hull position and posture data, loading time, hull operating parameters, operating environment data, material information and safety device status, and provides data support for subsequent automatic and accurate control by obtaining relatively comprehensive collected data; performs data processing on the operating area, hull position and posture data, loading time, hull operating parameters, operating environment data, material information and safety device status to obtain modeling data, and improves the accuracy of the data by performing data processing on the above data, thereby making the subsequent automatic control more accurate, avoiding collisions, and improving the operating efficiency, and controls the preset modeling model to perform 3D modeling of the hull on the modeling data to obtain a 3D model, so that the 3D model can be used for subsequent loading and unloading predictions, analysis and generation of accurate operation instructions; performs multi-dimensional feature extraction on the modeling data to obtain multi-dimensional key features, so that the extracted The features are relatively comprehensive and accurate, which is conducive to automated control operations; according to the 3D model, the loading and unloading operations of the hull are predicted to obtain the first predicted control parameters; the actions of the multi-dimensional key features are predicted to obtain the second predicted control parameters, and the second predicted control parameters include the predicted safety status and the remaining time for loading; the predicted safety status is judged, and when the predicted safety status is not within the preset state range, an alarm message is generated, and the alarm message is used to indicate the abnormal parameters in the second predicted control parameters; according to the alarm message and the remaining time for loading, after adjusting the second predicted control parameters using the first predicted control parameters, the step of judging the predicted safety status is executed, and when the predicted safety status is within the preset state range, an analysis result is obtained. Through the parameter adjustment in the above process, collisions of objects can be avoided during the automated control process, making the automated control more accurate; loading operation instructions are generated according to the analysis results, and the loading operation instructions are used to automatically control the loading operation, and automated control is performed by executing the operation instructions to improve operation efficiency.

[0056] It should be noted that the automatic control method of the ship loader is mainly used for the automatic loading and unloading control of bulk cargo in ports, and can also be used for the automatic loading and unloading control of goods in small ports, and is widely used.

[0057] The technical solution provided in the embodiments of the present application is further described below in conjunction with the accompanying drawings.

[0058] Reference Figure 1 , Figure 1The present invention is a flowchart of an automatic control method for a ship loader provided by an embodiment of the present invention. The automatic control method for a ship loader is applied to an automatic control device of the ship loader, and the automatic control method for the ship loader is executed by a processor in an electronic device or a readable storage medium. The automatic control method for the ship loader includes steps S100, S200, S300, S400, and S500.

[0059] Step S100, acquiring the collected data of the hull, the collected data including the operation area, hull position and posture data, loading time, hull operation parameters, operation environment data, material information and safety device status.

[0060] In one embodiment, the collected data includes the operation area, the hull position and attitude data, the loading time, the hull operation parameters, the operation environment data, the material information and the safety device status, wherein the operation area is the area where the ship loader controls the ship machine to move; the hull position and attitude data are the position and orientation of the hull; the hull operation parameters are various operating parameters during loading and unloading, including the operation mode; the loading time is the time for loading and unloading goods by the ship loader, which is affected by the operation environment data. When the operation environment data indicates that the environment is poor, the loading time will be longer; the operation environment data is the weather or the visibility of the hull during the operation of the ship loader, such as temperature, humidity, visibility, etc.; the material information is the parameter information of the size, height, type and other parameters of the loaded and unloaded items; the safety device status is whether the safety equipment of the ship loader and the hull is working normally to ensure safety. Sensors can be installed on the hull and the surrounding environment of the hull to collect the collected data of these hulls, and radar can also be used for monitoring, and the collected data can be transmitted to the ship loader to provide data support for the subsequent automated operation of the ship loader. An induction start device can also be installed to turn on the spray device to reduce pollution when a poor surrounding environment is detected, such as dust generation.

[0061] Step S200, processing the operating area, hull position and posture data, loading time, hull operating parameters, operating environment data, material information and safety device status to obtain modeling data, and controlling the preset modeling model to perform 3D modeling of the hull on the modeling data to obtain a 3D model.

[0062] In one embodiment, due to the influence of environmental factors or the influence of collection equipment such as sensors and radars, the collected data may be inaccurate. Data processing of the operating area, hull position and posture data, loading time, hull operating parameters, operating environment data, material information and safety device status can improve the accuracy of the collected data and facilitate subsequent automated control.

[0063] Specifically, the operation area, ship position and posture data, loading time, ship operation parameters, operation environment data, material information and safety device status are processed to obtain modeling data, including but not limited to the following steps:

[0064] Step S210, analyzing the operating environment data and the loading time to obtain the influencing factors.

[0065] In one embodiment, the influencing factors are the impact of the environment on the collected data, the impact of the environment on the loading time, the impact of the loading time on the loading progress, and then the impact on the automatic control progress. The preset neural network model is used to analyze the operating environment data and the loading time to obtain the influencing factors. The preset neural network model can be a recurrent neural network model or a variant thereof, which is used to predict changes in the environment and the loading time, thereby obtaining the influencing factors. For example, within a period of time, the operating environment and the time for loading and unloading goods in the future can be predicted based on the current environmental data. When the operating environment changes from good to bad, the loading time becomes longer; when the operating environment becomes better, the loading time may be shortened. The operating area, hull position and posture data, hull operating parameters, material information, and safety device status data are adjusted through the influencing factors.

[0066] Step S220, according to the influencing factors, data compensation is performed on the operating area, hull position and attitude data, hull operating parameters, material information and safety device status respectively, to obtain the compensated area data corresponding to the operating area, the compensated position and attitude data corresponding to the hull position and attitude data, the compensated parameter data corresponding to the hull operating parameters, the compensated material data corresponding to the material information and the compensated safety data corresponding to the safety device status.

[0067] In one embodiment, according to the influence factors obtained in step S210, the operation area, the hull position and posture data, the hull operation parameters, the material information and the safety device status are respectively compensated to obtain the compensation area data corresponding to the operation area, the compensation position and posture data corresponding to the hull position and posture data, the compensation parameter data corresponding to the hull operation parameters, the compensation material data corresponding to the material information and the compensation safety data corresponding to the safety device status. Exemplarily, when the influence factor indicates that the environment is deteriorating, the collected data may be affected by the light and deteriorate, the loading time becomes longer, and the hull operation will be affected, that is, the speed of loading and unloading goods is slow due to the long time, the operation area is occupied for a long time, and due to the weight of the object, the hull position and posture data change. It is necessary to adjust the data such as the operation area, the hull position and posture data, the hull operation parameters, the material information and the safety device status. For the operation area, due to the dimming of the light, there is a large error in the boundary of the operation area, which affects the automatic judgment of loading and unloading. The operation area can be corrected and compensated, thereby reducing the error of the operation area, the occupation time is long, and the hull operation parameters can be coordinated to obtain accurate data, thereby achieving accurate control.

[0068] Specifically, the preset compensation algorithm can be one of the linear compensation method, the least square method, the difference method, and the multiple regression analysis method. Taking the multiple regression analysis method as an example, the multiple regression analysis method is used to calculate the influencing factors and the operating area, so as to compensate the operating area data and obtain the compensated area data. Similarly, the multiple regression analysis method is used to calculate the influencing factors and the hull position and attitude data, so as to compensate the hull position and attitude data, and obtain the compensated position attitude data; the multiple regression analysis method is used to calculate the influencing factors and the hull operating parameters, so as to compensate the hull operating parameters, and obtain the compensated parameter data; the multiple regression analysis method is used to calculate the influencing factors and the material information, so as to compensate the material information, and obtain the compensated material data; the multiple regression analysis method is used to calculate the influencing factors and the safety device status, so as to compensate the safety device status, and obtain the compensated safety data. Through the above data compensation for each collected data, the data can be corrected, so as to obtain more accurate collected data, which is conducive to the subsequent accurate automatic control.

[0069] Step S230, performing data fusion on the compensation area data, compensation position and posture data, compensation parameter data, operating environment data, loading time, compensation material data and compensation safety data to obtain modeling data.

[0070] In one embodiment, compensation area data, compensation position and posture data, compensation parameter data, operating environment data, loading time, compensation material data and compensation safety data are sorted. The data sorting may be storing the above data in a matrix form to obtain modeling data; or the above data may be concatenated to obtain modeling data, which is conducive to reading the modeling data.

[0071] In another embodiment, before processing the data of the operation area, the position and attitude data of the hull, the loading time, the operation parameters of the hull, the operation environment data, the material information and the state of the safety device to obtain the modeling data, the automatic control of the loader further includes first cleaning the data of the operation area, the position and attitude data of the hull, the loading time, the operation parameters of the hull, the operation environment data, the material information and the state of the safety device, and the cleaning includes filling missing values, normalizing data, etc. By performing data cleaning, not only the accuracy of the data can be improved, but also the amount of calculation can be reduced.

[0072] In one embodiment, the hull operating parameters include hull operating parameters, port size, hull depth, number of hull layers and hull angle; controlling a preset modeling model to perform 3D modeling of the hull on the modeling data to obtain a 3D model, including but not limited to: inputting the modeling data into the modeling model, controlling the modeling model to perform modeling according to the hull operating parameters, port size, hull depth, number of hull layers and hull angle, and deriving modeling parameters; constructing a 3D model according to the modeling parameters.

[0073] In some possible embodiments of the present application, the hull can be modeled according to the hull position and posture data and the hull operation parameters in the modeling data, and the hull operation parameters include hull operation parameters, port size, hull depth, hull layer number and hull angle. The above data are input into the modeling model, which can be SolidWorks or ShipConstructor. The control modeling model is automatically modeled according to these parameters, and the modeling parameters are exported. The 3D model can be reproduced according to the exported modeling parameters, and the parameters are increased for use on different devices.

[0074] Step S300, extracting multi-dimensional features from modeling data to obtain multi-dimensional key features.

[0075] In one embodiment, due to differences in operating environments, obvious differences between different cargo ships, and the impact of cargo status on the appearance of the hull, the universality of the algorithm is affected. For traditional single algorithms, accurately extracting information without prior information is a challenging task, and prior information is difficult to obtain. The more comprehensive and accurate the extracted data is, the more conducive it is to automatic control operations, maximizing operating efficiency and safe production. Therefore, multi-dimensional feature extraction of modeling data is adopted to obtain multi-dimensional key features, which can extract data features more comprehensively and accurately, and is conducive to automatic control operations.

[0076] like Figure 2 As shown, multi-dimensional feature extraction is performed on the modeling data to obtain multi-dimensional key features, including but not limited to the following steps:

[0077] Step S310: extracting features from the working environment data in the modeling data to obtain environmental features.

[0078] In one embodiment, a time series algorithm can be used to extract features from the operating environment data in the modeling data to obtain environmental features, and predictions can be made based on the extracted environmental features to facilitate subsequent correlation calculations. The time series algorithm can be a recurrent neural network model and its variants.

[0079] Step S320, feature extraction is performed on the operating area, hull position and posture data, loading time, and hull operating parameters in the modeling data to obtain area features corresponding to the operating area, time features corresponding to the loading time, position and posture features corresponding to the hull position and posture data, and parameter features corresponding to the hull operating parameters.

[0080] In one embodiment, a preset machine learning algorithm is used to extract features of the operating area in the modeling data to obtain regional features; a preset machine learning algorithm is used to extract features of the hull position and posture data in the modeling data to obtain position and posture features; a preset machine learning algorithm is used to extract features of the loading time in the modeling data to obtain time features corresponding to the loading time; a preset machine learning algorithm is used to extract features of the hull operating parameters in the modeling data to obtain parameter features. Among them, the preset machine learning algorithm can be principal component analysis, autoencoder, etc., which is conducive to extracting key information by extracting features from each data, so as to facilitate subsequent multi-dimensional feature analysis and calculation.

[0081] Step S330, calculating the correlation relationship between the environment features, the area features, the time features, the position and posture features, and the parameter features to obtain the correlation features.

[0082] In one embodiment, a preset correlation algorithm is used to calculate the correlation between environmental features, regional features, time features, position and posture features, and parameter features to obtain correlation features. The preset correlation algorithm may be a mutual information algorithm, a Pearson correlation coefficient, or the like. Specifically, the Pearson correlation coefficient is used to calculate the correlation between environmental features and regional features, the Pearson correlation coefficient is used to calculate the correlation between environmental features and time features, the Pearson correlation coefficient is used to calculate the correlation between environmental features and position and posture features, and the Pearson correlation coefficient is used to calculate the correlation between environmental features and parameter features, and then each correlation is fused to obtain the correlation feature. By calculating the correlation feature, the influence of environmental features on each other feature can be obtained, thereby avoiding inaccurate automation control operations caused by environmental changes.

[0083] Step S340, extracting features of the material information and the safety device status in the modeling data to obtain material features corresponding to the material information and safety features corresponding to the safety device status.

[0084] In one embodiment, a preset machine learning algorithm is used to extract features of material information in the modeling data to obtain material features; a preset machine learning algorithm is used to extract features of the safety device status in the modeling data to obtain safety features; wherein the preset machine learning algorithm may be principal component analysis, autoencoder, etc., which is beneficial to extracting key information by extracting features from each data, so as to facilitate subsequent multi-dimensional feature analysis and calculation.

[0085] Step S350, environmental features, regional features, time features, position and posture features, parameter features, association features, material features and safety features are combined into multi-dimensional key features.

[0086] In one embodiment, environmental features, regional features, time features, position and posture features, parameter features, association features, material features and security features are organized. The feature organization can be storing the above features in the form of a matrix to obtain multi-dimensional key features; or the above features can be concatenated to obtain multi-dimensional key features, so as to perform feature analysis based on the multi-dimensional key features later.

[0087] Step S400: predicting the loading and unloading operation of the ship according to the 3D model to obtain a first prediction control parameter.

[0088] In one embodiment, the loading and unloading operation of the ship can be simulated by manually modifying the relevant parameters of the 3D model to obtain the loading and unloading parameters, and the simulated loading and unloading parameters can be predicted using a preset prediction algorithm to obtain a first prediction and control parameter, so that the first prediction and control parameter can be used for subsequent adjustment to perform accurate automated operation control. The preset prediction algorithm can be a trained machine learning model or a trained deep learning model, and the first prediction and control parameter is various parameters involved in loading under an ideal state, which will not cause safety accidents.

[0089] Step S500, predicting the actions of the multi-dimensional key features to obtain second prediction and control parameters, the second prediction and control parameters including the predicted safety status and the remaining time for loading.

[0090] In one embodiment, a preset prediction algorithm is used to predict the action of the multi-dimensional key features to obtain a second prediction and control parameter, which includes a predicted safety state and the remaining time for loading. The preset prediction algorithm can be a trained machine learning model or a trained deep learning model. The second prediction and control parameter is various parameters involved in loading under actual operating conditions. Collision accidents may occur due to light and tacit cooperation. The safety state and the remaining time for loading are predicted so that other parameters in the second prediction and control parameter can be adjusted later.

[0091] Step S600, judging the predicted safety status.

[0092] Step S700: When the predicted safety state is not within the preset state range, an alarm message is generated, where the alarm message is used to indicate an abnormal parameter in the second prediction and control parameter.

[0093] In one embodiment, the preset state range is the standard safety range specified for loading and unloading, and it is determined whether the predicted safety state is within the preset state range. If the predicted safety state is not within the preset state range, it indicates that the loading and unloading operation is not compliant and there will be a risk of collision of items. The collision area is queried, and an alarm message is generated according to the parameters corresponding to the collision area. The alarm message is used to indicate an abnormal parameter in the second prediction and control parameter. Exemplarily, the alarm message is generated for the operating area parameter, and the operating area is too close to the shore, resulting in a collision of items.

[0094] Step S800: According to the alarm information and the remaining loading time, the second prediction and control parameter is adjusted using the first prediction and control parameter.

[0095] Step S900, when the predicted safety state is within a preset state range, obtain an analysis result.

[0096] In one embodiment, according to the abnormal parameters indicated by the alarm information and the remaining time for loading, the second prediction and control parameters are adjusted using the first prediction and control parameters, specifically, the relevant parameters in the first prediction and control parameters and the relevant parameters in the second prediction and control parameters are extracted, and the remaining time for loading is first compared. When the remaining time for loading is close, the relevant parameters in the second prediction and control parameters are adjusted using the relevant parameters in the first prediction and control parameters, so that the relevant parameters in the second prediction and control parameters are close to the relevant parameters in the first prediction and control parameters. When the remaining time for loading is not close, the loading and unloading operation of the hull is predicted again according to the remaining time for loading and the relevant parameters in the first prediction and control parameters, and the relevant parameters in the second prediction and control parameters are adjusted according to the predicted parameters, so that the relevant parameters in the second prediction and control parameters are close to the relevant parameters in the predicted control parameters. Then, the step of predicting the safety state for judgment is executed, the safety state is re-judged, and the above steps are repeated. Until the predicted safety state is within the preset state range, indicating that there will be no collision of objects, the second prediction and control parameters are used as the analysis result to generate the operation instruction later.

[0097] Step S1000, generating a loading operation instruction according to the analysis result, the loading operation instruction is used to automatically control the loading operation.

[0098] In one embodiment, automatic loading is performed according to the second prediction and control parameters, and loading operation instructions are generated. The loading operation instructions are used to automatically control the loading operation, which can ensure the safety of automatic loading and unloading of goods by the loader under the parameters, avoid collision of objects, and improve operation efficiency.

[0099] like Figure 3 As shown, generating a loading operation instruction based on the analysis results also includes but is not limited to the following steps:

[0100] Step S1001, when the material information is a contaminated item, a cleaning measure instruction is generated;

[0101] Step S1002, generating a loading operation instruction according to the analysis result and the cleaning measure instruction.

[0102] Specifically, when the material information is a pollutant, environmental monitoring sensors are installed around the hull. Since pollutants may cause the working environment to deteriorate during the loading process, it will also have a certain impact on the loading time and loading operation. When the sensor detects that the environment has deteriorated during the loading process, it obtains monitoring data and generates a cleaning measure instruction. The cleaning measure instruction can trigger the opening of the spray device to reduce dust and other pollution without delaying the loading progress. Automated loading is performed according to the second prediction and control parameters, and loading operation instructions are generated in combination with the cleaning measure instructions to further ensure the safety of the loader's automatic loading and unloading of goods under this parameter, without causing collisions of objects, and improving operating efficiency.

[0103] In one embodiment, after generating a loading operation instruction based on the analysis results, and the loading operation instruction is used to automatically control the loading operation, the automatic control method of the loader also includes but is not limited to: transmitting the loading operation instruction to the operation terminal, and displaying the parameters and status corresponding to the loading operation instruction on the operation terminal, so that adjustments can be made according to the displayed parameters and status.

[0104] In some possible embodiments of the present application, according to the generated loading operation instruction, the loading operation instruction can also be transmitted to the operation terminal, and the operation terminal is provided with a display device, which can display the parameters in the instruction and show it to the operator for viewing. When the operator views the parameters and status corresponding to the displayed loading operation instruction on the operation terminal, he can make fine adjustments to make the automated operation safer and improve the operation efficiency.

[0105] like Figure 4As shown, the embodiment of the present application provides an automatic control device 100 of a ship loader, and the automatic control device 100 of the ship loader obtains the collected data of the hull through the data acquisition module 110, and the collected data includes the operation area, the hull position and posture data, the loading time, the hull operation parameters, the operation environment data, the material information and the safety device status. By obtaining relatively comprehensive collected data, data support is provided for subsequent automatic and accurate control; the data processing module 120 is then used to process the operation area, the hull position and posture data, the loading time, the hull operation parameters, the operation environment data, the material information and the safety device status to obtain modeling data, and the data accuracy is improved by processing the above data, so that the subsequent automatic control is more accurate, collision is avoided, and the operation efficiency is improved, and the preset modeling model is controlled to perform 3D modeling of the hull on the modeling data to obtain a 3D model, so that the loading and unloading prediction and analysis can be performed through the 3D model to generate accurate operation instructions; the feature extraction module 130 is used to extract multi-dimensional features of the modeling data to obtain multi-dimensional key features, so that the proposed The feature extraction is more comprehensive and accurate, which is conducive to automated control operations; the data analysis module 140 predicts the loading and unloading operations of the hull according to the 3D model to obtain the first prediction and control parameters; the multi-dimensional key features are predicted to obtain the second prediction and control parameters, and the second prediction and control parameters include the predicted safety state and the remaining time for loading; the predicted safety state is judged, and when the predicted safety state is not within the preset state range, an alarm message is generated, and the alarm message is used to indicate the abnormal parameters in the second prediction and control parameters; according to the alarm message and the remaining time for loading, after adjusting the second prediction and control parameters using the first prediction and control parameters, the step of judging the predicted safety state is executed, and when the predicted safety state is within the preset state range, an analysis result is obtained. Through the parameter adjustment in the above process, collision of objects can be avoided during the automated control process, making the automated control more accurate; finally, the instruction generation module 150 is used to generate loading operation instructions according to the analysis results, and the loading operation instructions are used to automatically control the loading operation. Automated control is performed by executing the operation instructions to improve operation efficiency.

[0106] It should be noted that the data acquisition module 110 is connected to the data processing module 120 , the data processing module 120 is connected to the feature extraction module 130 , the feature extraction module 130 is connected to the data analysis module 140 , and the data analysis module 140 is connected to the instruction generation module 150 . The above-mentioned automatic control method of the ship loader is applied to the automatic control device 100 of the ship loader. The automatic control device 100 of the ship loader obtains the collected data of the hull, and the collected data includes the operation area, the hull position and posture data, the hull operation parameters, the operation environment data, the material information and the safety device status. By obtaining relatively comprehensive collected data, data support is provided for subsequent automatic and accurate control; the operation area, the hull position and posture data, the hull operation parameters, the operation environment data, the material information and the safety device status are processed to obtain modeling data. By processing the above data, the accuracy of the data is improved, so that the subsequent automatic control is more accurate, collisions are avoided, and the operation efficiency is improved. The preset modeling model is controlled to perform 3D modeling of the hull on the modeling data to obtain a 3D model, so that the loading and unloading prediction and analysis can be performed through the 3D model to generate accurate operation instructions; multi-dimensional feature extraction is performed on the modeling data to obtain multi-dimensional key features, so that the extracted features are more comprehensive and accurate. The invention is indeed conducive to automated control operations; according to the 3D model, the loading and unloading operations of the hull are predicted to obtain the first predicted control parameters; the action of the multi-dimensional key features is predicted to obtain the second predicted control parameters, and the second predicted control parameters include the predicted safety status and the remaining time for loading; the predicted safety status is judged, and when the predicted safety status is not within the preset state range, an alarm message is generated, and the alarm message is used to indicate the abnormal parameters in the second predicted control parameters; according to the alarm message and the remaining time for loading, after adjusting the second predicted control parameters using the first predicted control parameters, the step of judging the predicted safety status is performed, and when the predicted safety status is within the preset state range, an analysis result is obtained. Through the parameter adjustment in the above process, collision of objects can be avoided during the automated control process, making the automated control more accurate; the loading operation instructions are generated according to the analysis results, and the loading operation instructions are used to automatically control the loading operation, and the automated control is performed by executing the operation instructions to improve the operation efficiency.

[0107] It should also be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0108] The present application also discloses an electronic device. Figure 5 , Figure 5 The electronic device 500 may include: at least one processor 501 , at least one network interface 504 , a user interface 503 , a memory 505 , and at least one communication bus 502 .

[0109] The communication bus 502 is used to realize the connection and communication between these components.

[0110] The user interface 503 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 503 may also include a standard wired interface and a wireless interface.

[0111] The network interface 504 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0112] Among them, the processor 501 may include one or more processing cores. The processor 501 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 505, and calling data stored in the memory 505. Optionally, the processor 501 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 501 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 501, and it can be implemented separately through a chip.

[0113] Among them, the memory 505 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 505 includes a non-transitory computer-readable storage medium. The memory 505 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 505 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 505 may also be optionally at least one storage device located away from the aforementioned processor 501. Refer to Figure 5 The memory 505 as a computer storage medium may include an operating system, a network communication module, a user interface module and an application program of an automatic control method for a ship loader.

[0114] exist Figure 5 In the electronic device 500 shown, the user interface 503 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 501 can be used to call the application program of a ship loader automation control method stored in the memory 505, and when executed by one or more processors 501, the electronic device 500 executes one or more methods in the above-mentioned embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited by the described order of actions, because according to the present application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0115] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0116] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0117] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0118] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0119] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0120] The above are only exemplary embodiments of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other embodiments of the present disclosure.

[0121] This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art not described in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. An automatic control method for a ship loader, characterized in that: The method comprises: Acquire the collected data of the hull, the collected data including the operation area, hull position and posture data, loading time, hull operation parameters, operation environment data, material information and safety device status; Processing the collected data to obtain modeling data, and controlling a preset modeling model to perform 3D modeling of a hull on the modeling data to obtain a 3D model; Performing multi-dimensional feature extraction on the modeling data to obtain multi-dimensional key features consisting of environmental features, regional features, time features, position and posture features, parameter features, association features, material features, and safety features; Predicting the loading and unloading operations of the ship according to the 3D model to obtain a first prediction control parameter; Performing action prediction on the multi-dimensional key features to obtain second prediction and control parameters, wherein the second prediction and control parameters include a predicted safety state and a remaining time for loading; Determining the predicted safety state, and generating an alarm message when the predicted safety state is not within a preset state range, wherein the alarm message is used to indicate an abnormal parameter in the second prediction and control parameter; According to the alarm information and the remaining loading time, after adjusting the second prediction and control parameter by using the first prediction and control parameter, the step of judging the predicted safety state is performed, and when the predicted safety state is within a preset state range, an analysis result is obtained; A loading operation instruction is generated according to the analysis result, and the loading operation instruction is used to automatically control the loading operation.

2. The method according to claim 1, characterized in that The hull operation parameters include hull operation parameters, port size, hull depth, hull layer number and hull angle; The controlling the preset modeling model to perform 3D modeling of the hull on the modeling data to obtain a 3D model includes: Inputting the modeling data into the modeling model, controlling the modeling model to perform modeling according to the hull operation parameters, the port size, the hull depth, the number of hull layers and the hull angle, and deriving modeling parameters; The 3D model is constructed according to the modeling parameters.

3. The method according to claim 1, characterized in that Generating a loading operation instruction according to the analysis result includes: In the case where the material information is a contaminated item, generating a cleaning measure instruction; The loading operation instruction is generated according to the analysis result and the cleaning measure instruction.

4. The method according to claim 1, characterized in that: The multi-dimensional feature extraction of the modeling data is performed to obtain multi-dimensional key features consisting of environmental features, regional features, time features, position and posture features, parameter features, association features, material features and safety features, including: Extracting features of the working environment data in the modeling data to obtain environmental features; Performing feature extraction on the operation area, the ship position and posture data, the loading time, and the ship operation parameters in the modeling data to obtain the area feature corresponding to the operation area, the time feature corresponding to the loading time, the position and posture feature corresponding to the ship position and posture data, and the parameter feature corresponding to the ship operation parameters; Calculating the correlation relationship between the environmental feature, the regional feature, the time feature, the position and posture feature, and the parameter feature to obtain the correlation feature; Performing feature extraction on the material information and the safety device status in the modeling data to obtain the material feature corresponding to the material information and the safety feature corresponding to the safety device status; The environmental features, the regional features, the time features, the position and posture features, the parameter features, the association features, the material features and the security features constitute the multi-dimensional key features.

5. The method according to claim 1, characterized in that: The step of processing the collected data to obtain modeling data includes: Analyzing the operating environment data and the loading time to obtain influencing factors; According to the influencing factors, data compensation is performed on the operating area, the hull position and posture data, the hull operating parameters, the material information and the safety device status respectively to obtain compensated area data corresponding to the operating area, compensated position and posture data corresponding to the hull position and posture data, compensated parameter data corresponding to the hull operating parameters, compensated material data corresponding to the material information and compensated safety data corresponding to the safety device status; The compensation area data, the compensation position and posture data, the compensation parameter data, the operating environment data, the loading time, the compensation material data and the compensation safety data are fused to obtain the modeling data.

6. The method according to claim 5, characterized in that The method of performing data compensation on the operation area, the hull position and attitude data, the hull operation parameters, the material information and the safety device status according to the influencing factors to obtain compensated area data corresponding to the operation area, compensated position and attitude data corresponding to the hull position and attitude data, compensated parameter data corresponding to the hull operation parameters, compensated material data corresponding to the material information and compensated safety data corresponding to the safety device status includes: By using a preset compensation algorithm, data compensation is performed on the operating area, the hull position and posture data, the hull operating parameters, the material information and the safety device status according to the influencing factors, so as to obtain the compensated area data corresponding to the operating area, the compensated position and posture data corresponding to the hull position and posture data, the compensated parameter data corresponding to the hull operating parameters, the compensated material data corresponding to the material information and the compensated safety data corresponding to the safety device status.

7. The method according to claim 1, characterized in that After generating a loading operation instruction according to the analysis result, wherein the loading operation instruction is used to automatically control the loading operation, the method further includes: The loading operation instruction is transmitted to the operation terminal, and the parameters and status corresponding to the loading operation instruction are displayed on the operation terminal, so that adjustments are made according to the displayed parameters and status.

8. An automatic control device for a ship loader, characterized in that: The device comprises: A data acquisition module (110) is used to acquire collected data of the hull, wherein the collected data includes an operating area, hull position and posture data, loading time, hull operation parameters, operating environment data, material information and safety device status; A data processing module (120) is used to process the collected data to obtain modeling data, and control a preset modeling model to perform 3D modeling of the hull on the modeling data to obtain a 3D model; A feature extraction module (130) is used to extract multi-dimensional features from the modeling data to obtain multi-dimensional key features consisting of environmental features, regional features, time features, position and posture features, parameter features, association features, material features and safety features; A data analysis module (140) is used to predict the loading and unloading operation of the ship body according to the 3D model to obtain a first prediction and control parameter; predict the action of the multi-dimensional key feature to obtain a second prediction and control parameter, the second prediction and control parameter including a predicted safety state and a remaining loading time; judge the predicted safety state, and if the predicted safety state is not within a preset state range, generate an alarm message, the alarm message is used to indicate an abnormal parameter in the second prediction and control parameter; according to the alarm message and the remaining loading time, after adjusting the second prediction and control parameter using the first prediction and control parameter, perform the step of judging the predicted safety state, and if the predicted safety state is within a preset state range, obtain an analysis result; An instruction generation module (150) is used to generate a loading operation instruction according to the analysis result, wherein the loading operation instruction is used to automatically control the loading operation.

9. An electronic device, characterized in that: The electronic device (500) comprises a processor (501), a memory (505), a user interface (503), a communication bus (502) and a network interface (504), wherein the processor (501), the memory (505), the user interface (503) and the network interface (504) are respectively connected to the communication bus (502), the memory (505) is used to store instructions, the user interface (503) and the network interface (504) are used to communicate with other devices, and the processor (501) is used to execute the instructions stored in the memory (505) so that the electronic device (500) executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is executed.

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