Construction and data acquisition method and system of gear motor control network

Abstract

The invention relates to a construction and data acquisition method and system of a gear motor control network, and belongs to the technical field of gear motor control. Torque data characteristic change data based on a time sequence is constructed, and a dynamic Bayesian network is introduced; inputting the torque data characteristic change data based on the time sequence into a dynamic Bayesian network to perform observation state updating, and obtaining the updated torque data characteristic change data based on the time sequence through the observation state updating; and carrying out torque state abnormity identification and analysis on the updated torque data characteristic change data based on the time sequence, and generating a related control strategy based on an abnormity identification analysis result. By constructing the speed reducer control network, a plurality of speed reducing motors can be monitored, so that abnormal conditions of the speed reducing motors are found in time, the speed reducing motors are controlled in time, abnormal use of the speed reducing motors is avoided, and the service life of the speed reducing motors is prolonged.

Description

Technical Field

[0001] The present invention relates to the technical field of reduction motor control, and in particular to a reduction motor control network construction and data acquisition method and system. Background Art

[0002] A reduction motor refers to an integrated body of a reducer and a motor (motor). This integrated body is also commonly referred to as a gear motor or gear motor. It is usually integrated and assembled by a professional reducer manufacturer and then supplied as a complete set. Reducer motors are widely used in the steel industry, machinery industry, etc. The advantages of using a reduction motor are simplified design and space saving. With the continuous development of the reduction motor industry, more and more industries and enterprises have used reduction motors, and a number of enterprises have also entered the reduction motor industry. The micro reduction motor and DC reduction motor industry were established in the 1950s. After going through the stages of imitation, self-design, research and development, and large-scale manufacturing, it has formed an industrial system with complete supporting facilities such as product development, large-scale production, key components, key materials, special manufacturing equipment, and testing instruments, and its internationalization is constantly improving. However, during the use of the reduction motor, it is affected by external abnormalities, and the torque of the reduction motor may be affected. If the torque abnormality cannot be discovered in time, the reduction motor will be damaged and the service life of the reduction motor will be reduced. Summary of the invention

[0003] The present invention overcomes the deficiencies of the prior art and provides a method and system for constructing a reduction motor control network and collecting data.

[0004] To achieve the above object, the technical solution adopted by the present invention is: The first aspect of the present invention provides a method for constructing a reduction motor control network and collecting data, comprising the following steps: Constructing a reduction motor control network, integrating the data of the reduction motors in the target area into the reduction motor control network, and initializing data acquisition parameters of the reduction motor control network; Setting a data collection cycle, collecting torque data information of each reduction motor within a data collection cycle through the reduction motor control network, and analyzing and processing the torque data information of the reduction motor within a data collection cycle to obtain processed torque data information; Constructing torque data characteristic change data based on time series based on the processed torque data information, introducing a dynamic Bayesian network, and inputting the torque data characteristic change data based on time series into the dynamic Bayesian network for observation state update; By observing the state update, the updated time-series-based torque data characteristic change data is obtained, and the updated time-series-based torque data characteristic change data is subjected to torque state abnormality identification and analysis, and the relevant control strategy is generated based on the abnormality identification and analysis results.

[0005] Further, in the method for constructing a reduction motor control network and collecting data, a reduction motor control network is constructed, data of reduction motors in a target area are integrated into the reduction motor control network, and data collection parameters of the reduction motor control network are initialized, specifically including: Construct a reduction motor control network, obtain the maximum information processing volume of the reduction motor control network within a data collection cycle, and the data size information of a single reduction motor during data collection, and obtain the quantity information of the reduction motors in the current target area; Calculate the total estimated data processing volume under the number information of the reduction motors in the current target area based on the data size information of the single number reduction motors during data collection and the number information of the reduction motors in the current target area; If the total estimated data processing amount under the quantity information of the reduction motors in the current target area is greater than the maximum information processing amount of the reduction motor control network within a data collection cycle, then the quantity information of the reduction motors during data collection within a data collection cycle is reinitialized; Until it is no greater than the maximum information processing amount of the reduction motor control network within one data collection cycle, a data collection plan is constructed according to the quantity information of the reduction motors when performing data collection within one data collection cycle, and data collection planning is performed according to the data collection plan.

[0006] Furthermore, in the construction and data collection method of the reduction motor control network, a data collection period is set, and the torque data information of each reduction motor within a data collection period is collected through the reduction motor control network, and the torque data information of the reduction motor within a data collection period is analyzed and processed to obtain the processed torque data information, specifically: Setting a data collection cycle, collecting torque data information of each reduction motor within a data collection cycle through the reduction motor control network, introducing an outlier detection algorithm, and inputting the torque data information of each reduction motor within a data collection cycle into the outlier detection algorithm for outlier detection; By outlier detection, outlier point data in the torque data information of each reduction motor within a data collection cycle is obtained, and the outlier point data in the torque data information of each reduction motor within a data collection cycle is deleted; After deletion, the sorted torque data information of each reduction motor within a data collection cycle is obtained and output as processed torque data information.

[0007] Furthermore, in the construction and data acquisition method of the reduction motor control network, torque data characteristic change data based on time series is constructed based on the processed torque data information, a dynamic Bayesian network is introduced, and the torque data characteristic change data based on time series is input into the dynamic Bayesian network for observation state update, specifically: Arrange and sort the processed torque data information according to the order of time points, construct torque data feature change data based on time series, and introduce a dynamic Bayesian network; Inputting the torque data characteristic change data based on the time series into the dynamic Bayesian network, and taking the torque data information of each timestamp in the torque data characteristic change data based on the time series as an observation state; Calculate the state transition probability value of each observed state transferring to another observed state, set a state transition probability threshold, and determine whether the state transition probability value of each observed state transferring to another observed state is greater than the state transition probability threshold; The observed state whose state transition probability value is greater than the state transition probability threshold is updated to another observed state, and the observed state whose state transition probability value is not greater than the state transition probability threshold is maintained unchanged.

[0008] Furthermore, in the construction and data acquisition method of the reduction motor control network, by observing the state update, the updated torque data characteristic change data based on the time series is obtained, and the torque state abnormality identification and analysis are performed on the updated torque data characteristic change data based on the time series, specifically including: By observing the state update, obtaining updated torque data characteristic change data based on the time series, and setting a torque data threshold, determining whether there is torque data greater than the torque data threshold in the updated torque data characteristic change data based on the time series; When there is torque data greater than a torque data threshold value in the updated torque data characteristic change data based on the time series, the corresponding reduction motor is regarded as an abnormally operating reduction motor, and an early warning is issued; When there is no torque data greater than the torque data threshold value in the updated time series-based torque data characteristic change data, the output is a normal operation of the reduction motor.

[0009] Furthermore, in the construction of the reduction motor control network and the data acquisition method, the relevant control strategies are generated based on the abnormality identification and analysis results, specifically including: Acquire the real-time torque data of the abnormal reduction motor in the abnormal identification and analysis result, and acquire the estimated maximum torque data of the current abnormal reduction motor, and set the stop working torque threshold according to the estimated maximum torque data of the current abnormal reduction motor; Determine whether the real-time torque data of the abnormal reduction motor in the abnormal identification and analysis result is greater than the stop working torque threshold; When the real-time torque data of the abnormal reduction motor in the abnormal identification and analysis result is greater than the stop-work torque threshold, a stop-work instruction is issued, and the reduction motor is controlled according to the stop-work instruction; When the real-time torque data of the abnormal reduction motor in the abnormal identification and analysis result is not greater than the stop working torque threshold, the current abnormal reduction motor is continuously monitored and an early warning is issued.

[0010] A second aspect of the present invention provides a system for constructing and acquiring data of a reduction motor control network, comprising a memory and a processor, wherein the memory comprises a program for constructing and acquiring data of a reduction motor control network, and when the program for constructing and acquiring data of a reduction motor control network is executed by the processor, the steps of any one of the methods for constructing and acquiring data of a reduction motor control network are implemented.

[0011] The third aspect of the present invention provides a computer-readable storage medium, which includes a program for the construction and data acquisition method of a reduction motor control network. When the program for the construction and data acquisition method of a reduction motor control network is executed by a processor, the steps of any one of the methods for construction and data acquisition of a reduction motor control network are implemented.

[0012] The present invention solves the defects existing in the background technology and has the following beneficial effects: The present invention integrates the data of the reduction motor in the target area into the reduction motor control network by constructing a reduction motor control network, initializes the data acquisition parameters of the reduction motor control network, sets the data acquisition cycle, collects the torque data information of each reduction motor within a data acquisition cycle through the reduction motor control network, analyzes and processes the torque data information of the reduction motor within a data acquisition cycle, obtains the processed torque data information, constructs the torque data characteristic change data based on the time series based on the processed torque data information, introduces the dynamic Bayesian network, and inputs the torque data characteristic change data based on the time series into the dynamic Bayesian network for observation state update, obtains the updated torque data characteristic change data based on the time series through the observation state update, and performs torque state abnormality identification and analysis on the updated torque data characteristic change data based on the time series, and generates relevant control strategies based on the abnormal identification and analysis results. The present invention can monitor multiple reduction motors by constructing a reduction motor control network, so as to timely discover the abnormal situation of the reduction motor, and then timely control the reduction motor, avoid the abnormal use of the reduction motor, and improve the service life of the reduction motor. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, drawings of other embodiments can be obtained based on these drawings without paying creative work.

[0014] Figure 1 The overall flow chart of the construction of the reduction motor control network and the data acquisition method is shown; Figure 2 A partial flow chart showing the construction of a reduction motor control network and a data acquisition method; Figure 3 The system block diagram of the reduction motor control network and the data acquisition system is shown. DETAILED DESCRIPTION

[0015] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0016] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0017] like Figure 1 As shown, the first aspect of the present invention provides a method for constructing a reduction motor control network and collecting data, comprising the following steps: S102: constructing a reduction motor control network, integrating the data of the reduction motors in the target area into the reduction motor control network, and initializing data acquisition parameters of the reduction motor control network; S104: setting a data collection cycle, collecting torque data information of each reduction motor within a data collection cycle through the reduction motor control network, and analyzing and processing the torque data information of the reduction motor within a data collection cycle to obtain processed torque data information; S106: constructing torque data feature change data based on time series based on the processed torque data information, introducing a dynamic Bayesian network, and inputting the torque data feature change data based on time series into the dynamic Bayesian network to update the observation state; S108: Obtain updated time-series-based torque data feature change data by observing the state update, and perform torque state anomaly identification and analysis on the updated time-series-based torque data feature change data, and generate relevant control strategies based on the anomaly identification and analysis results.

[0018] It should be noted that the present invention can monitor multiple reduction motors by constructing a reduction motor control network, so as to promptly detect abnormal conditions of the reduction motors, and then promptly control the reduction motors, avoid abnormal use of the reduction motors, and increase the service life of the reduction motors.

[0019] like Figure 2 As shown, further, in the construction and data acquisition method of the reduction motor control network, the reduction motor control network is constructed, the data of the reduction motor in the target area is integrated into the reduction motor control network, and the data acquisition parameters of the reduction motor control network are initialized, specifically including: S202: constructing a reduction motor control network, and obtaining the maximum information processing amount of the reduction motor control network within a data collection cycle and the data size information of a single number of reduction motors during data collection, and obtaining the number information of the reduction motors in the current target area; S204: Calculating the total estimated data processing volume under the number of reduction motors in the current target area based on the data size information of the single number reduction motors during data collection and the number of reduction motors in the current target area; S206: If the total estimated data processing volume under the quantity information of the reduction motors in the current target area is greater than the maximum information processing volume of the reduction motor control network within a data collection cycle, then reinitialize the quantity information of the reduction motors when performing data collection within a data collection cycle; S208: Until it is no more than the maximum information processing amount of the reduction motor control network within a data collection cycle, a data collection plan is constructed according to the number information of the reduction motors during data collection within a data collection cycle, and data collection planning is carried out according to the data collection plan.

[0020] It should be noted that, in fact, the data that needs to be collected by the reduction motor include real-time speed information, real-time torque information, real-time voltage information, real-time current information, etc. The more data that needs to be collected, the larger the data size information of a single number of reduction motors during data collection will be. For example, the data size that needs to be processed once is 10 megabytes. In fact, the processing power of the computer system has an upper limit, that is, the reduction motor control network has a maximum amount of information processing within a data collection cycle. This method can optimize the number of reduction motors during data collection within a data collection cycle based on the fact that the reduction motor control network has a maximum amount of information processing within a data collection cycle, thereby improving the rationality of data collection.

[0021] It should be noted that obtaining the maximum information processing capacity of the reduction motor control network within a data acquisition cycle specifically includes: By testing the reduction motor control network, the maximum information processing volume change characteristics of the reduction motor control network within a data acquisition cycle under each storage volume data are obtained; Introducing a graph neural network, inputting the maximum information processing volume change characteristics of the reduction motor control network under each storage volume data within a data acquisition cycle into the graph neural network, and using the storage volume data as the first node of the graph neural network; Taking the maximum information processing amount feature as the second node of the graph neural network, constructing a directed description relationship, connecting the first node and the second node based on the directed description relationship, constructing a topological structure graph, and obtaining a related adjacency matrix based on the topological structure graph; constructing a knowledge graph, inputting the related adjacency matrix into the knowledge graph for storage, obtaining the real-time data storage capacity of the current reduction motor control network, and inputting the real-time data storage capacity of the current reduction motor control network into the knowledge graph for data matching; Through data matching, the real-time data storage capacity of the current reduction motor control network below the current data storage capacity is obtained, and the maximum information processing capacity of the current reduction motor control network within a data acquisition cycle is updated according to the real-time data storage capacity of the reduction motor control network below the current data storage capacity.

[0022] It should be noted that due to the influence of data storage capacity, when the computer system is processing data, the larger the data storage capacity, the corresponding data processing capacity gradually decreases. Therefore, this method is used to update the maximum information processing capacity of the current reduction motor control network within a data acquisition cycle, thereby improving the rationality of the construction of the reduction motor control network.

[0023] Furthermore, in the construction and data collection method of the reduction motor control network, a data collection cycle is set, and the torque data information of each reduction motor within a data collection cycle is collected through the reduction motor control network, and the torque data information of the reduction motor within a data collection cycle is analyzed and processed to obtain the processed torque data information, specifically: A data collection cycle is set, and the torque data information of each reduction motor within a data collection cycle is collected through the reduction motor control network. An outlier detection algorithm is introduced, and the torque data information of each reduction motor within a data collection cycle is input into the outlier detection algorithm for outlier detection; By outlier detection, outlier point data in the torque data information of each reduction motor within a data collection cycle is obtained, and outlier point data in the torque data information of each reduction motor within a data collection cycle is deleted; After deletion, the sorted torque data information of each reduction motor within a data collection cycle is obtained and output as processed torque data information.

[0024] It should be noted that since data collection is to collect a number of data within a data collection cycle (such as 10 seconds, 20 seconds, 1 minute, etc.), the data collected within this data collection cycle may be abnormal. The outlier detection algorithm is used to delete abnormal data to make data collection more reasonable.

[0025] Furthermore, in the construction and data acquisition method of the reduction motor control network, the torque data characteristic change data based on the time series is constructed based on the processed torque data information, the dynamic Bayesian network is introduced, and the torque data characteristic change data based on the time series is input into the dynamic Bayesian network for observation state update, specifically: The processed torque data information is sorted and arranged according to the order of time points, the torque data characteristic change data based on time series is constructed, and the dynamic Bayesian network is introduced; Inputting the torque data characteristic change data based on the time series into the dynamic Bayesian network, and taking the torque data information of each timestamp in the torque data characteristic change data based on the time series as an observation state; Calculate the state transition probability value of each observation state transferring to another observation state, set the state transition probability threshold, and determine whether the state transition probability value of each observation state transferring to another observation state is greater than the state transition probability threshold; The observed state whose state transition probability value is greater than the state transition probability threshold is updated to another observed state, and the observed state whose state transition probability value is not greater than the state transition probability threshold is maintained unchanged.

[0026] It should be noted that the state transition probability value of each observation state transferring to another observation state is calculated through the dynamic Bayesian network, and the state transition probability threshold is set. The observation state with a state transition probability value greater than the state transition probability threshold is updated to another observation state, and the observation state with a state transition probability value not greater than the state transition probability threshold is maintained unchanged. In this way, the torque data characteristic change data based on the time series can be updated in time to improve the rationality of monitoring.

[0027] Furthermore, in the construction and data acquisition method of the reduction motor control network, by observing the state update, the updated torque data characteristic change data based on the time series is obtained, and the torque state abnormality identification and analysis are performed on the updated torque data characteristic change data based on the time series, specifically including: By observing the state update, the updated torque data characteristic change data based on the time series is obtained, and the torque data threshold is set to determine whether there is torque data greater than the torque data threshold in the updated torque data characteristic change data based on the time series; When there is torque data greater than the torque data threshold value in the updated torque data characteristic change data based on the time series, the corresponding reduction motor is regarded as an abnormally operating reduction motor and an early warning is issued; When there is no torque data greater than the torque data threshold value in the updated time series-based torque data characteristic change data, the output is the normal operation of the reduction motor.

[0028] It should be noted that in some special scenarios, such as abnormal external factors, the torque data of the reduction motor gradually increases within a predetermined time. When it increases to a certain limit, the reduction motor will cause abnormal operation. This method can identify abnormally operating reduction motors.

[0029] Furthermore, in the construction of the reduction motor control network and the data acquisition method, the relevant control strategies are generated based on the abnormality identification and analysis results, specifically including: Acquire the real-time torque data of the abnormal reduction motor in the abnormal identification and analysis results, and acquire the estimated maximum torque data of the current abnormal reduction motor, and set the stop working torque threshold according to the estimated maximum torque data of the current abnormal reduction motor; Determine whether the real-time torque data of the abnormal reduction motor in the abnormal identification analysis result is greater than the stop working torque threshold; When the real-time torque data of the abnormal reduction motor in the abnormal identification and analysis result is greater than the stop-work torque threshold, a stop-work instruction is issued, and the reduction motor is controlled according to the stop-work instruction; When the real-time torque data of the abnormal reduction motor in the abnormal identification and analysis results is not greater than the stop working torque threshold, the current abnormal reduction motor is continuously monitored and warned.

[0030] It should be noted that, in fact, the motor has a maximum torque parameter under the extreme working parameters. Through this method, the stopping working torque threshold can be set according to the estimated maximum torque data of the current abnormal reduction motor, so as to control the current abnormal reduction motor and improve the service life of the reduction motor.

[0031] Among them, in this method, obtaining the estimated maximum torque data of the current abnormal reduction motor specifically includes: The maximum torque data change characteristics of the reduction motor under various working environments are obtained through big data, and a prediction model for the maximum torque data change characteristics of the reduction motor is constructed based on a deep neural network; Inputting the maximum torque data change characteristics of the reduction motor under the various working environments into the reduction motor maximum torque data change characteristic prediction model for training, and obtaining the trained reduction motor maximum torque data change characteristic prediction model; Obtaining the working environment information of the current abnormal reduction motor and the maximum torque data change characteristics within a preset time, and inputting the working environment information of the current abnormal reduction motor and the maximum torque data change characteristics within a preset time into the trained reduction motor maximum torque data change characteristic prediction model for prediction; The estimated maximum torque data of the abnormal reduction motor in the current timestamp is obtained through prediction, and the estimated maximum torque data of the current abnormal reduction motor is updated according to the estimated maximum torque data of the abnormal reduction motor in the current timestamp.

[0032] It should be noted that the maximum torque data change characteristics of the reduction motor under different working environments (such as temperature, humidity, load conditions, etc.) are different. Through this method, the estimated maximum torque data of the current abnormal reduction motor can be updated to further provide the rationality of monitoring.

[0033] like Figure 3 As shown, the second aspect of the present invention provides a reduction motor control network construction and data acquisition system 4, including a memory 41 and a processor 42, the memory 41 includes a reduction motor control network construction and data acquisition method program, and when the reduction motor control network construction and data acquisition method program is executed by the processor 42, the following steps are implemented: Construct a reduction motor control network, integrate the data of the reduction motors in the target area into the reduction motor control network, and initialize the data acquisition parameters of the reduction motor control network; Setting a data collection cycle, collecting torque data information of each reduction motor within a data collection cycle through a reduction motor control network, and analyzing and processing the torque data information of the reduction motor within a data collection cycle to obtain processed torque data information; Based on the processed torque data information, torque data feature change data based on time series is constructed, a dynamic Bayesian network is introduced, and the torque data feature change data based on time series is input into the dynamic Bayesian network for observation state update; By observing the state update, the updated torque data characteristic change data based on the time series is obtained, and the torque state abnormality identification and analysis are performed on the updated torque data characteristic change data based on the time series, and the relevant control strategy is generated based on the abnormality identification and analysis results.

[0034] Furthermore, in this system, a reduction motor control network is constructed, the data of the reduction motor in the target area is integrated into the reduction motor control network, and the data acquisition parameters of the reduction motor control network are initialized, specifically including: Construct a reduction motor control network, obtain the maximum information processing volume of the reduction motor control network within a data collection cycle, and the data size information of a single reduction motor during data collection, and obtain the quantity information of the reduction motors in the current target area; Based on the data size information of a single number of reduction motors during data collection and the number information of reduction motors in the current target area, the total estimated data processing volume under the number information of reduction motors in the current target area is calculated; If the total estimated data processing volume under the quantity information of the reduction motors in the current target area is greater than the maximum information processing volume of the reduction motor control network within one data collection cycle, the quantity information of the reduction motors during data collection within one data collection cycle is reinitialized; Until it is no greater than the maximum information processing amount of the reduction motor control network within one data collection cycle, a data collection plan is constructed based on the quantity information of the reduction motors when performing data collection within one data collection cycle, and data collection planning is carried out according to the data collection plan.

[0035] Furthermore, in this system, a data collection cycle is set, and the torque data information of each reduction motor within a data collection cycle is collected through the reduction motor control network, and the torque data information of the reduction motor within a data collection cycle is analyzed and processed to obtain the processed torque data information, specifically: A data collection cycle is set, and the torque data information of each reduction motor within a data collection cycle is collected through the reduction motor control network. An outlier detection algorithm is introduced, and the torque data information of each reduction motor within a data collection cycle is input into the outlier detection algorithm for outlier detection; By outlier detection, outlier point data in the torque data information of each reduction motor within a data collection cycle is obtained, and outlier point data in the torque data information of each reduction motor within a data collection cycle is deleted; After deletion, the sorted torque data information of each reduction motor within a data collection cycle is obtained and output as processed torque data information.

[0036] Furthermore, in this system, based on the processed torque data information, torque data feature change data based on time series is constructed, a dynamic Bayesian network is introduced, and the torque data feature change data based on time series is input into the dynamic Bayesian network for observation state update, specifically: The processed torque data information is sorted and arranged according to the order of time points, the torque data characteristic change data based on time series is constructed, and the dynamic Bayesian network is introduced; Inputting the torque data characteristic change data based on the time series into the dynamic Bayesian network, and taking the torque data information of each timestamp in the torque data characteristic change data based on the time series as an observation state; Calculate the state transition probability value of each observation state transferring to another observation state, set the state transition probability threshold, and determine whether the state transition probability value of each observation state transferring to another observation state is greater than the state transition probability threshold; The observed state whose state transition probability value is greater than the state transition probability threshold is updated to another observed state, and the observed state whose state transition probability value is not greater than the state transition probability threshold is maintained unchanged.

[0037] Furthermore, in the system, by observing the state update, the updated torque data characteristic change data based on the time series is obtained, and the torque state abnormality identification and analysis are performed on the updated torque data characteristic change data based on the time series, specifically including: By observing the state update, the updated torque data characteristic change data based on the time series is obtained, and the torque data threshold is set to determine whether there is torque data greater than the torque data threshold in the updated torque data characteristic change data based on the time series; When there is torque data greater than the torque data threshold value in the updated torque data characteristic change data based on the time series, the corresponding reduction motor is regarded as an abnormally operating reduction motor and an early warning is issued; When there is no torque data greater than the torque data threshold value in the updated time series-based torque data characteristic change data, the output is the normal operation of the reduction motor.

[0038] Furthermore, in this system, relevant control strategies are generated based on the abnormality identification and analysis results, including: Acquire the real-time torque data of the abnormal reduction motor in the abnormal identification and analysis results, and acquire the estimated maximum torque data of the current abnormal reduction motor, and set the stop working torque threshold according to the estimated maximum torque data of the current abnormal reduction motor; Determine whether the real-time torque data of the abnormal reduction motor in the abnormal identification analysis result is greater than the stop working torque threshold; When the real-time torque data of the abnormal reduction motor in the abnormal identification and analysis result is greater than the stop-work torque threshold, a stop-work instruction is issued, and the reduction motor is controlled according to the stop-work instruction; When the real-time torque data of the abnormal reduction motor in the abnormal identification and analysis results is not greater than the stop working torque threshold, the current abnormal reduction motor is continuously monitored and warned.

[0039] The third aspect of the present invention provides a computer-readable storage medium, which includes a program for the construction and data acquisition method of a reduction motor control network. When the program for the construction and data acquisition method of a reduction motor control network is executed by a processor, any step of the construction and data acquisition method of a reduction motor control network is implemented.

[0040] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0041] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0042] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0043] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: a mobile storage device, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0044] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods of each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0045] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for constructing a reduction motor control network and collecting data, characterized in that: The following steps are involved: Constructing a reduction motor control network, integrating the data of the reduction motors in the target area into the reduction motor control network, and initializing data acquisition parameters of the reduction motor control network; Setting a data collection cycle, collecting torque data information of each reduction motor within a data collection cycle through the reduction motor control network, and analyzing and processing the torque data information of the reduction motor within a data collection cycle to obtain processed torque data information; Constructing torque data characteristic change data based on time series based on the processed torque data information, introducing a dynamic Bayesian network, and inputting the torque data characteristic change data based on time series into the dynamic Bayesian network to update the observation state; By observing the state update, the updated torque data characteristic change data based on the time series is obtained, and the torque state abnormality identification and analysis are performed on the updated torque data characteristic change data based on the time series, and the relevant control strategy is generated based on the abnormality identification and analysis results.

2. The method for constructing a reduction motor control network and collecting data according to claim 1, characterized in that: Constructing a reduction motor control network, integrating the data of the reduction motors in the target area into the reduction motor control network, and initializing the data acquisition parameters of the reduction motor control network, specifically including: Construct a reduction motor control network, obtain the maximum information processing volume of the reduction motor control network within a data collection cycle, and the data size information of a single reduction motor during data collection, and obtain the quantity information of the reduction motors in the current target area; Calculate the total estimated data processing volume under the number information of the reduction motors in the current target area based on the data size information of the single number reduction motors during data collection and the number information of the reduction motors in the current target area; If the total estimated data processing amount under the quantity information of the reduction motors in the current target area is greater than the maximum information processing amount of the reduction motor control network within a data collection cycle, then the quantity information of the reduction motors during data collection within a data collection cycle is reinitialized; Until it is no greater than the maximum information processing amount of the reduction motor control network within one data collection cycle, a data collection plan is constructed according to the quantity information of the reduction motors when performing data collection within one data collection cycle, and data collection planning is performed according to the data collection plan.

3. The method for constructing a reduction motor control network and collecting data according to claim 1, characterized in that: A data collection cycle is set, and torque data information of each reduction motor within a data collection cycle is collected through the reduction motor control network, and the torque data information of the reduction motor within a data collection cycle is analyzed and processed to obtain the processed torque data information, specifically: Setting a data collection cycle, collecting torque data information of each reduction motor within a data collection cycle through the reduction motor control network, introducing an outlier detection algorithm, and inputting the torque data information of each reduction motor within a data collection cycle into the outlier detection algorithm for outlier detection; By outlier detection, outlier point data in the torque data information of each reduction motor within a data collection cycle is obtained, and the outlier point data in the torque data information of each reduction motor within a data collection cycle is deleted; After deletion, the sorted torque data information of each reduction motor within a data collection cycle is obtained and output as processed torque data information.

4. The method for constructing a reduction motor control network and collecting data according to claim 1, characterized in that: Based on the processed torque data information, torque data feature change data based on time series is constructed, a dynamic Bayesian network is introduced, and the torque data feature change data based on time series is input into the dynamic Bayesian network for observation state update, specifically: Arrange and sort the processed torque data information according to the order of time points, construct torque data feature change data based on time series, and introduce a dynamic Bayesian network; Inputting the torque data characteristic change data based on the time series into the dynamic Bayesian network, and taking the torque data information of each timestamp in the torque data characteristic change data based on the time series as an observation state; Calculate the state transition probability value of each observed state transferring to another observed state, set a state transition probability threshold, and determine whether the state transition probability value of each observed state transferring to another observed state is greater than the state transition probability threshold; The observed state whose state transition probability value is greater than the state transition probability threshold is updated to another observed state, and the observed state whose state transition probability value is not greater than the state transition probability threshold is maintained unchanged.

5. The method for constructing a reduction motor control network and collecting data according to claim 1, characterized in that: By observing the state update, obtaining the updated torque data characteristic change data based on the time series, and performing torque state abnormality identification and analysis on the updated torque data characteristic change data based on the time series, specifically including: By observing the state update, obtaining updated torque data characteristic change data based on the time series, and setting a torque data threshold, determining whether there is torque data greater than the torque data threshold in the updated torque data characteristic change data based on the time series; When there is torque data greater than a torque data threshold value in the updated torque data characteristic change data based on the time series, the corresponding reduction motor is regarded as an abnormally operating reduction motor, and an early warning is issued; When there is no torque data greater than the torque data threshold value in the updated time series-based torque data characteristic change data, the output is a normal operation of the reduction motor.

6. The method for constructing a reduction motor control network and collecting data according to claim 1, characterized in that: Generate relevant control strategies based on the abnormality identification and analysis results, including: Acquire the real-time torque data of the abnormal reduction motor in the abnormal identification and analysis result, and acquire the estimated maximum torque data of the current abnormal reduction motor, and set the stop working torque threshold according to the estimated maximum torque data of the current abnormal reduction motor; Determine whether the real-time torque data of the abnormal reduction motor in the abnormal identification and analysis result is greater than the stop working torque threshold; When the real-time torque data of the abnormal reduction motor in the abnormal identification and analysis result is greater than the stop-work torque threshold, a stop-work instruction is issued, and the reduction motor is controlled according to the stop-work instruction; When the real-time torque data of the abnormal reduction motor in the abnormal identification and analysis result is not greater than the stop working torque threshold, the current abnormal reduction motor is continuously monitored and an early warning is issued.

7. A reduction motor control network construction and data acquisition system, characterized in that: It comprises a memory and a processor, wherein the memory comprises a method program for constructing a reduction motor control network and collecting data, and when the method program for constructing a reduction motor control network and collecting data is executed by the processor, the steps of the method for constructing a reduction motor control network and collecting data as described in any one of claims 1 to 6 are implemented.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a reduction motor control network construction and data acquisition method program. When the reduction motor control network construction and data acquisition method program is executed by a processor, the steps of the reduction motor control network construction and data acquisition method as described in any one of claims 1-6 are implemented.

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