Tower crane brain control system, method and equipment and storage medium

Abstract

The invention relates to the technical field of intelligent control, in particular to a tower crane brain control system, method and equipment and a storage medium. The system comprises a brain control system, a data acquisition system, a learning system, a service system and an interaction system, wherein the data acquisition system, the service system and the interaction system are connected with the brain control system; the learning system trains a preset brain model based on the tower crane real-time sensing data, generates a first tower crane map through the trained preset brain model, and updates the preset brain model based on the tower crane map sent by the interaction system to obtain a second tower crane map; and the brain control system sends a control instruction to the data acquisition system, the at least one tower crane control system and the interaction system according to the user control data of different control permissions, and makes a decision according to the first tower crane map or the second tower crane map. According to the invention, the automation level of tower crane operation can be obviously improved, human errors are reduced, and the working efficiency and safety are improved.

Description

Technical Field

[0001] The present application relates to the field of intelligent control technology, and in particular, to a tower crane brain control system, method, device and storage medium. Background Art

[0002] With the rapid development of science and technology and the expansion of urban construction, tower cranes, as the most commonly used lifting equipment on construction sites, are increasing in number and frequency of use. However, the existing technology has some obvious technical shortcomings in the intelligent management of tower cranes. First, the monitoring and operation of tower cranes are highly dependent on the experience and skills of operators, which not only increases labor costs, but also increases the risk of operating errors due to human factors. Secondly, with the intensive construction of urban buildings, the operating environment of tower cranes has become complex, and problems such as line of sight obstruction and space limitations have led to low management efficiency and increased safety hazards. In addition, the existing tower crane management system has deficiencies in data collection and analysis, and cannot make full use of the collected data to improve management efficiency and operational safety. The maintenance cost is high, and the existing tower crane control system has a slow response speed in emergency situations, lacks intelligent decision support, and is difficult to adapt to the rapidly changing operating environment and needs.

[0003] In view of the shortcomings of existing technologies, the technical problems that need to be solved are mainly focused on how to improve the automation and intelligence level of tower crane intelligent management and reduce dependence on manual operation. It is necessary to develop an intelligent control system that can monitor the status of tower cranes in real time, analyze the operating environment, and automatically adjust the operating strategy. In addition, the system should be able to effectively collect and utilize data, predict and reduce operating risks through advanced data analysis technology, and improve management efficiency and response speed. By integrating the latest scientific and technological achievements, such as artificial intelligence and Internet of Things technology, the intelligent management level of tower cranes can be further improved, ensuring the safety and efficiency of operations, reducing maintenance costs, and providing more reliable and efficient lifting equipment management solutions for construction sites. Summary of the invention

[0004] The present application aims to provide a tower crane brain control system, method, device and storage medium, which can at least solve one of the above-mentioned technical problems to a certain extent.

[0005] In a first aspect, the present application provides a tower crane brain control system, including a brain control system, a data acquisition system, a learning system, a service system and an interactive system, wherein the data acquisition system, the service system and the interactive system are all connected to the brain control system; The data acquisition system is connected to at least one tower crane control system to obtain the real-time tower crane perception data of the tower crane control system; The learning system trains a preset brain model based on the real-time perception data of the tower crane, generates a first tower crane atlas through the trained preset brain model, and updates the preset brain model based on the tower crane atlas sent by the interactive system to obtain a second tower crane atlas; The service system obtains user information and opens different information permissions and different control permissions for different users; The interactive system receives and / or displays corresponding tower crane diagrams according to preset tower crane data and different information permissions; The brain control system sends control instructions to the data acquisition system, at least one tower crane control system and the interactive system according to the user control data with different control permissions, and makes decisions according to the first tower crane map or the second tower crane map.

[0006] In some embodiments of the present application, the brain control system includes a speed decision module, a torque decision module, a path decision module and a safety decision module.

[0007] In some embodiments of the present application, the real-time perception data includes speed perception data, torque perception data and path perception data.

[0008] In some embodiments of the present application, the speed perception data includes tower crane load, ambient wind speed, surrounding obstacle distribution and tower crane equipment status; The generating of the first tower crane atlas by using the trained preset brain model comprises: The trained preset brain model predicts the optimal operating speed by analyzing the tower crane load, ambient wind speed, surrounding obstacle distribution, tower crane equipment status and the preset tower crane data; An initial speed map is generated based on the optimal operating speed.

[0009] In some embodiments of the present application, the torque sensing data includes the power of the drive and the operating status of the drive; The generating of the first tower crane atlas by using the trained preset brain model comprises: The trained preset brain model predicts the optimal torque by analyzing the power of the drive, the operating state of the drive, the speed perception data and the preset tower crane data; An initial torque map is generated based on the optimal torque.

[0010] In some embodiments of the present application, the path perception data includes a starting location; The generating of the first tower crane atlas by using the trained preset brain model comprises: The trained preset brain model predicts the optimal path by analyzing the starting location, the speed perception data and the preset tower crane data; An initial path map is generated based on the optimal path.

[0011] In some embodiments of the present application, generating a first tower crane atlas by using a trained preset brain model includes: determining safety data based on the speed perception data, the torque perception data, and the path perception data; The trained preset brain model predicts optimal safety data by analyzing the safety data; An initial safety map is generated based on the optimal safety data.

[0012] In some embodiments of the present application, the tower crane map sent by the interactive system includes a target map sent by a user and / or a feedback map for the first tower crane map; The method of updating the preset brain model based on the tower crane map sent by the interactive system and obtaining a second tower crane map includes: Preprocessing the target spectrum and / or feedback spectrum; Perform feature extraction on the preprocessed graph to obtain feature extraction results; Retraining the trained preset brain model based on the feature extraction result to obtain a dual-trained brain model; The second tower crane atlas is predicted by the dual-trained brain model.

[0013] In some embodiments of the present application, the preset tower crane data includes management regulation data, tower crane operating parameter data and operation requirement data.

[0014] The second aspect of the present application provides a tower crane brain control method using the tower crane brain control system, which specifically includes the following steps: Acquire real-time perception data of the tower crane, train a preset brain model based on the real-time perception data of the tower crane, generate a first tower crane atlas through the trained preset brain model, and update the preset brain model based on the tower crane atlas sent by the interactive system to obtain the first tower crane atlas and the second tower crane atlas; Obtain user information and set up different information permissions and control permissions for different users; Receive and / or display corresponding tower crane diagrams according to preset tower crane data and different information permissions; Control instructions are sent according to the user control data with different control authorities, and decisions are made according to the first tower crane map or the second tower crane map.

[0015] The technical solution provided in the embodiments of the present application has at least the following technical effects or advantages: Compared with the prior art, the tower crane brain control system and method in each embodiment of the present application have at least the following three beneficial effects: First, by using the preset brain model, the system can generate a first tower crane map based on the collected real-time perception data. This map not only includes the operating status and environmental factors of the tower crane, but also predicts the optimal operating parameters, such as speed, torque and path. And the present application can update the preset brain model according to the target map and feedback map sent by the interactive system to generate a second tower crane map, that is, dynamically adjust its decision model to adapt to environmental changes and user needs, thereby achieving more efficient and safe tower crane operation.

[0016] Second, the system is designed with a service system and an interactive system that can set up different information permissions and control permissions based on the information of different users. This not only ensures the safety of operations, but also provides personalized services based on the specific needs of users. For example, different operators will see different tower crane maps based on their permissions, which ensures that each user can receive the information they need and that is suitable for their permission level.

[0017] Third, the brain control system integrates multiple decision modules, such as speed decision module, torque decision module, path decision module and safety decision module. These modules can use the information extracted from the real-time perception data of the tower crane, combined with the analysis results of the preset brain model, to generate the optimal decision on the tower crane operation. These decision supports not only improve the efficiency of operation, but also greatly enhance the safety of operation, especially in complex and changing working environments.

[0018] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present application. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings: Figure 1 It is a structural schematic diagram of a tower crane brain control system in an exemplary embodiment of the present application; Figure 2 is a schematic diagram of the structure of a brain control system in an exemplary embodiment of the present application; Figure 3 It is a schematic diagram of the steps of a tower crane brain control method using the tower crane brain control system in an exemplary embodiment of the present application; Figure 4 It is a structural schematic diagram of a tower crane brain control device provided by an exemplary embodiment of the present application.

[0020] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. DETAILED DESCRIPTION

[0021] In the process of realizing this application, the inventor found that with the current intensive construction of urban buildings, the operating environment of tower cranes has become more and more complex, and more safety hazards have appeared in the processes of tower crane control, operation and management. At present, there is an urgent need for a complete tower crane brain control solution to help industry managers, equipment companies and tower crane direct operation managers to timely understand the tower crane operation status and site operation status, and ensure the overall process safety and safety management of the tower crane.

[0022] With the current intensive construction of urban buildings, the operating environment of tower cranes has become increasingly complex, and many safety hazards have emerged in the processes of tower crane control, operation and management. At the same time, it is also found that the systems for each process of tower crane management currently work separately, without integrating the various parts together, and it is impossible to achieve comprehensive tower crane management through one system.

[0023] Based on this, the present application provides a tower crane brain control system, in which a data acquisition system is connected to a smart construction site tower crane control platform to collect complete operating data of each tower crane in the country or in a city or region.

[0024] The smart construction site tower crane control platform can integrate multiple common expert systems, such as: 1. Environmental safety perception and assessment expert system; 2. Operation learning virtual expert system; 3. Communication detection expert system; 4. Embedded path planning expert system; 5. Online monitoring expert system and 6. Maintenance expert system. It is called an expert system because the system has intelligent data collection or data intelligent analysis functions.

[0025] Furthermore, the brain control system realizes remote control of the tower crane through a specific smart construction site tower crane control platform.

[0026] The server system includes a user rights management module, which is used to display different data parameters and tower crane maps according to different user attributes. Furthermore, the brain control system generates different remote control instructions according to different user attributes. User attributes include government departments, industry management departments, industry associations, enterprises, tower crane drivers, etc.

[0027] In summary, the tower crane brain control system and method of the present application can be logged in and used by user groups such as global / national / city / enterprise / smart construction site managers, making it convenient for government departments to manage tower cranes, drivers, operators, equipment, etc., and will have a positive effect on the overall technological and quality improvement of the tower crane industry.

[0028] In order to make the technical solutions and advantages in the embodiments of the present application more clearly understood, the exemplary embodiments of the present application are further described in detail below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than an exhaustive list of all the embodiments. It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict.

[0029] Example 1 Figure 1 Schematic diagram of the structure of a tower crane brain control system in an exemplary embodiment of the present application. Figure 1 As shown, the tower crane brain control system includes a brain control system 10, a data acquisition system 20, a learning system 30, a service system 40 and an interactive system 50. The data acquisition system 20 includes a tower crane interface unit, and the tower crane interface unit is connected to the tower crane control system.

[0030] The data acquisition system 20 is connected to at least one tower crane control system to obtain the real-time perception data of the tower crane of the tower crane control system; the system is responsible for collecting real-time perception data from the tower crane control system, and the real-time perception data includes speed perception data, torque perception data and path perception data, such as the load of the tower crane, ambient wind speed, distribution of surrounding obstacles, etc. For example, sensors are used to collect the operating status and external environment data of the tower crane, and these data are transmitted through the tower crane interface unit, thereby ensuring that the system can obtain the latest data and provide real-time basis for the decision-making of the subsequent brain control system.

[0031] The learning system 30 trains a preset brain model based on the real-time perception data of the tower crane, generates a first tower crane atlas through the trained preset brain model, and updates the preset brain model based on the tower crane atlas sent by the interactive system to obtain a second tower crane atlas. The learning system trains a preset brain model based on the collected real-time perception data, generates a tower crane atlas, and updates the model according to the feedback from the interactive system. Through continuous learning and optimization, correct decisions are made to improve the accuracy and efficiency of tower crane operations.

[0032] The service system 40 obtains user information and opens different information permissions and different control permissions for different users. In other words, the service system 40 manages user information, provides customized information and control permissions for different users, and sets access permissions and control permissions according to the user's role and needs to ensure data security and correctness of operations.

[0033] The interactive system 50 receives and / or displays the corresponding tower crane map according to the preset tower crane data and different information permissions. It displays the tower crane map, receives the user's input, and displays the corresponding data according to the user's permissions. It displays data through a graphical user interface, receives user feedback and instructions, thereby improving the user experience and enabling the user to intuitively understand the tower crane status and make decisions.

[0034] For example Figure 1 As shown, the brain control system 10 sends control instructions to the data acquisition system, at least one tower crane control system, and the interactive system according to the user control data of different control permissions, and makes decisions according to the first tower crane map or the second tower crane map. The brain control system 10 integrates the data and analysis results of all subsystems, uses decision-making algorithms to formulate operation strategies, realizes automated and intelligent tower crane control, and improves safety and operation efficiency.

[0035] Figure 2 It is a schematic diagram of the structure of a brain control system. Figure 2 As shown, the brain control system includes a speed decision module, a torque decision module, a path decision module and a safety decision module. In addition, the brain control system also includes a surrounding control module. The speed decision module, the torque decision module, the path decision module and the safety decision module make decisions based on the speed spectrum, the torque spectrum, the path spectrum and the safety spectrum respectively. Therefore, the speed decision module, the torque decision module, the path decision module and the safety decision module here belong to the main control module. The brain control system controls other systems in the tower crane brain control system through the surrounding control module.

[0036] In a specific implementation, the speed perception data includes the tower crane load, ambient wind speed, surrounding obstacle distribution, and tower crane equipment status; the first tower crane map is generated by a trained preset brain model, including: the trained preset brain model predicts the optimal operating speed by analyzing the tower crane load, ambient wind speed, surrounding obstacle distribution, tower crane equipment status, and preset tower crane data; and an initial speed map is generated based on the optimal operating speed. At this time, the speed decision module can give a corresponding decision, i.e., a control instruction, according to the initial speed map.

[0037] The speed decision module predicts the optimal operating speed using a preset brain model (such as a convolutional neural network) by analyzing the real-time load of the tower crane, the ambient wind speed, the equipment status of the tower crane, and other data. These data are input into the model, and the model predicts the current optimal speed based on the weights obtained by training historical data. Once the optimal speed is predicted, a speed map, namely the initial speed map, is generated, showing the recommended speed under different conditions. For example, if strong winds are detected, the model may recommend reducing the operating speed to ensure safety. Assuming that the current load of the tower crane is 2,000 kg and the ambient wind speed is 5 m / s, the model may predict that the optimal speed under these conditions is 2 m / s. This speed will be recorded in the speed map and used for actual operations.

[0038] In a specific implementation, the torque perception data includes the power of the drive and the operating state of the drive; the first tower crane map is generated by a trained preset brain model, including: the trained preset brain model predicts the optimal torque by analyzing the power of the drive, the operating state of the drive, the speed perception data and the preset tower crane data; the initial torque map is generated based on the optimal torque. The torque decision module analyzes the power and operating state of the drive, and predicts the most suitable torque setting through a preset brain model (such as a linear regression model). The data input to the model include the power of the drive, the operating state of the drive, the speed perception data and the preset tower crane data, wherein the preset tower crane data includes management regulations data, tower crane working parameter data and operation demand data. The map generated by analyzing the management regulations, working parameters and operation requirements of the tower crane can provide strong support for monitoring, analysis and decision-making, and help optimize the operation and maintenance strategy of the tower crane. According to the prediction results, the system generates a torque map indicating the torque to be used under different powers and states. For example, if the drive power is 10 kilowatts and the operating state is good, the model may recommend a torque of 500 Nm. This recommended value will be used to adjust the actual operation of the tower crane to optimize performance and reduce wear.

[0039] In a specific implementation, the path perception data includes a starting location; a first tower crane map is generated through a trained preset brain model, including: the trained preset brain model predicts the optimal path by analyzing the starting location, speed perception data and preset tower crane data; an initial path map is generated based on the optimal path. The preset brain model can integrate a graph search algorithm to find the optimal path. A path map is generated, which contains specific path information from the starting point to the target point. The path map records the connection relationship between each node and its adjacent nodes, and marks the optimal path. In other words, a path map will be generated based on the algorithm results to show the best path from the starting point to the end point. For example, a tower crane needs to move from point A to point B, and there are multiple obstacles on the path. The path decision module will calculate the shortest feasible path to bypass the obstacles and display this path in the path map.

[0040] In another specific implementation, a first tower crane map is generated by a trained preset brain model, including: determining safety data based on speed perception data, torque perception data and path perception data; the trained preset brain model predicts optimal safety data by analyzing safety data; and generating an initial safety map based on the optimal safety data. It can be understood that the preset brain model is a superordinate concept, and different models can be used in different decision modules. For example, the safety decision module analyzes the environment and equipment status around the tower crane, and it can use a decision tree model to predict the best safety measures. The data input to the model includes equipment status and surrounding environment information, such as obstacle distribution. Based on the model prediction, a safety map is generated, and the safety decision module uses the safety map to indicate the safety measures to be taken in a specific environment. For example, if the model detects that there are multiple obstacles in close proximity and the wind speed is high, it may recommend reducing the operating speed and increasing monitoring. These measures will be recorded in the safety map and implemented to ensure operational safety.

[0041] It can be seen that through the collaborative work of these modules, the tower crane brain control system can adjust the operation strategy in real time, optimize the performance and safety of the tower crane, and reduce the need for manual operation. This intelligent control system significantly improves the efficiency and safety of tower crane operation and adapts to complex and changing working environments. In short, the tower crane brain control system can provide dynamic operation optimization, personalized user experience and powerful decision support through its highly integrated and intelligent design, significantly improving the operating efficiency and safety of tower cranes.

[0042] Example 2 The present application provides a tower crane brain control system. On the basis of Example 1, the tower crane map sent by the interactive system includes the target map sent by the user and / or the feedback map for the first tower crane map. The interactive system receives the target map from the user and / or the feedback map for the first tower crane map. The target map is the desired operating mode set by the user according to specific needs, while the feedback map contains the user's feedback on the current tower crane operation, which may point out the shortcomings of certain operations or make suggestions for improvement. For example, if the user observes that the stability of the tower crane is insufficient at a specific wind speed, they may submit a feedback map through the interactive system, suggesting to reduce the operating speed under similar conditions.

[0043] In a specific implementation, updating a preset brain model based on a tower crane map sent by an interactive system and obtaining a second tower crane map includes: The first step is to preprocess the target spectrum and / or feedback spectrum; perform feature extraction on the preprocessed spectrum to obtain feature extraction results.

[0044] Specifically, the received graph is preprocessed, such as formatting and normalizing the data, for easy processing. Feature extraction is then performed, which is a step to extract useful information from the preprocessed data to provide input for model training. Feature extraction may involve statistical analysis, image processing techniques, or other data transformation methods, with the goal of identifying and extracting key variables and patterns. For example, the features extracted from the feedback graph may include performance indicators of the tower crane under specific operating conditions, such as specific environmental conditions and operating parameters that cause stability degradation.

[0045] In the second step, the trained preset brain model is retrained based on the feature extraction results to obtain a dual-trained brain model.

[0046] Using the features extracted from the target map and the feedback map, the preset brain model is retrained. In this way, the model learns how to adjust its predictions and decisions based on the new data. Different model learning methods are used according to the specific models used by the preset brain model in different decision modules, such as supervised learning, unsupervised learning or reinforcement learning. The specific method depends on the nature of the task and the type of data used by different decision modules. For example, if the feedback map shows that the tower crane performance is unstable when operating at high wind speeds, the model training will focus on how to adjust the operating parameters under similar conditions to enhance stability.

[0047] The third step is to predict the second tower crane map through the doubly trained brain model.

[0048] Based on the retrained preset brain model, the second tower crane map is predicted and generated. This updated map reflects the new knowledge learned from the target map and feedback map, as well as the optimization of the model. The second tower crane map will provide more precise operating guidance, improve the shortcomings of the previous model, and improve the overall performance and safety of the tower crane. For example, after considering user feedback, the second tower crane map may suggest reducing the operating speed under high wind speed conditions and may add more safety monitoring measures. Every time a decision is made, the preset brain model is updated with the second tower crane map on which the decision is based. This feedback-based iterative learning and optimization process is a key feature of modern intelligent systems, enabling the system to continue to improve and better serve actual operational needs.

[0049] Through the above steps, the brain control system makes decisions according to the first tower crane map or the second tower crane map. The brain control system can dynamically learn and adapt to new operating environments and user needs, and continuously optimize its performance and response.

[0050] The service system 40 includes a user login unit and a user authority unit. The user login unit is used to communicate with the user login interface and obtain user information through the user login interface. The user authority unit is connected to the user login unit, and the user service unit confirms the user authority according to the user information, and opens different display information and different control authorities according to different user authorities. Different users include government department users, industry management department users, enterprise users, manufacturer users, and tower crane driver users.

[0051] The tower crane control system integrates multiple common expert systems, such as: 1. Environmental safety perception and assessment expert system; 2. Operation learning virtual expert system; 3. Communication detection expert system; 4. Embedded path planning expert system; 5. Online monitoring expert system and 6. Maintenance expert system. It is called an expert system because the system has intelligent data collection or data intelligent analysis functions.

[0052] Furthermore, the brain control system 10 realizes remote control of the tower crane through a specific smart construction site tower crane control platform.

[0053] Among them, the operation learning virtual expert system is used for training drivers and is not enabled during actual operation. However, the operation learning virtual expert system will use the data of other systems to optimize the details of the virtual system. The other five expert systems, namely the environmental safety perception and assessment expert system, the communication detection expert system, the embedded path planning expert system, the online monitoring expert system and the maintenance expert system, can be optionally enabled during tower crane operation and are interrelated.

[0054] In specific applications, before the tower crane operates, the environmental parameters in the scene are obtained through the environmental safety perception and assessment expert system, and the corresponding relationship between the relevant material properties and geometric shapes in the scene is marked according to the environmental parameters to truly restore the material characteristics in the on-site scene and provide accurate parameters.

[0055] When the tower crane is operating, five expert systems, including the environmental safety perception and assessment expert system, the communication detection expert system, the embedded path planning expert system, the online monitoring expert system and the maintenance expert system, run simultaneously. The overall control platform coordinates the data fed back by multiple expert systems, and after AI learning and calculation, it outputs the control instructions of each expert system.

[0056] Among them, the environmental safety perception and assessment expert system monitors environmental parameters and environmental safety risks in real time, and performs high-performance computing to achieve comprehensive path planning. The embedded path planning expert system is used to plan the movement path of the hook in real time based on the data of the environmental safety perception and assessment expert system and the online monitoring expert system.

[0057] The communication detection expert system detects the radio environment of the construction site in real time to ensure normal communication. The online monitoring expert system is used to monitor the operating parameters of the environment, equipment, personnel, materials, etc. in real time.

[0058] The maintenance expert system is used to monitor the technical parameters of the tower crane and the parameters of parts and components that need maintenance in real time, and to provide timely reminders and maintenance.

[0059] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application.

[0060] Example 3 This embodiment provides a tower crane brain control method using the tower crane brain control system described in Embodiment 1 or Embodiment 2. Figure 3 Schematic diagram of the steps of the tower crane brain control method using the tower crane brain control system, such as Figure 3 As shown, the specific steps include: S1. Acquire real-time perception data of a tower crane, train a preset brain model based on the real-time perception data of the tower crane, generate a first tower crane atlas through the trained preset brain model, and update the preset brain model based on the tower crane atlas sent by the interactive system to obtain a first tower crane atlas and a second tower crane atlas; S2. Obtain user information and set up different information permissions and control permissions for different users; S3. Receive and / or display corresponding tower crane diagrams according to preset tower crane data and different information permissions; S4. Send control instructions according to the user control data of different control authorities, and make decisions according to the first tower crane map or the second tower crane map.

[0061] The above-mentioned tower crane brain control method has established a complete tower crane brain control solution to help industry managers, equipment companies and tower crane direct operation managers to timely understand the tower crane operation status and construction site operation status, and ensure the overall process safety and safety management of the tower crane.

[0062] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application.

[0063] Example 4 This embodiment provides a tower crane brain control device. For details not disclosed in the tower crane brain control device of this embodiment, please refer to the specific implementation content of the tower crane brain control method or system in other embodiments.

[0064] Figure 4 This is a schematic diagram of the structure of a tower crane brain control device. Figure 4As shown, the tower crane brain control device 400 includes: Memory 402: used to store executable instructions; and Processor 401: used to connect with memory 402 to execute executable instructions to complete the motion vector prediction method.

[0065] Those skilled in the art will understand that Figure 4 It is only an example of the tower crane brain control device 400 and does not constitute a limitation of the tower crane brain control device 400. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the tower crane brain control device 400 may also include input and output devices, network access devices, buses, etc.

[0066] The so-called processor 401 (Central Processing Unit, CPU) can also be other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), application-specific integrated circuits (Application Specific Integrated Circuit, ASIC), field-programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor 401 can also be any conventional processor, etc. The processor 401 is the control center of the tower crane brain control device 400, and uses various interfaces and lines to connect various parts of the entire tower crane brain control device 400.

[0067] The memory 402 can be used to store computer-readable instructions. The processor 401 implements various functions of the tower crane brain control device 400 by running or executing the computer-readable instructions or modules stored in the memory 402 and calling the data stored in the memory 402. The memory 402 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the tower crane brain control device 400, etc. In addition, the memory 402 can include a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, a read-only memory (Read-Only Memory, ROM), a random access memory (Random Access Memory, RAM) or other non-volatile / volatile storage devices.

[0068] If the modules integrated in the tower crane brain control device 400 are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also instruct the relevant hardware to complete through computer-readable instructions. The computer-readable instructions can be stored in a computer-readable storage medium, and when the computer-readable instructions are executed by the processor, the steps of the above-mentioned various method embodiments can be implemented.

[0069] Example 5 This embodiment provides a computer-readable storage medium on which a computer program is stored; the computer program is executed by a processor to implement the tower crane brain control method in other embodiments.

[0070] The tower crane industry brain control device and computer storage medium in the embodiment of the present application obtain real-time perception data of the tower crane, train a preset brain model based on the real-time perception data of the tower crane, generate a first tower crane map through the trained preset brain model, and update the preset brain model based on the tower crane map sent by the interactive system and obtain the first tower crane map and the second tower crane map; obtain user information, and open different information permissions and different control permissions for different users; receive and / or display the corresponding tower crane map according to the preset tower crane data and different information permissions; send control instructions according to the user control data of the different control permissions, and make decisions according to the first tower crane map or the second tower crane map. Furthermore, a complete set of "industry brain" tower crane industry brain control solutions is established through this application to help industry managers, equipment companies and tower crane direct operation managers to timely understand the operation status of tower cranes and construction site operation status, and ensure the overall process safety and safety management of tower cranes.

[0071] When the equipment is operating, this application first reasonably plans the operating scope according to the environmental parameter range to ensure safe work, optimizes and adjusts the control path in real time during the operation, optimizes the control process and controls the tower crane operation. Therefore, this application reduces the safety hazards in the entire process of tower crane control, operation and management to a certain extent.

[0072] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0073] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0074] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0075] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0076] The terms used in the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The singular forms "a", "said" and "the" used in the present invention and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0077] It should be understood that although the terms first, second, third, etc. may be used in the present invention to describe various information, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present invention, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0078] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0079] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

[0080] The above is only a preferred specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. The tower crane brain control system is characterized by: It includes a brain control system, a data acquisition system, a learning system, a service system and an interactive system, wherein the data acquisition system, the service system and the interactive system are all connected to the brain control system; The data acquisition system is connected to at least one tower crane control system to obtain the real-time tower crane perception data of the tower crane control system; The learning system trains a preset brain model based on the real-time perception data of the tower crane, generates a first tower crane atlas through the trained preset brain model, and updates the preset brain model based on the tower crane atlas sent by the interactive system to obtain a second tower crane atlas; The service system obtains user information and opens different information permissions and different control permissions for different users; The interactive system receives and / or displays corresponding tower crane diagrams according to preset tower crane data and different information permissions; The brain control system sends control instructions to the data acquisition system, at least one tower crane control system and the interactive system according to the user control data with different control permissions, and makes decisions according to the first tower crane map or the second tower crane map.

2. The tower crane brain control system according to claim 1 is characterized in that: The brain control system includes a speed decision module, a torque decision module, a path decision module and a safety decision module.

3. The tower crane brain control system according to claim 2 is characterized in that: The real-time perception data includes speed perception data, torque perception data and path perception data.

4. The tower crane brain control system according to claim 3 is characterized in that: The speed perception data includes tower crane load, ambient wind speed, surrounding obstacle distribution and tower crane equipment status; The generating of the first tower crane atlas by using the trained preset brain model comprises: The trained preset brain model predicts the optimal operating speed by analyzing the tower crane load, ambient wind speed, surrounding obstacle distribution, tower crane equipment status and the preset tower crane data; An initial speed map is generated based on the optimal operating speed.

5. The tower crane brain control system according to claim 3 is characterized in that: The torque sensing data includes the driving power and the driving operation status; The generating of the first tower crane atlas by using the trained preset brain model comprises: The trained preset brain model predicts the optimal torque by analyzing the power of the drive, the operating state of the drive, the speed perception data and the preset tower crane data; An initial torque map is generated based on the optimal torque.

6. The tower crane brain control system according to claim 3 is characterized in that: The path perception data includes a starting location; The generating of the first tower crane atlas by using the trained preset brain model comprises: The trained preset brain model predicts the optimal path by analyzing the starting location, the speed perception data and the preset tower crane data; An initial path map is generated based on the optimal path.

7. The tower crane brain control system according to claim 3 is characterized in that: The generating of the first tower crane atlas by using the trained preset brain model comprises: determining safety data based on the speed perception data, the torque perception data, and the path perception data; The trained preset brain model predicts optimal safety data by analyzing the safety data; An initial safety map is generated based on the optimal safety data.

8. The tower crane brain control system according to claim 1 is characterized in that: The tower crane map sent by the interactive system includes the target map sent by the user and / or the feedback map for the first tower crane map; The method of updating the preset brain model based on the tower crane map sent by the interactive system and obtaining a second tower crane map includes: Preprocessing the target spectrum and / or feedback spectrum; Perform feature extraction on the preprocessed graph to obtain feature extraction results; Retraining the trained preset brain model based on the feature extraction result to obtain a dual-trained brain model; The second tower crane atlas is predicted by the dual-trained brain model.

9. The tower crane brain control system according to claim 1 is characterized in that: The preset tower crane data includes management regulation data, tower crane working parameter data and operation requirement data.

10. A tower crane brain control method using the tower crane brain control system according to any one of claims 1 to 9, characterized in that: The specific steps include: Acquire real-time perception data of the tower crane, train a preset brain model based on the real-time perception data of the tower crane, generate a first tower crane atlas through the trained preset brain model, and update the preset brain model based on the tower crane atlas sent by the interactive system to obtain the first tower crane atlas and the second tower crane atlas; Obtain user information and set up different information permissions and control permissions for different users; Receive and / or display corresponding tower crane diagrams according to preset tower crane data and different information permissions; Control instructions are sent according to the user control data with different control authorities, and decisions are made according to the first tower crane map or the second tower crane map.

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