Intelligent control method and system of bucket wheel excavator based on Internet of Things

Abstract

The present invention discloses an intelligent control method and system for bucket wheel machine based on the Internet of Things, which relates to the field of intelligent control technology. The method includes: combining the Internet of Things to build a generalized network system; reading the bucket wheel machine control task and locking the target network loop; identifying the parameter control standard of the bucket wheel machine control task, performing hierarchical collaborative control analysis, and determining the pre-control data based on the task cycle; determining the network performance index, adjusting the network compensation based on the signal response standard of the full control cycle, and determining the network compensation data; combining the pre-control data to perform full-cycle operation control; transmitting back real-time control data and performing over-limit judgment, and performing feedback control of the bucket wheel machine group. The present invention solves the technical problem in the prior art that the control accuracy and stability are low due to the complex motion trajectory and large data processing volume of the bucket wheel machine, and achieves the technical effect of building an intelligent control network for bucket wheel machine based on the Internet of Things to improve the control accuracy and thus improve the operation efficiency.

Description

Technical Field

[0001] The present invention relates to the field of intelligent control technology, and in particular to an intelligent control method and system for a bucket wheel machine based on the Internet of Things. Background Art

[0002] Bucket wheel excavator is a large mechanical equipment used for stacking and loading and unloading of materials. It can quickly complete material loading and unloading work and reduce labor costs. Due to the increase in the complexity of production environment and equipment, traditional control methods are difficult to meet the needs of modern production. Intelligent remote control of bucket wheel excavator is required, but the motion trajectory of bucket wheel excavator includes trolley walking, cantilever pitching, rotation and other actions, which makes the motion trajectory of bucket wheel excavator very complicated. In addition, bucket wheel excavator needs to process a large amount of data during work, which will affect the intelligent control accuracy and stability of bucket wheel excavator. Summary of the invention

[0003] The present application provides an intelligent control method and system for a bucket wheel machine based on the Internet of Things, which is used to solve the technical problem in the prior art that the control accuracy and stability are low due to the complex motion trajectory and large data processing volume of the bucket wheel machine.

[0004] The first aspect of the present application provides an intelligent control method for a bucket wheel machine based on the Internet of Things, the method comprising: combining the Internet of Things, performing hierarchical gateway analysis based on intelligent control, and constructing a generalized network system; reading the bucket wheel machine control task, identifying the complexity of the task control, and locking the target network loop based on the generalized network system; identifying the parameter control standard based on the bucket wheel machine control task, combining the target network loop, performing hierarchical collaborative control analysis, and determining pre-control data based on the task cycle, wherein the control target includes a monitoring sensor device and a bucket wheel machine unit; determining network performance indicators based on the target network loop, performing network compensation adjustment based on the signal response standard of the full control cycle, and determining network compensation data based on the task cycle, wherein the network performance indicators include network delay, network bandwidth and throughput; combining the pre-control data and the network compensation data, and combining the intelligent control module to perform full-cycle operation control of the bucket wheel machine control task; transmitting real-time control data and performing over-limit judgment, and performing feedback regulation of the bucket wheel machine unit.

[0005] The second aspect of the present application provides an intelligent control system for bucket wheel excavators based on the Internet of Things, the system comprising: a generalized network system construction module, the generalized network system construction module is used to combine the Internet of Things, perform hierarchical gateway analysis based on intelligent control, and build a generalized network system; a target network loop locking module, the target network loop locking module is used to read the bucket wheel excavator control task, identify the complexity of task control, and lock the target network loop based on the generalized network system; a pre-control data determination module, the pre-control data determination module is used to identify the control standard based on the bucket wheel excavator control task, combine the target network loop, perform hierarchical collaborative control analysis, and determine the pre-control data based on the task cycle, wherein the control The targets include monitoring sensors and bucket wheel units; a network compensation data determination module, which is used to determine network performance indicators based on the target network loop, perform network compensation adjustments based on the signal response standards of the full control cycle, and determine network compensation data based on the task cycle, wherein the network performance indicators include network delay, network bandwidth and throughput; a full-cycle operation control module, which is used to combine the pre-control data with the network compensation data, and combine with the intelligent control module to perform full-cycle operation control of the bucket wheel control task; a feedback control module, which is used to transmit real-time control data and perform over-limit judgment, and perform feedback control of the bucket wheel unit.

[0006] In a third aspect, the present application provides an electronic device comprising: a processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the steps of any one of the methods described in the first aspect above.

[0007] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the steps of any one of the methods described in the first aspect when executed.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0009] The intelligent control method for bucket wheel excavator based on Internet of Things provided in the present application relates to the field of intelligent control technology. A generalized network system is constructed through Internet of Things, bucket wheel excavator control tasks are read, target network loops are locked, hierarchical collaborative control analysis is performed, pre-control data based on task cycles is determined, network compensation adjustment is performed based on network performance indicators and signal response standards of the entire control cycle, network compensation data is determined, and full-cycle operation management and control are performed in combination with pre-control data. The technical problem of low control accuracy and stability due to the complex motion trajectory and large data processing volume of bucket wheel excavators in the prior art is solved, and the technical effect of improving control accuracy, thereby improving operation efficiency, and reducing energy consumption and equipment wear is achieved by constructing an intelligent control network for bucket wheel excavators based on Internet of Things. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0011] Figure 1 A schematic diagram of a process flow of an intelligent control method for a bucket wheel excavator based on the Internet of Things provided in an embodiment of the present application;

[0012] Figure 2 A schematic diagram of a process for constructing a generalized network system in combination with the Internet of Things in the intelligent control method for bucket wheel excavators based on the Internet of Things provided in an embodiment of the present application;

[0013] Figure 3 A schematic diagram of a process for performing full-cycle operation control of a bucket wheel machine control task in an intelligent control method of a bucket wheel machine based on the Internet of Things provided in an embodiment of the present application;

[0014] Figure 4 A schematic diagram of the structure of an intelligent control system for a bucket wheel excavator based on the Internet of Things provided in an embodiment of the present application;

[0015] Figure 5 A structural schematic diagram of an electronic device is provided for the present application.

[0016] Explanation of the accompanying drawings: generalized network system construction module 11, target network loop locking module 12, pre-control data determination module 13, network compensation data determination module 14, full-cycle operation management and control module 15, feedback control module 16, electronic device 300, memory 301, processor 302, communication interface 303, bus architecture 304. DETAILED DESCRIPTION

[0017] The present application provides an intelligent control method for a bucket wheel machine based on the Internet of Things, which is used to solve the technical problems in the prior art of low control accuracy and stability due to the complex motion trajectory and large amount of data processing of the bucket wheel machine.

[0018] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0019] It should be noted that the terms "first", "second", etc. in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices.

[0020] Embodiment 1

[0021] like Figure 1 As shown, the present application provides an intelligent control method for a bucket wheel machine based on the Internet of Things, the method comprising:

[0022] P10: Combined with the Internet of Things, conduct hierarchical gateway analysis based on intelligent control and build a generalized network system;

[0023] Further, such as Figure 2 As shown, step P10 of the embodiment of the present application also includes:

[0024] P11: Perform gateway interconnection of lower-level controllers to generate a primary network communication layer. The lower-level controllers include independent distributed monitoring sensor devices and control devices;

[0025] P12: For the first-level network communication layer, collaborative interconnection is performed to determine the second-level network communication layer;

[0026] P13: As the level of collaborative control increases, the establishment of N-level network communication layers is completed, where each level of network communication layer includes the perception layer, network layer and application layer;

[0027] P14: Based on the first-level network communication layer, the second-level network communication layer and up to the N-level network communication layer, the generalized network system is generated.

[0028] It should be understood that, based on the Internet of Things, an intelligent control network for bucket wheel excavator units is established through information sensing equipment, and the gateways of low-level controllers are first interconnected. The low-level controller refers to a controller directly connected to basic equipment such as bucket wheel excavators and belt conveyors, including independent distributed monitoring sensing equipment and control equipment, which can be used to monitor the equipment status and control the working parameters of basic equipment. Furthermore, the gateways of the low-level controllers are communicated and interconnected to generate a first-level network communication layer, that is, an independent control network communication layer.

[0029] Furthermore, when there are multiple devices working in coordination, multiple low-level controllers are required for coordinated control. Then, for the first-level network communication layer, the associated controllers are interconnected in coordination to determine the second-level network communication layer. And so on. With the increase of the level of coordinated control, the network association continues until all network communication layers are established. It is assumed here that there are N levels of coordinated control layers. In addition, each level of network communication layer includes a perception layer, a network layer, and an application layer. The perception layer is used to receive multi-source monitoring data of each device when it is working. The network layer is used to transmit and transfer monitoring data. The application layer is used to analyze the control status of the equipment according to the monitoring data, and adjust the corresponding equipment parameters according to the control status analysis results to ensure the normal operation of the bucket wheel machine unit. The application layer can be trained by collecting the historical operating status data of the bucket wheel machine as training data and combining the machine learning principle to perform supervised training until the output data converges.

[0030] The generalized network system is composed of the first-level network communication layer, the second-level network communication layer and the N-level network communication layer, and can provide a bucket wheel excavator task execution network.

[0031] P20: Read the bucket wheel machine control task, identify the complexity of the task control, and lock the target network loop based on the generalized network system;

[0032] Specifically, the target control tasks of the bucket wheel machine are received and read, which may be control tasks such as cantilever pitch, rotation, material picking, and stacking. According to the task type, the complexity of the task control, that is, the control difficulty of the target control task, is identified, and then the target network loop that needs to use the generalized network system is locked. The low-level controllers are connected through the target network loop to control each grassroots equipment to complete the target production task.

[0033] P30: Identify the control parameters based on the bucket wheel machine control task, combine the target network loop, perform hierarchical collaborative control analysis, and determine the pre-control data based on the task cycle, wherein the control target includes the monitoring sensor device and the bucket wheel machine group;

[0034] Furthermore, step P30 of the embodiment of the present application further includes:

[0035] P31: Based on the bucket wheel excavator control task, determine the target operation mode, wherein the operation mode includes stacking operation and reclaiming operation based on the automatic control mode or the semi-automatic control mode;

[0036] P32: For the target operation mode, determine the node control information in the whole task cycle as the control standard.

[0037] Exemplarily, different parameter control standards need to be executed in different control modes. Therefore, it is necessary to determine the target operation mode based on the bucket wheel excavator control task. The operation mode includes stacking operation and material retrieving operation under the automatic control mode, and stacking operation and material retrieving operation under the semi-automatic control mode. After the target operation mode is determined, the parameter control standard of each task node in the whole task cycle is determined for the target operation mode as the parameter control standard. Furthermore, in combination with the target network loop, hierarchical collaborative control analysis is performed, that is, collaborative control parameters of each level are selected according to control experience data, and control parameters of each control node in the whole cycle of the target control task are determined, that is, pre-control data.

[0038] Among them, the control targets of each control node include monitoring sensors and bucket wheel units. The monitoring sensors include image sensors, temperature sensors, etc. The bucket wheel units include bucket wheel machines and other auxiliary equipment, such as belt conveyors, loaders, etc.

[0039] Furthermore, step P30 of the embodiment of the present application further includes:

[0040] P33: Identify the pre-control data and determine whether there is common rail collision data, wherein the common rail collision includes collisions between units and between components in the same unit;

[0041] P34: Identifying a collision characteristic value based on the common rail collision data;

[0042] P35: Taking the time difference and trajectory difference as the control direction, the pre-control data is compensated and adjusted.

[0043] In a possible embodiment of the present application, after determining the pre-control data based on the task cycle, it is necessary to conduct a collision risk check between each unit and each device based on the pre-control data, and perform avoidance adjustments to avoid the collision risk between the units and the equipment. First, a target control task simulation is performed based on the pre-control data. This can be achieved by constructing a three-dimensional simulation model, and based on the simulation data, common rail collision data is extracted, that is, collision data between different bucket wheel units and between components in the same unit, such as a collision between a bucket wheel machine and a conveyor. Further, collision characteristic values ​​based on the common rail collision data are identified, including the overlap range of the equipment at the time of collision, the excess spacing, etc., and by calculating the time difference and trajectory difference of the operation of each device, the pre-control data is compensated and adjusted to adjust the time sequence and position of each device when working together to avoid equipment collision risks and improve work efficiency.

[0044] P40: Determine a network performance indicator based on the target network loop, perform network compensation adjustment based on a signal response standard of the full control cycle, and determine network compensation data based on a task cycle, wherein the network performance indicator includes network delay, network bandwidth and throughput;

[0045] Specifically, the network performance indicators of the target network loop are determined, including the network delay, network bandwidth and throughput of the target network loop, wherein the network delay is the delay duration of the task instruction during the transmission process, the network bandwidth is the maximum transmission rate per unit time, and the throughput is the actual transmission rate at a certain moment. Furthermore, it is determined whether the network performance indicators of the target network loop meet the signal response standards of the entire control cycle. If not, network compensation adjustments are made based on the signal response standards, such as network upgrades, external plug-ins, etc., to determine the network compensation data based on the task cycle to optimize the target network loop, so as to improve the propagation and response timeliness of the control instructions and reduce the control error.

[0046] P50: Combining the pre-control data and the network compensation data with the intelligent control module to perform full-cycle operation control of the bucket wheel excavator control task;

[0047] Further, such as Figure 3 As shown, step P50 of the embodiment of the present application also includes:

[0048] P51: Based on the perception layer of the target network loop, monitor and collect multi-source monitoring sensor data, perform correlation integration based on independence and collaboration, and determine control and supervision data, wherein the control and supervision data includes a single data set and a collaborative data set;

[0049] P52: In combination with the network layer, the control supervision data flow is transferred to the application layer, and the control state assessment is performed to determine the feedback response instructions, which act on the control deviation device.

[0050] Optionally, the pre-control data and the network compensation data are used to perform full-cycle operation control of the bucket wheel machine control task through an intelligent control module. The intelligent control module is a module used for intelligent operation control of bucket wheel machine, which is embedded with the generalized network system and can perform equipment parameter control for different bucket wheel machine control tasks through the corresponding target network loop. First, through the perception layer of the target network loop, multi-source monitoring sensor data, that is, monitoring data from multiple sensors, is monitored and collected, and independent data and collaborative data are associated and integrated according to the subtask type. Independent data and collaborative data are analyzed separately. For example, independent data analysis is performed based on the first layer of the mapped control layer, and collaborative control analysis is performed based on the second layer, the third layer, etc., to obtain a single data set based on independent control and a collaborative data set based on collaborative control, which are used together as control supervision data.

[0051] Furthermore, the control supervision data is transferred to the application layer through the network layer, and the control status is evaluated through the application layer to obtain the current bucket wheel machine operation status evaluation result, including the operation status evaluation of each bucket wheel unit and auxiliary equipment, and to determine whether the current bucket wheel machine operation status evaluation result exceeds the allowable range. If so, a feedback response instruction is generated, that is, a control parameter adjustment instruction, which acts on the corresponding control deviation device to adjust the equipment error and improve production quality.

[0052] P60: Send back real-time control data and make over-limit judgments, and perform feedback control of the bucket wheel unit.

[0053] Furthermore, step P60 of the embodiment of the present application also includes:

[0054] P61: Based on the control effect calculation formula, read the real-time control data to perform control over-limit calculation and determine the over-limit coefficient;

[0055] The control effect calculation formula is as follows:

[0056]

[0057] Where n is the number of bucket wheel units, x k is the excess value of unit operating efficiency, α1 is the configuration weight of operating efficiency, y k is the excess value of unit energy consumption, α2 is the configuration weight of unit energy consumption, m k is the excess value of system control stability, α3 is the configuration weight of system control stability;

[0058] P62: If the excess coefficient meets the coefficient threshold, a feedback control instruction is generated.

[0059] Specifically, the real-time control data is transmitted back to the intelligent control module, and an over-limit judgment is made, that is, an over-limit assessment of the operating efficiency, unit energy consumption, and system control stability is performed to determine whether each control indicator is within the allowable range. First, a control effect calculation formula is constructed: Where n is the number of bucket wheel units, x k is the excess value of unit operating efficiency, α1 is the configuration weight of operating efficiency, y k is the excess value of unit energy consumption, α2 is the configuration weight of unit energy consumption, m k is the system control stability over-limit value, α3 is the configuration weight of the system control stability. Furthermore, according to the control effect calculation formula, the real-time control data is read to perform control over-limit calculation and determine the over-limit coefficient. If the over-limit coefficient reaches the coefficient threshold, it means that there is a control indicator that exceeds the allowable range, then a feedback control instruction is generated to adjust the control parameters to ensure smooth operation of the system.

[0060] Furthermore, the embodiment of the present application further includes step P70, and step P70 further includes:

[0061] P71: Based on the predetermined control time zone, a time zone control record is collected, wherein the predetermined control time zone is a phased time zone within the task cycle;

[0062] P72: Identify the time zone control records and mine abnormal control data that meets the frequency threshold;

[0063] P73: Activate the model learning mode to perform learning updates on the intelligent control module based on the abnormal control data.

[0064] It should be understood that the phased time zones within the task cycle are set as predetermined control time zones, and based on the predetermined control time zones, time zone control records are collected, that is, control record data of the specified working stage is recorded. Further, the time zone control records are identified, and abnormal control data mining is performed, including mining of data such as operating errors and equipment failures, and screening out abnormal control data that meet the frequency threshold, that is, obtaining abnormal data with a higher frequency of occurrence. The frequency threshold can be set according to actual needs, for example, failures occur more than 5 times a week. Further, for the abnormal control data, the model learning mode is activated, that is, the intelligent update mode of the intelligent control module, and the intelligent control module is subjected to learning and updating based on the abnormal control data, so as to achieve the purpose of optimizing and updating the intelligent system.

[0065] In summary, the embodiments of the present application have at least the following technical effects:

[0066] This application constructs a generalized network system through the Internet of Things, reads bucket wheel machine control tasks, locks the target network loop, performs hierarchical collaborative control analysis, determines pre-control data based on the task cycle, makes network compensation adjustments based on network performance indicators and signal response standards for the entire control cycle, determines network compensation data, combines pre-control data, performs full-cycle operation management and control, transmits real-time control data and makes over-limit judgments, and performs feedback regulation of the bucket wheel machine group.

[0067] The technical effect of building an intelligent control network for bucket wheel excavators based on the Internet of Things to improve control accuracy, thereby improving operating efficiency, and reducing energy consumption and equipment wear has been achieved.

[0068] Embodiment 2

[0069] Based on the same inventive concept as the bucket wheel machine intelligent control method based on the Internet of Things in the aforementioned embodiment, Figure 4 As shown, the present application provides an intelligent control system for bucket wheel excavators based on the Internet of Things. The system and method embodiments in the present application are based on the same inventive concept. The system includes:

[0070] A generalized network system construction module 11, which is used to combine the Internet of Things, perform hierarchical gateway analysis based on intelligent control, and build a generalized network system;

[0071] A target network loop locking module 12, the target network loop locking module 12 is used to read the bucket wheel machine control task, identify the task control complexity, and lock the target network loop based on the generalized network system;

[0072] A pre-control data determination module 13, the pre-control data determination module 13 is used to identify the control standard based on the bucket wheel machine control task, combine the target network loop, perform hierarchical collaborative control analysis, and determine the pre-control data based on the task cycle, wherein the control target includes the monitoring sensor device and the bucket wheel machine group;

[0073] A network compensation data determination module 14, the network compensation data determination module 14 is used to determine the network performance index based on the target network loop, perform network compensation adjustment based on the signal response standard of the control full cycle, and determine the network compensation data based on the task cycle, wherein the network performance index includes network delay, network bandwidth and throughput;

[0074] A full-cycle operation control module 15, which is used to combine the pre-control data and the network compensation data with the intelligent control module to perform full-cycle operation control of the bucket wheel excavator control task;

[0075] The feedback control module 16 is used to transmit real-time control data and make over-limit judgments, and perform feedback control of the bucket wheel unit.

[0076] Furthermore, the generalized network system construction module 11 is also used to perform the following steps:

[0077] Performing gateway interconnection of lower-level controllers to generate a primary network communication layer, wherein the lower-level controllers include independent distributed monitoring sensor devices and control devices;

[0078] For the first-level network communication layer, collaborative interconnection is performed to determine the second-level network communication layer;

[0079] As the level of collaborative control increases, the establishment of N-level network communication layers is completed, where each level of network communication layer includes the perception layer, network layer and application layer;

[0080] The generalized network system is generated based on the primary network communication layer, the secondary network communication layer and up to the N-level network communication layer.

[0081] Furthermore, the pre-control data determination module 13 is further configured to perform the following steps:

[0082] Based on the bucket wheel excavator control task, a target operation mode is determined, wherein the operation mode includes a stacking operation and a reclaiming operation based on an automatic control mode or a semi-automatic control mode;

[0083] According to the target operation mode, the node control information in the whole task cycle is determined as the control standard.

[0084] Furthermore, the pre-control data determination module 13 is further configured to perform the following steps:

[0085] Identify the pre-control data and determine whether there is common rail collision data, wherein the common rail collision includes collisions between units and between components in the same unit;

[0086] identifying a collision characteristic value based on the common rail collision data;

[0087] The pre-control data is compensated and adjusted based on the time difference and trajectory difference as the control direction.

[0088] Furthermore, the full-cycle operation control module 15 is also used to perform the following steps:

[0089] Based on the perception layer of the target network loop, multi-source monitoring sensor data is monitored and collected, and independent and collaborative correlation integration is performed to determine control and supervision data, wherein the control and supervision data includes a single data set and a collaborative data set;

[0090] In combination with the network layer, the control supervision data is transferred to the application layer, and a control state assessment is performed to determine a feedback response instruction, which acts on the control deviation device.

[0091] Furthermore, the feedback control module 16 is further configured to perform the following steps:

[0092] Based on the control effect calculation formula, the real-time control data is read to perform control over-limit calculation and determine the over-limit coefficient;

[0093] The control effect calculation formula is as follows:

[0094]

[0095] Where n is the number of bucket wheel units, x k is the excess value of unit operating efficiency, α1 is the configuration weight of operating efficiency, y k is the excess value of unit energy consumption, α2 is the configuration weight of unit energy consumption, m k is the excess value of system control stability, α3 is the configuration weight of system control stability;

[0096] If the excess coefficient meets the coefficient threshold, a feedback control instruction is generated.

[0097] Furthermore, the system also includes:

[0098] A time zone control record collection module, the time zone control record collection module is used to collect time zone control records based on a predetermined control time zone, wherein the predetermined control time zone is a phased time zone within a task cycle;

[0099] An abnormal control data mining module, the abnormal control data mining module is used to identify the time zone control record and mine abnormal control data that meets a frequency threshold;

[0100] A learning update module is used to activate a model learning mode and perform learning updates on the intelligent control module based on the abnormal control data.

[0101] Embodiment 3

[0102] Based on the same inventive concept as the bucket wheel machine intelligent control method based on the Internet of Things in the aforementioned embodiment, the present application also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method in embodiment one is implemented.

[0103] Through the above detailed description of the intelligent control method of bucket wheel machine based on the Internet of Things, those skilled in the art can clearly know the intelligent control method and system of bucket wheel machine based on the Internet of Things in this embodiment, so for the sake of brevity of the specification, it will not be described in detail here. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0104] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0105] Reference below Figure 5 To describe the electronic device of the embodiment of the present application:

[0106] The electronic device 300 includes: a processor 302, a communication interface 303, and a memory 301. Optionally, the electronic device 300 may also include a bus architecture 304. The communication interface 303, the processor 302, and the memory 301 may be interconnected via the bus architecture 304; the bus architecture 304 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus. The bus architecture 304 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0107] The processor 302 may be a CPU, a microprocessor, an ASIC, or one or more integrated circuits for controlling the execution of the program of the present application.

[0108] The communication interface 303 uses any transceiver-like device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), wired access network, etc.

[0109] The memory 301 may be a ROM or other types of static storage devices that can store static information and instructions, a RAM or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory may exist independently and be connected to the processor through a bus architecture 304. The memory may also be integrated with the processor, wherein the memory 301 is used to store computer-executable instructions for executing the solution of the present application, and the execution is controlled by the processor 302. The processor 302 is used to execute the computer-executable instructions stored in the memory 301.

[0110] An embodiment of the present application also provides a computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable a processor to implement the bucket wheel machine intelligent control method based on the Internet of Things in the aforementioned embodiment when executed.

[0111] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0112] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0113] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. The intelligent control method of bucket wheel excavator based on Internet of Things is characterized by: The method comprises: Combined with the Internet of Things, we conduct hierarchical gateway analysis based on intelligent control and build a generalized network system; Read the bucket wheel machine control task, identify the complexity of the task control, and lock the target network loop based on the generalized network system; Identify the control parameters based on the bucket wheel machine control task, combine the target network loop, perform hierarchical collaborative control analysis, and determine the pre-control data based on the task cycle, wherein the control target includes the monitoring sensor device and the bucket wheel machine group; Determine a network performance indicator based on the target network loop, perform network compensation adjustment based on a signal response standard of the full control cycle, and determine network compensation data based on a task cycle, wherein the network performance indicator includes network delay, network bandwidth and throughput; Combining the pre-control data with the network compensation data and the intelligent control module, the full-cycle operation control of the bucket wheel excavator control task is performed; Feedback real-time control data and make over-limit judgments, and conduct feedback control of bucket wheel units; Combining the Internet of Things to build a generalized network system, the method includes: Performing gateway interconnection of lower-level controllers to generate a primary network communication layer, wherein the lower-level controllers include independent distributed monitoring sensor devices and control devices; For the first-level network communication layer, collaborative interconnection is performed to determine the second-level network communication layer; As the level of collaborative control increases, the establishment of N-level network communication layers is completed, where each level of network communication layer includes the perception layer, network layer and application layer; The generalized network system is generated based on the primary network communication layer, the secondary network communication layer and up to the N-level network communication layer.

2. The method according to claim 1, characterized in that Identifying a control parameter standard based on the bucket wheel machine control task, the method comprising: Based on the bucket wheel excavator control task, a target operation mode is determined, wherein the operation mode includes a stacking operation and a reclaiming operation based on an automatic control mode or a semi-automatic control mode; According to the target operation mode, the node control information in the whole task cycle is determined as the control standard.

3. The method according to claim 1, characterized in that After determining the pre-control data based on the task cycle, the method includes: Identify the pre-control data and determine whether there is common rail collision data, wherein the common rail collision includes collisions between units and between components in the same unit; identifying a collision characteristic value based on the common rail collision data; The pre-control data is compensated and adjusted based on the time difference and trajectory difference as the control direction.

4. The method according to claim 1, characterized in that Performing full-cycle operation control of the bucket wheel excavator control task, the method comprises: Based on the perception layer of the target network loop, multi-source monitoring sensor data is monitored and collected, and independent and collaborative correlation integration is performed to determine control and supervision data, wherein the control and supervision data includes a single data set and a collaborative data set; In combination with the network layer, the control supervision data is transferred to the application layer, and a control state assessment is performed to determine a feedback response instruction, which acts on the control deviation device.

5. The method according to claim 4, characterized in that The method includes: Based on the predetermined control time zone, a time zone control record is collected, wherein the predetermined control time zone is a phased time zone within the task cycle; Identify the time zone control records and mine abnormal control data that meets a frequency threshold; A model learning mode is activated to perform learning updates based on the abnormal control data on the intelligent control module.

6. The method according to claim 1, characterized in that The method of returning real-time control data and making an over-limit determination includes: Based on the control effect calculation formula, the real-time control data is read to perform control over-limit calculation and determine the over-limit coefficient; The control effect calculation formula is as follows: Where n is the number of bucket wheel units, x k is the excess value of unit operating efficiency, α1 is the configuration weight of operating efficiency, y k is the excess value of unit energy consumption, α2 is the configuration weight of unit energy consumption, m k is the excess value of system control stability, α3 is the configuration weight of system control stability; If the excess coefficient meets the coefficient threshold, a feedback control instruction is generated.

7. The intelligent control system of bucket wheel excavator based on Internet of Things is characterized by: The system is used to execute the bucket wheel machine intelligent control method based on the Internet of Things according to any one of claims 1 to 6, and the system includes: A generalized network system construction module, which is used to combine the Internet of Things, perform hierarchical gateway analysis based on intelligent control, and build a generalized network system; A target network loop locking module, the target network loop locking module is used to read the bucket wheel machine control task, identify the task control complexity, and lock the target network loop based on the generalized network system; A pre-control data determination module, the pre-control data determination module is used to identify the control standard based on the bucket wheel machine control task, combine the target network loop, perform hierarchical collaborative control analysis, and determine the pre-control data based on the task cycle, wherein the control target includes the monitoring sensor device and the bucket wheel machine group; A network compensation data determination module, the network compensation data determination module is used to determine network performance indicators based on the target network loop, perform network compensation adjustment based on the signal response standard of the control full cycle, and determine network compensation data based on the task cycle, wherein the network performance indicators include network delay, network bandwidth and throughput; A full-cycle operation control module, which is used to combine the pre-control data and the network compensation data with the intelligent control module to perform full-cycle operation control of the bucket wheel excavator control task; A feedback control module is used to transmit real-time control data back and make over-limit judgments, and perform feedback control of the bucket wheel unit.

8. An electronic device, characterized in that: The electronic device comprises: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the intelligent control method for bucket wheel excavator based on the Internet of Things as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the intelligent control method for a bucket wheel machine based on the Internet of Things as described in any one of claims 1 to 6 when executed.

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