Control method, device and equipment for advanced hydraulic support and storage medium

Abstract

The invention provides a control method, device and equipment for an advanced hydraulic support and a storage medium, and the method comprises the steps that sensor data of multiple types of sensors arranged on the hydraulic support are acquired; wherein at least one sensor is arranged in each type; performing data fusion processing on the sensor data to obtain sensor fusion data corresponding to each sensor of the type; performing data fusion analysis on the sensor fusion data to obtain pose estimation data of the hydraulic support; generating a first control instruction of the hydraulic support based on the pose estimation data and a preset hydraulic support control requirement; and closed-loop control is conducted on the hydraulic support based on the first control instruction. According to the technical scheme, the stability and safety of the hydraulic support can be improved.

Description

Technical Field

[0001] This application relates to the technical field of mine mining, and particularly relates to a control method, device, equipment and storage medium for an advanced hydraulic support. Background Technique

[0002] In the related art, in a complex underground environment, the control system of a hydraulic support is easily affected by various interferences, such as electromagnetic interference, vibration interference, etc., resulting in unstable operation of the control system, and further resulting in problems of insufficient reliability, flexibility and intelligence in the control of the advanced hydraulic support. Summary of the Invention

[0003] This application aims to solve at least one of the technical problems in the related art to some extent.

[0004] In a first aspect, this application proposes a control method for an advanced hydraulic support, and the method includes: acquiring sensor data of multiple types of sensors arranged on the hydraulic support; where each type of sensor is at least one; performing data fusion processing on the sensor data to obtain sensor fusion data corresponding to each type of sensor; performing data fusion analysis on the sensor fusion data to obtain pose estimation data of the hydraulic support; generating a first control instruction for the hydraulic support based on the pose estimation data and a preset control requirement of the hydraulic support; and performing closed-loop control on the hydraulic support based on the first control instruction.

[0005] In one implementation, the sensors include at least one of the following: pressure sensor; displacement sensor; inclination sensor; vision sensor; laser ranging sensor.

[0006] In an optional implementation, the performing data fusion processing on the sensor data to obtain sensor fusion data corresponding to each type of sensor includes at least one of the following: performing data consistency verification on multiple pressure data to obtain pressure fusion data; performing push stroke consistency verification based on the displacement data of the hydraulic support and the displacement data of adjacent hydraulic supports to obtain displacement fusion data; in response to the inclination data being abnormal, compensating the inclination data based on the displacement data and the vision data to obtain inclination fusion data; optimizing the data accuracy of the vision data based on the pressure fusion data and / or the inclination fusion data to obtain vision fusion data; correcting the tilt angle error of the laser ranging data based on the inclination fusion data and / or the displacement fusion data to obtain laser ranging fusion data.

[0007] In one implementation, the data fusion analysis of the sensor fusion data to obtain the pose estimation data of the hydraulic support includes: using the Bayesian estimation method, combining the pressure fusion data, the displacement fusion data, and the inclination fusion data to obtain the attitude estimation data of the hydraulic support; inputting the visual fusion data and the laser ranging fusion data into a pre-trained deep learning model to obtain the position estimation data of the hydraulic support; and obtaining the pose estimation data based on the attitude estimation data and the position estimation data.

[0008] In one implementation, the control requirements include stability requirements, pushing efficiency requirements, and obstacle avoidance requirements. The generation of the first control instruction for the hydraulic support based on the pose estimation data and the preset control requirements of the hydraulic support includes: constructing a joint cost function based on the stability requirements, the pushing efficiency requirements, and the obstacle avoidance requirements; and generating the first control instruction based on the pose estimation data and the joint cost function.

[0009] In one implementation, the method further includes: determining whether there is an abnormality in the hydraulic support based on the sensor fusion data; and if it is determined that there is an abnormality in the hydraulic support, generating a second control instruction based on the sensor fusion data.

[0010] In a second aspect, the present application provides a control device for a front hydraulic support. The device includes: an input module for acquiring sensor data of multiple types of sensors provided on the hydraulic support; where each type of sensor is at least one; a processing module for performing data fusion processing on the sensor data to obtain sensor fusion data corresponding to each type of sensor; the processing module is further configured to: perform data fusion analysis on the sensor data to obtain the pose estimation data of the hydraulic support; the processing module is further configured to: generate a control instruction for the hydraulic support based on the pose estimation data and the preset control requirements of the hydraulic support; and an output module for performing closed-loop control on the hydraulic support based on the control instruction.

[0011] In one implementation, the sensors include at least one of the following: a pressure sensor; a displacement sensor; an inclination sensor; a vision sensor; a laser ranging sensor.

[0012] In an alternative implementation, the processing module is configured to perform at least one of the following: perform data consistency verification on multiple pressure data to obtain pressure fusion data; perform push travel consistency verification based on the displacement data of the hydraulic support and the displacement data of adjacent hydraulic supports to obtain displacement fusion data; in response to an abnormality in the inclination data, compensate the inclination data based on the displacement data and the visual data to obtain inclination fusion data; optimize the data accuracy of the visual data based on the pressure fusion data and / or the inclination fusion data to obtain visual fusion data; correct the tilt angle error of the laser ranging data based on the inclination fusion data and / or the displacement data to obtain laser ranging fusion data.

[0013] In one implementation, the processing module is specifically configured to: use the Bayesian estimation method to combine the pressure fusion data, the displacement fusion data, and the inclination fusion data to obtain the attitude estimation data of the hydraulic support; input the visual fusion data and the laser ranging fusion data into a pre-trained deep learning model to obtain the position estimation data of the hydraulic support; obtain the pose estimation data based on the attitude estimation data and the position estimation data.

[0014] In one implementation, the processing module is specifically configured to: construct a joint cost function based on the stability requirement, the push efficiency requirement, and the obstacle avoidance requirement; generate the first control instruction based on the pose estimation data and the joint cost function.

[0015] In one implementation, the processing module is further configured to: determine whether there is an abnormality in the hydraulic support based on the sensor fusion data; determine that there is an abnormality in the hydraulic support, and generate a second control instruction based on the sensor fusion data.

[0016] In a third aspect, the present application provides an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and when the instructions are executed by the at least one processor, the at least one processor is enabled to execute the control method of the advanced hydraulic support as described in the first aspect.

[0017] In a fourth aspect, the present application provides a computer-readable storage medium for storing instructions, which when executed, implement the method as described in the first aspect.

[0018] In a fifth aspect, the present application provides a computer program product, including a computer program, which when executed by a processor, implements the steps of the control method of the advanced hydraulic support as described in the first aspect.

[0019] The control method, device, equipment and storage medium of the advanced hydraulic support provided by this application can perform data fusion based on multiple types of sensor data, obtain the sensor fusion data of each type, and obtain the pose estimation data of the hydraulic support based on the sensor fusion data of multiple types, so as to perform closed-loop control on the hydraulic support based on the pose estimation data and the control requirements of the hydraulic support. It can improve the stability and safety of the hydraulic support.

[0020] Additional aspects and advantages of this application will be given in part in the following description, become apparent in part from the following description, or be learned through the practice of this application. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The above and / or additional aspects and advantages of this application will become apparent and easy to understand from the following description of the embodiments in conjunction with the drawings, in which:

[0022] Figure 1 is a schematic flowchart of a control method for an advanced hydraulic support provided by an embodiment of this application;

[0023] Figure 2 is a schematic flowchart of another control method for an advanced hydraulic support provided by an embodiment of this application;

[0024] Figure 3 is a schematic structural diagram of a control device for an advanced hydraulic support provided by an embodiment of this application;

[0025] Figure 4 is a schematic structural diagram of another control device for an advanced hydraulic support provided by an embodiment of this application;

[0026] Figure 5 is a schematic structural diagram of an electronic device provided by an embodiment of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0027] The embodiments of this application will be described in detail below. The examples of the embodiments are shown in the drawings, in which the same or similar reference numerals denote the same or similar elements or elements with the same or similar functions from beginning to end. The embodiments described below by referring to the drawings are exemplary and are intended to explain this application and should not be construed as a limitation of this application.

[0028] The control method and device of the advanced hydraulic support according to the embodiments of this application will be described below with reference to the drawings.

[0029] Figure 1 is a schematic flowchart of a control method for an advanced hydraulic support provided by an embodiment of this application. As Figure 1 shown, the method may include but is not limited to the following steps:

[0030] Step S101: Obtain the sensor data of multiple types of sensors arranged on the hydraulic support.

[0031] Among them, there is at least one sensor of each type.

[0032] Among them, in the embodiments of the present application, the above sensors include at least one of the following: pressure sensor; displacement sensor; inclination sensor; vision sensor; laser ranging sensor.

[0033] Step S102: Perform data fusion processing on the sensor data to obtain the sensor fusion data corresponding to each type of sensor.

[0034] Exemplarily, based on the sensor data corresponding to the sensors of type A and type B, perform sensor data fusion correction on the sensor data corresponding to the sensors of type C to obtain the sensor fusion data corresponding to the sensors of type C, so as to obtain the sensor fusion data corresponding to each type of sensor. Among them, type A, type B, and type C are any one type of sensor, and type A, type B, and type C are different from each other.

[0035] In one implementation manner, the above-mentioned performing data fusion processing on the sensor data to obtain the sensor fusion data corresponding to each type of sensor includes at least one of the following: performing data consistency verification on multiple pressure data to obtain pressure fusion data; performing push travel consistency verification based on the displacement data of the hydraulic support and the displacement data of adjacent hydraulic supports to obtain displacement fusion data; in response to the inclination data being abnormal, compensating the inclination data based on the displacement data and visual data to obtain inclination fusion data; optimizing the data accuracy of the visual data based on the pressure fusion data and / or inclination fusion data to obtain visual fusion data; correcting the tilt angle error of the laser ranging data based on the inclination fusion data and / or displacement fusion data to obtain laser ranging fusion data.

[0036] Exemplarily, based on the pre-established mathematical model of the support hydraulic system, perform pressure consistency verification on the multiple pressure transmission data obtained by multiple pressure sensors to eliminate the abnormal values in the multiple pressure transmission data and obtain the pressure sensor fusion data.

[0037] Exemplarily, obtain the first displacement data of the hydraulic support and the second displacement data of other supports adjacent to the hydraulic support, so as to verify the consistency of the push travel between different hydraulics based on the first hydraulic data and the second hydraulic data. And when the consistency of the push travel between different hydraulics is abnormal, perform data correction on the first hydraulic data by integrating the hydraulic data of multiple hydraulic supports.

[0038] Exemplarily, in combination with pressure and displacement data, the inclination calculation model is dynamically adjusted, and when the inclination sensor fails, indirect data from displacement and vision sensors are used for compensation.

[0039] Exemplarily, based on pressure fusion data and / or inclination fusion data, the data accuracy of vision data is optimized to obtain vision fusion data, so as to eliminate dust interference in the acquisition environment.

[0040] Exemplarily, in response to abnormal inclination data, the inclination data is compensated based on displacement data and vision data to obtain inclination fusion data.

[0041] Exemplarily, based on inclination fusion data and / or displacement fusion data, the tilt angle error of laser ranging data is corrected to obtain laser ranging fusion data.

[0042] Exemplarily, based on the three-dimensional space information of the vision sensor, the ranging accuracy of the laser sensor is verified.

[0043] Step S103: Perform data fusion analysis on the sensor fusion data to obtain pose estimation data of the hydraulic support.

[0044] Exemplarily, according to the physical characteristics of the hydraulic support and historical sensor data, the prior probability distribution parameters of different sensor data are determined, and according to the working principle of the hydraulic support and the measurement model of the sensor, a relationship model between different sensor data and the support posture is established; according to the observation data and the model, the likelihood probability is calculated; according to Bayes' theorem, combining the prior probability and the likelihood probability, the posterior probability is calculated; according to the posterior probability distribution, the posture parameters of the hydraulic support are estimated. And during the processing, the data of different sensors are synchronized based on the time stamp to ensure the timeliness of the fusion data.

[0045] Exemplarily, according to the geometric structure and kinematic model of the hydraulic support, a pose estimation model is established, and a variety of sensor fusion data are substituted into the model for calculation to obtain pose estimation data of the hydraulic support.

[0046] In some embodiments, a Bayesian estimation method can be adopted. Combining pressure fusion data, displacement fusion data and inclination fusion data, pose estimation data of the hydraulic support is obtained; the vision fusion data and laser ranging fusion data are input into a pre-trained deep learning model to obtain position estimation data of the hydraulic support; based on the pose estimation data and the position estimation data, pose estimation data is obtained. Among them, the above deep learning model has obtained the ability to perform pose estimation through pre-learning of vision fusion data and laser ranging fusion data.

[0047] Step S104: Based on the pose estimation data and the preset control requirements of the hydraulic support, generate a first control instruction for the hydraulic support.

[0048] Exemplarily, the control requirements of the hydraulic support are used as the objective function of multi-objective optimization to establish a multi-objective optimization model, and the multi-objective optimization model is solved based on the pose estimation data to obtain the first control instruction of the hydraulic support.

[0049] Step S105: Perform closed-loop control on the hydraulic support based on the first control instruction.

[0050] Exemplarily, a pushing, lifting or rotating instruction is sent to the hydraulic cylinder of the hydraulic support based on the first control instruction, and the execution result of the instruction is monitored in real time. The execution result is compared with the expected value, and accuracy correction is performed according to the comparison result by using the PID (Proportion Integration Differentiation) control algorithm.

[0051] By implementing the embodiments of the present application, data fusion can be performed based on sensor data of multiple types to obtain sensor fusion data of each type, and pose estimation data of the hydraulic support can be obtained based on the sensor fusion data of multiple types, so as to perform closed-loop control on the hydraulic support based on the pose estimation data and the control requirements of the hydraulic support. The stability and safety of the hydraulic support can be improved.

[0052] In some embodiments, the control requirements of the hydraulic support include stability requirements, pushing efficiency requirements and obstacle avoidance requirements. A model can be established based on the control requirements of the hydraulic support, and the pose estimation data is input into the model to generate the first control instruction. As an example, please refer to Figure 2 , Figure 2 is a schematic flowchart of another control method for the advanced hydraulic support provided by the embodiments of the present application. As shown in Figure 2 , the method may include but is not limited to the following steps:

[0053] Step S201: Obtain sensor data of multiple types of sensors arranged on the hydraulic support.

[0054] In the embodiments of the present application, step S201 can be implemented in any one of the embodiments of the present application. The embodiments of the present application do not make any limitations on this and will not be elaborated further.

[0055] Step S202: Perform data fusion processing on the sensor data to obtain sensor fusion data corresponding to each type of sensor.

[0056] In the embodiments of the present application, step S202 can be implemented in any one of the embodiments of the present application. The embodiments of the present application do not make any limitations on this and will not be elaborated further.

[0057] Step S203: Perform data fusion analysis on the sensor fusion data to obtain pose estimation data of the hydraulic support.

[0058] In the embodiments of the present application, step S203 can be implemented in any one of the embodiments of the present application. The embodiments of the present application do not make any limitations in this regard and will not be elaborated further.

[0059] Step S204: Construct a joint cost function based on the stability requirement, the pushing efficiency requirement, and the obstacle avoidance requirement.

[0060] Exemplarily, define cost functions corresponding to the stability requirement, the pushing efficiency requirement, and the obstacle avoidance requirement respectively, and allocate corresponding weight values according to the importance of each requirement. Linearly combine the cost functions of each target according to the weights to obtain the joint cost function.

[0061] Step S205: Generate a first control instruction based on the pose estimation data and the joint cost function.

[0062] Exemplarily, associate the pose estimation data with the control requirements of the aforementioned hydraulic support to establish an association model, so as to obtain index expressions corresponding to each control requirement based on the association model, and then optimize the control parameters of the hydraulic support with the goal of minimizing the joint cost function according to the index expressions to generate a first control instruction.

[0063] Step S206: Perform closed-loop control on the hydraulic support based on the first control instruction.

[0064] In the embodiments of the present application, step S206 can be implemented in any one of the embodiments of the present application. The embodiments of the present application do not make any limitations in this regard and will not be elaborated further.

[0065] By implementing the embodiments of the present application, a joint cost function can be constructed based on the control requirements of the hydraulic support, and the joint cost function can be solved according to the pose estimation data of the hydraulic support to obtain a first control instruction, so as to control the hydraulic support based on the first control instruction.

[0066] In some embodiments, the above method further includes: determining whether there is an abnormality in the hydraulic support based on the sensor fusion data; determining that there is an abnormality in the hydraulic support, and generating a second control instruction based on the sensor fusion data.

[0067] Exemplarily, monitor the pushing stroke of the hydraulic support based on the displacement fusion data, monitor the pushing speed and the motion state at each stage, and generate a second control instruction when it is determined that the pushing speed is abnormal and / or the motion state is abnormal, so as to correct the pushing speed and / or the motion state of the hydraulic support.

[0068] Exemplarily, based on the data fused with the tilt angle, determine whether the attitude of the hydraulic support is stable, and generate a second control instruction when it is determined that the attitude of the hydraulic support is unstable, so as to prevent the hydraulic support from tipping over due to eccentric loading or geological changes.

[0069] Exemplarily, based on the pressure fusion data, determine the roof load and the pressure distribution of each cylinder of the hydraulic support, judge whether the hydraulic support is evenly stressed, and generate a second control instruction when it is determined that the hydraulic support is unevenly stressed, so as to avoid overloading or unloading instability of the hydraulic support.

[0070] By implementing the embodiments of the present application, the state of the hydraulic support can be monitored based on the sensor fusion data, so as to generate a second control instruction when it is determined that the hydraulic support is abnormal, and adjust the hydraulic support in time to avoid danger.

[0071] Please refer to Figure 3 , Figure 3 which is a schematic structural diagram of a control device for a front hydraulic support provided by the embodiments of the present application. As Figure 3 shown, the device 300 includes: an input module 301 for obtaining sensor data of multiple types of sensors arranged on the hydraulic support; wherein, each type of sensor is at least one; a processing module 302 for performing data fusion processing on the sensor data to obtain sensor fusion data corresponding to each type of sensor; the processing module 302 is further configured to: perform data fusion analysis on the sensor data to obtain pose estimation data of the hydraulic support; the processing module 302 is further configured to: generate a control instruction for the hydraulic support based on the pose estimation data and the preset control requirements of the hydraulic support; an output module 303 for performing closed-loop control on the hydraulic support based on the control instruction

[0072] In one implementation, the sensor includes at least one of the following: a pressure sensor; a displacement sensor; an inclination sensor; a vision sensor; a laser ranging sensor.

[0073] In an alternative implementation, the processing module 302 is configured to perform at least one of the following: perform data consistency verification on multiple pressure data to obtain pressure fusion data; perform push travel consistency verification based on the displacement data of the hydraulic support and the displacement data of the adjacent hydraulic support to obtain displacement fusion data; in response to the inclination data being abnormal, compensate the inclination data based on the displacement data and the vision data to obtain inclination fusion data; optimize the data accuracy of the vision data based on the pressure fusion data and / or the inclination fusion data to obtain vision fusion data; correct the tilt angle error of the laser ranging data based on the inclination fusion data and / or the displacement fusion data to obtain laser ranging fusion data.

[0074] In one implementation, the processing module 302 is specifically configured to: use the Bayesian estimation method, combine the pressure fusion data, displacement fusion data, and inclination fusion data to obtain the attitude estimation data of the hydraulic support; input the visual fusion data and laser ranging fusion data into a pre-trained deep learning model to obtain the position estimation data of the hydraulic support; and obtain the pose estimation data based on the attitude estimation data and the position estimation data.

[0075] In one implementation, the processing module 302 is specifically configured to: construct a joint cost function based on the stability requirement, pushing efficiency requirement, and obstacle avoidance requirement; and generate a first control instruction based on the pose estimation data and the joint cost function.

[0076] In one implementation, the processing module 302 is further configured to: determine whether there is an abnormality in the hydraulic support based on the sensor fusion data; and if it is determined that there is an abnormality in the hydraulic support, generate a second control instruction based on the sensor fusion data.

[0077] Through the device according to the embodiments of the present application, data fusion can be performed based on multiple types of sensor data to obtain the sensor fusion data of each type, and the pose estimation data of the hydraulic support can be obtained based on the sensor fusion data of multiple types, so as to perform closed-loop control on the hydraulic support based on the pose estimation data and the hydraulic support control requirements. The stability and safety of the hydraulic support can be improved.

[0078] In one implementation, the above input module 301 and output module 303 support signal types such as analog quantity, digital quantity / switching quantity, have an adaptive function, can automatically identify the external device type and configure control parameters; and configure an A / D conversion module to convert the analog signal into a digital signal.

[0079] In one implementation, the above processing module adopts a heterogeneous ARM architecture, supports real-time multitasking processing and high-parallel computing, and has strong control and data processing capabilities.

[0080] In one implementation, the above input module 301 and output module 303 are built with a miniaturized processing module, which can execute some control tasks and reduce the load on the core board. It supports more flexible configuration and diagnostic functions, can directly perform signal processing and data conversion. It supports multiple communication protocols and can communicate with the core board through Ethernet, serial port, etc.

[0081] In one implementation, the above processing module adopts a modular hardware design, where each functional module is connected through a standardized interface, supports flexible configuration, replacement, and expansion according to on-site requirements, and improves the adaptability, flexibility, and maintainability of the system.

[0082] In one implementation, the above input module 301 and output module 303 support hot plugging.

[0083] In an alternative implementation, the above input module 301 and output module 303 have a self-diagnosis function, which can detect the module status during the plugging and unplugging process and perform automatic configuration. For example, the main core boards of the input module 301 and output module 303 can quickly identify the type, function, and address of the new module, and perform automatic configuration and management according to actual requirements.

[0084] In one implementation, by integrating an embedded microcontroller, driver, and automatic configuration function, the above input module 301 and output module 303 can automatically identify and communicate with the main core board after being inserted, without complex settings.

[0085] In one implementation, the input module 301 includes a sensor expansion module and a control expansion module. The other modules of the control device, the sensor expansion module, and the control expansion module can be independently installed and deployed, and are interconnected through interfaces such as Ethernet, CAN, and Modbus.

[0086] In one implementation, the above controller supports the TSN (Time-Sensitive Networking) function.

[0087] In one implementation, the above controller adopts a distributed architecture, and multiple controllers can work together through network communication to achieve collaborative control and data sharing between devices. For example, the controllers of multiple advanced hydraulic supports can work together through network communication to achieve collaborative control and data sharing between devices.

[0088] As an example, please refer to Figure 4 , Figure 4 is a schematic structural diagram of another control device for an advanced hydraulic support provided by an embodiment of the present application. As shown in Figure 4 , the device 400 further includes: a power management module 404, a network communication module 405, and a thermal management module 406. Among them, Figure 4 the modules 401-403 in Figure 3 have the same structure and function as the modules 301-303 in

[0089] In one implementation, the power management module 404 includes a main power supply, a redundant power supply, and a UPS (Uninterruptible Power Supply) unit.

[0090] In an alternative implementation, the power management module 404 supports automatic power switching, can automatically switch to the backup power supply when the main power supply fails, and continuously supplies power through the UPS unit for more than 30 minutes to ensure the uninterrupted operation of the system.

[0091] In one implementation, the network communication module 405 supports Ethernet, industrial wireless communication protocols (such as 5G, Wi-Fi, LoRa, etc.) and CAN / 485 communication protocols, and can achieve real-time data exchange and collaborative control with third-party devices.

[0092] In one implementation, the thermal management module 406 is used to dynamically adjust the heat dissipation unit according to the temperature and humidity monitoring data (for example, adjust the rotation speed of the heat dissipation fan), prevent the control device from malfunctioning due to overheating, and ensure the continuous and stable operation of the control device in a high-load environment.

[0093] It should be noted that the foregoing explanations of the embodiments of the control method for the advanced hydraulic support also apply to the control device of the advanced hydraulic support in this embodiment, and will not be elaborated here.

[0094] To implement the above embodiments, the present application also proposes an electronic device. Please refer to Figure 5 , Figure 5 which is a schematic structural diagram of the electronic device provided by the embodiments of the present application. As Figure 5 shown, the electronic device 500 includes: a processor 501, and a memory 502 communicatively connected to the processor 501; the memory 502 stores computer-executable instructions; the processor 501 executes the computer-executable instructions stored in the memory to implement the method provided in the foregoing embodiments.

[0095] To implement the above embodiments, the present application also proposes a computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, they are used to implement the method provided in the foregoing embodiments.

[0096] To implement the above embodiments, the present application also proposes a computer program product, including a computer program, which when executed by a processor, implements the method provided in the foregoing embodiments.

[0097] Among them, in the description of the present application, unless otherwise specified, " / " means "or", for example, A / B can represent A or B; "and / or" herein is only a description of the association relationship of associated objects, indicating that three relationships can exist, for example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone.

[0098] In the description of the foregoing embodiments, the descriptions referring to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0099] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present application, "a plurality of" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0100] Any process or method description shown in the flowchart or described in other ways herein may be understood to represent a module, segment, or portion of code including one or more executable instructions for implementing a customized logic function or process, and the scope of the preferred embodiments of the present application includes additional implementations, where the functions may be executed in a substantially simultaneous manner or in a reverse order according to the functions involved, rather than in the order shown or discussed, which should be understood by those skilled in the art to which the embodiments of the present application pertain.

[0101] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in conjunction with these instruction execution systems, apparatuses, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection portion having one or more wirings (electronic device), a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or other suitable processing as necessary, and then stored in a computer memory.

[0102] It should be understood that various parts of the present application can be implemented by hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0103] Those of ordinary skill in the art of this technology can understand that all or part of the steps carried by the method of implementing the above embodiments can be completed by a program instructing relevant hardware, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0104] In addition, each functional unit in various embodiments of the present application may be integrated in a processor, may exist separately physically for each unit, or two or more units may be integrated in a module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0105] The above-mentioned storage medium may be a read-only memory, a magnetic disk, an optical disc, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A control method for an advanced hydraulic support, characterized in that: include: Acquire sensor data of multiple types of sensors disposed on the hydraulic support, wherein each type of sensor is at least one; Performing data fusion processing on the sensor data to obtain sensor fusion data corresponding to each sensor of the type; Performing data fusion analysis on the sensor fusion data to obtain position and posture estimation data of the hydraulic support; Based on the posture estimation data and the preset hydraulic support control requirements, generating a first control instruction for the hydraulic support; The hydraulic support is controlled in a closed loop based on the first control instruction.

2. The method according to claim 1, characterized in that The sensor includes at least one of the following: Pressure sensor; Displacement sensor; Tilt sensor; Vision sensors; Laser distance sensor.

3. The method according to claim 2, characterized in that The performing data fusion processing on the sensor data to obtain sensor fusion data corresponding to each sensor of the type includes at least one of the following: Perform data consistency verification on multiple pressure data to obtain pressure fusion data; Based on the displacement data of the hydraulic support and the displacement data of the adjacent hydraulic supports, the consistency of the displacement stroke is verified to obtain displacement fusion data; In response to the presence of an abnormality in the inclination data, compensating the inclination data based on the displacement data and the visual data to obtain inclination fusion data; Optimizing the data accuracy of the visual data based on the pressure fusion data and / or the inclination fusion data to obtain visual fusion data; Based on the inclination angle fusion data and / or the displacement fusion data, the inclination angle error of the laser ranging data is corrected to obtain the laser ranging fusion data.

4. The method according to claim 3, characterized in that The step of performing data fusion analysis on the sensor fusion data to obtain the position and posture estimation data of the hydraulic support includes: Using a Bayesian estimation method, combining the pressure fusion data, the displacement fusion data and the inclination fusion data, to obtain attitude estimation data of the hydraulic support; Inputting the visual fusion data and the laser ranging fusion data into a pre-trained deep learning model to obtain position estimation data of the hydraulic support; The pose estimation data is acquired based on the posture estimation data and the position estimation data.

5. The method according to claim 1, characterized in that The control requirements include stability requirements, pushing efficiency requirements and obstacle avoidance requirements. The first control instruction of the hydraulic support is generated based on the posture estimation data and the preset hydraulic support control requirements, including: Constructing a joint cost function based on the stability requirement, the moving efficiency requirement and the obstacle avoidance requirement; Based on the pose estimation data and the joint cost function, the first control instruction is generated.

6. The method according to claim 1, characterized in that The method further comprises: Determining whether the hydraulic support has an abnormality based on the sensor fusion data; It is determined that an abnormality exists in the hydraulic support, and a second control instruction is generated based on the sensor fusion data.

7. A control device for an advanced hydraulic support, characterized in that: include: An input module, used to obtain sensor data of multiple types of sensors arranged on the hydraulic support, wherein each type of sensor is at least one; A processing module, used for performing data fusion processing on the sensor data to obtain sensor fusion data corresponding to each type of sensor; The processing module is also used to: perform data fusion analysis on the sensor data to obtain the position and posture estimation data of the hydraulic support; The processing module is further used to: generate a control instruction for the hydraulic support based on the posture estimation data and the preset hydraulic support control requirements; An output module is used to perform closed-loop control on the hydraulic support based on the control instruction.

8. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, they are used to implement the method according to any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises a computer program, which implements the method according to any one of claims 1 to 6 when being executed by a processor.

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