Rotary transmission device data processing method and system based on 5G and edge calculation

Abstract

The invention discloses a rotary transmission device data processing method and system based on 5G and edge computing, and belongs to the technical field of data processing, and the method comprises the following steps: S1, analyzing to obtain a light load state node; s2, obtaining a target light load state node and carrying out data processing; s3, analyzing to obtain a fluctuation amplitude value of the rotation transmission data, thereby adjusting a target light load state node filtering parameter, and processing to obtain to-be-uploaded data; s4, obtaining an initial compression result; s5, obtaining a primary compression execution result; and S6, obtaining a secondary compression execution result, executing corresponding compression processing, and then uploading the to-be-uploaded data of the transfer cache station to the cloud, thereby solving the problems of low data processing efficiency and poor timeliness caused by insufficient flexibility of processing resources in the prior art.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular, to a data processing method and system for a rotary transmission device based on 5G and edge computing. Background Art

[0002] With the rapid development of industrial automation and intelligent manufacturing, rotary transmission devices are increasingly widely used in fields such as automobile manufacturing. In the production process, there are high requirements for the timeliness of the operating state and data analysis of rotary transmission devices. The existing data processing methods are realized through a centralized cloud computing center.

[0003] For example, the data processing method and data processing device disclosed in the invention patent announcement with the publication number of CN111950849B include: the data processing device configures a first sub-resource from a first resource for a first sub-task; the data processing device pre-configures a second sub-resource from the first resource for a second sub-task, where the second sub-task and the first sub-task are any two sub-tasks among the multiple sub-tasks included in the target task; the data processing device configures a third sub-resource from a second resource for the second sub-task based on the second sub-resource, and the second resource is the resource in the first resource except the first sub-resource.

[0004] However, in the process of implementing the inventive technical solution in the embodiments of the present application, it is found that the above technologies have at least the following technical problems:

[0005] In the prior art, the data obtained by edge nodes is usually processed in a centralized manner. However, for the batch data generated by rotary transmission devices, the amount of data generated is large and the frequency is fast. Therefore, a large bandwidth and more computing resources are required to achieve low-latency data upload and data analysis and processing. The traditional data processing method relies on a centralized cloud computing center, resulting in high data transmission latency and large bandwidth occupancy, and it is difficult to meet the strict requirements of rotary transmission devices for real-time performance and accuracy. Therefore, there are problems of low data processing efficiency and poor timeliness due to insufficient flexibility of processing resources. Summary of the Invention

[0006] The embodiments of the present application provide a data processing method and system for a rotary transmission device based on 5G and edge computing, which solve the problems of low data processing efficiency and poor timeliness caused by insufficient flexibility of processing resources in the prior art, and realize the timely processing and upload of data.

[0007] The embodiment of the present application provides a data processing method for a rotary transmission device based on 5G and edge computing, including the following steps: S1. After the detection device receives the rotary transmission device data acquisition signal, obtain the resource status parameters of each edge node, and analyze to obtain the lightly loaded state nodes; S2. Obtain the rotary transmission data rate change parameter within a preset time period, analyze to obtain the resource status demand value of the data acquisition port, and match to obtain the target lightly loaded state node for data processing; S3. Obtain the rotary transmission data fluctuation amplitude parameter within a preset time period, analyze to obtain the fluctuation amplitude value of the rotary transmission data, adjust the filtering parameter of the target lightly loaded state node accordingly, and process to obtain the data to be uploaded; S4. Upload the data to be uploaded to the transfer cache station, obtain the data volume of the transfer cache station, and perform an initial compression determination to obtain the initial compression result; S5. Obtain the data time span of the transfer cache station, determine whether to perform correction compression, and process to obtain the first compression execution result; S6. Obtain the 5G bandwidth utilization rate of the transfer cache station, process to obtain the second compression execution result, and after performing the corresponding compression processing, upload the data to be uploaded of the transfer cache station to the cloud again.

[0008] Further, it also includes analyzing based on the change measurement value and the fluctuation amplitude value of the rotary transmission data, and adjusting the data acquisition frequency of the rotary transmission device accordingly. The specific method is as follows: Obtain the preset rate change threshold range and the fluctuation amplitude threshold range in the database; Compare the change measurement value of the rotary transmission data with the rate change threshold range. If the change measurement value of the rotary transmission data is within the rate change threshold range, do not perform data acquisition frequency adjustment. Otherwise, obtain the first sampling frequency adjustment coefficient, and analyze to obtain the first data sampling frequency adjustment result; If the change measurement value of the rotary transmission data is greater than the maximum value of the rate change threshold range, increase the sampling frequency based on the first sampling frequency adjustment coefficient. If the change measurement value of the rotary transmission data is less than the minimum value of the rate change threshold range, decrease the sampling frequency based on the first sampling frequency adjustment coefficient; Compare the fluctuation amplitude value of the rotary transmission data with the fluctuation amplitude threshold range. If the fluctuation amplitude value of the rotary transmission data is within the fluctuation amplitude threshold range, do not perform data acquisition frequency adjustment. Otherwise, obtain the second sampling frequency adjustment coefficient, and analyze to obtain the second data sampling frequency adjustment result; If the fluctuation amplitude value of the rotary transmission data is greater than the maximum value of the fluctuation amplitude threshold range, increase the sampling frequency based on the second sampling frequency adjustment coefficient. If the fluctuation amplitude value of the rotary transmission data is less than the minimum value of the fluctuation amplitude threshold range, decrease the sampling frequency based on the second sampling frequency adjustment coefficient; Analyze based on the first data sampling frequency adjustment result and the second data sampling frequency adjustment result, and adjust the data acquisition frequency of the rotary transmission device accordingly.

[0009] The embodiment of the present application provides a data processing system for a rotary drive device based on 5G and edge computing, including: a light load state node analysis module, a target light load state node matching module, an analysis module for data to be uploaded, a primary compression module, a correction compression judgment module, and a secondary compression execution module; among them, the light load state node analysis module is used to obtain the resource status parameters of each edge node when the detection device receives the rotary drive device data acquisition signal, and thus analyze and obtain the light load state nodes; the target light load state node matching module is used to obtain the rotary drive data rate change parameters within a preset time period, analyze and obtain the resource status demand values of the data acquisition ports, and thus match and obtain the target light load state nodes for data processing; the analysis module for data to be uploaded is used to obtain the rotary drive data fluctuation amplitude parameters within a preset time period, analyze and obtain the fluctuation amplitude values of the rotary drive data, and thus adjust the filtering parameters of the target light load state nodes and process to obtain the data to be uploaded; the primary compression module is used to upload the data to be uploaded to the transfer cache station, obtain the data volume of the transfer cache station, and thus perform a primary compression determination to obtain a primary compression result; the correction compression judgment module is used to obtain the data time span of the transfer cache station, judge whether to perform correction compression, and thus process to obtain a primary compression execution result;

[0010] The secondary compression execution module is used to obtain the 5G bandwidth utilization rate of the transfer cache station, process to obtain a secondary compression execution result, and after performing the corresponding compression processing, upload the data to be uploaded in the transfer cache station to the cloud again.

[0011] One or more technical solutions provided in the embodiment of the present application have at least the following technical effects or advantages:

[0012] 1. The method for processing data of a rotary drive device based on 5G and edge computing provided by the present invention identifies light load state nodes according to resource status parameters at the edge nodes, and matches the actual operation parameters of the rotary drive device, thereby optimizing resource allocation and task scheduling, and then realizing the timely processing and uploading of data, effectively solving the problems of low data processing efficiency and poor timeliness caused by insufficient flexibility of processing resources in the prior art.

[0013] 2. The present invention performs primary compression, correction compression, and secondary compression, thereby dynamically adjusting the compression ratio according to the data volume, data time span, and 5G bandwidth utilization rate of the transfer cache station. While ensuring data quality, it reduces the data transmission volume and storage requirements, optimizes the use efficiency of bandwidth and storage resources, and ensures the high efficiency of data transmission.

[0014] 3. By dynamically adjusting the filtering parameters to adapt to the change in the fluctuation amplitude of the rotary drive data, the data quality is improved, the influence of noise and interference is reduced, and thus the data uploaded to the cloud has higher accuracy and reliability. Brief Description of the Drawings

[0015] Figure 1 It is a flowchart of the data processing method for the rotary drive device based on 5G and edge computing provided by the embodiment of the present application;

[0016] Figure 2 It is a flowchart of the implementation of the data processing method for the rotary drive device based on 5G and edge computing provided by the embodiment of the present application;

[0017] Figure 3 It is a specific flowchart of uploading the data to be uploaded at the transfer cache station to the cloud provided by the embodiment of the present application;

[0018] Figure 4 It is a schematic structural diagram of the data processing system for the rotary drive device based on 5G and edge computing provided by the embodiment of the present application. Detailed Embodiment

[0019] In the embodiment of the present application, by providing a data processing method and system for a rotary drive device based on 5G and edge computing, the problems of low data processing efficiency and poor timeliness caused by insufficient flexibility of processing resources in the prior art are solved, and timely processing and uploading of data are achieved.

[0020] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings of the specification and specific embodiments.

[0021] As Figure 1 shown, it is a flowchart of the data processing method for the rotary drive device based on 5G and edge computing provided by the embodiment of the present application. The method includes the following steps: S1. After the detection device receives the rotary drive device data acquisition signal, obtain the resource status parameters of each edge node, and analyze to obtain the lightly loaded state nodes; S2. Obtain the change parameter of the rotary drive data rate within a preset time period, analyze to obtain the resource status demand value of the data acquisition port, and match to obtain the target lightly loaded state node for data processing; S3. Obtain the fluctuation amplitude parameter of the rotary drive data within a preset time period, analyze to obtain the fluctuation amplitude value of the rotary drive data, adjust the filtering parameter of the target lightly loaded state node accordingly, and process to obtain the data to be uploaded; S4. Upload the data to be uploaded to the transfer cache station, obtain the data volume of the transfer cache station, and perform an initial compression determination to obtain an initial compression result; S5. Obtain the data time span of the transfer cache station, determine whether to perform correction compression, and process to obtain a primary compression execution result; S6. Obtain the 5G bandwidth utilization rate of the transfer cache station, process to obtain a secondary compression execution result, and after performing the corresponding compression processing, upload the data to be uploaded at the transfer cache station to the cloud again.

[0022] In this embodiment, as Figure 2As shown in the figure, it is a flowchart for implementing a data processing method of a rotary transmission device based on 5G and edge computing provided by an embodiment of the present application. From this, it can be analyzed that when a data acquisition signal of the rotary transmission device is received, nodes in the light load state are obtained, and the nodes in the light load state are matched with the resource status demand values of the data acquisition ports to determine the most suitable target light load state node. Then, it is judged whether the fluctuation amplitude value of the rotary transmission data is greater than a preset reference value. If so, the data is transmitted to the cloud, and the filtering parameters of the target light load state node are synchronously adjusted to reduce the filtering cut-off frequency and increase the filtering intensity. If not, the data is uploaded to the transit cache station, and the data to be uploaded at the transit cache station is processed and then uploaded to the cloud.

[0023] As Figure 3 shown in the figure, it is a specific flowchart for uploading the data to be uploaded at the transit cache station to the cloud provided by an embodiment of the present application. From this, it can be analyzed that the specific process of processing the data to be uploaded at the transit cache station and then uploading it to the cloud is as follows: At the transit cache station, it is judged whether the time span of the data checks whether the time span of the data is within a preset threshold range. If so, the initial compression result is used as the first compression execution result. If not, the initial compression result is corrected and compressed according to the time span threshold range to obtain the first compression execution result; it is judged whether the 5G bandwidth utilization rate of the transit cache station checks whether the 5G bandwidth utilization rate is lower than a preset threshold. If so, the first compression execution result is marked as the second compression execution result. If not, the second compression correction process is performed to obtain the second compression execution result. Finally, the processed data is uploaded to the cloud.

[0024] It should be noted that to match the target light load state node and perform data processing, the specific method is as follows: Obtain the available resource value of the light load state node (which can be obtained through the performance monitoring device built into the rotary transmission device), and compare it with the resource status demand value of the data acquisition port. The light load state node with the available resource value greater than the resource status demand value of the data acquisition port and closest to the resource status demand value of the data acquisition port is used as the target light load state node. The data processing specifically includes: The target light load state node first receives the change parameter of the rotary transmission data rate within a preset time period and performs preliminary processing, including denoising processing to ensure data quality. Compare the available resource value of the light load state node with the resource status demand value of the data acquisition port. If the demand is met, resources are reasonably allocated and a data processing task is created and added to the task queue for execution. During the task execution process, continuously monitor the resource usage of the node.

[0025] Further, obtain the resource status parameters of each edge node, and analyze to obtain lightly loaded nodes. The specific method is as follows: Obtain the resource status parameters of each edge node. The resource status parameters include CPU utilization rate and 5G bandwidth utilization rate; obtain the preset CPU utilization rate threshold and 5G bandwidth utilization rate threshold in the database, and compare them with the resource status parameters. If the CPU utilization rate is above the CPU utilization rate threshold or the 5G bandwidth utilization rate is above the 5G bandwidth utilization rate threshold, then the edge node is a heavily loaded node; otherwise, the edge node is a lightly loaded node.

[0026] In this embodiment, the CPU utilization rate is obtained by retrieving through the operating system performance monitoring tool of the edge node. The 5G bandwidth utilization rate can be obtained through the traffic statistics function of the network device.

[0027] By obtaining the resource status parameters of each edge node to analyze and obtain lightly loaded nodes, it is possible to allocate data processing tasks to nodes with sufficient resources, avoiding resource contention and congestion during the data processing process, effectively improving the efficiency of data processing. At the same time, no tasks are allocated to heavily loaded nodes to avoid problems such as system errors caused by resource overload and excessive data volume, improving the reasonable allocation of resources, the utilization efficiency of resources, and the data processing efficiency to a certain extent.

[0028] Further, obtain the change parameter of the rotational drive data rate within a preset time period, and analyze to obtain the resource status demand value of the data acquisition port. The specific method is as follows: Obtain the change parameter of the rotational drive data rate within a preset time period through the data acquisition port. The change parameter of the rotational drive data rate includes the change rate of the transmission ratio, the torque fluctuation frequency, and the change rate of the load inertia; obtain the preset calibration set of the rotational drive data rate change in the database, and conduct comparative analysis with the change rate of the transmission ratio, the torque fluctuation frequency, and the change rate of the load inertia respectively to obtain the comparative analysis result, and introduce the corresponding weighting factor to obtain the change measurement value of the rotational drive data; the calibration set of the rotational drive data rate change includes the calibration value of the change rate of the transmission ratio, the calibration value of the torque fluctuation frequency, and the calibration value of the change rate of the load inertia; obtain the preset value range of each rotational drive data rate change and the corresponding resource status demand reference value of each rotational drive data rate change value range in the database, and compare them with the change measurement value of the rotational drive data. If the change measurement value of the rotational drive data is within a certain preset rotational drive data rate change value range, then obtain the corresponding resource status demand reference value of this range as the resource status demand value of the data acquisition port.

[0029] In this embodiment, the transmission ratio change rate refers to the maximum amplitude of the change in the transmission ratio within a preset time period (specifically, the change rate between the maximum value and the minimum value of the transmission ratio), and the acquisition method is as follows: By using the rotational speed sensors and encoders installed on the rotational transmission device, the rotational speeds of the driving wheel and the driven wheel are monitored in real time to obtain the transmission ratio. The torque fluctuation frequency is the change frequency during the torque fluctuation process, and the torque fluctuation frequency represents the highest frequency of the torque fluctuation, which can be obtained by using a torque sensor to monitor the torque output of the rotational transmission device in real time. The load inertia change rate is the change rate of the load inertia over time, and the load inertia change rate is the maximum amplitude of the change in the load inertia per unit time within a preset time period. The acquisition method is as follows: It can be detected by an inertial measurement unit built into the rotational transmission device.

[0030] Obtain the change measurement value of the rotational transmission data. The specific method is as follows:

[0031] ;

[0032] In the formula, represents the change measurement value of the rotational transmission data, represents the transmission ratio change rate, represents the calibration value of the transmission ratio change rate, represents the torque fluctuation frequency, represents the calibration value of the torque fluctuation frequency, represents the load inertia change rate, represents the calibration value of the load inertia change rate, represents the weighting factor of the transmission ratio change rate, represents the weighting factor of the torque fluctuation frequency, represents the weighting factor of the load inertia change rate.

[0033] The weighting factor of the transmission ratio change rate, the weighting factor of the torque fluctuation frequency, and the weighting factor of the load inertia change rate can be obtained from the database. For example: The weighting factor of the transmission ratio change rate can be obtained by acquiring the historical transmission ratio change rate stored in the database and the corresponding weighting factor of the transmission ratio change rate, thereby constructing a transmission ratio change rate mapping set. There is a one-to-one or many-to-one correspondence relationship in this mapping set. By inputting the transmission ratio change rate data to be used into the transmission ratio change rate mapping set, the weighting factor of the transmission ratio change rate can be obtained. The acquisition methods of the other weighting factors are the same as that of the weighting factor of the transmission ratio change rate, and they can all be matched and obtained in the corresponding mapping sets. Among them, the weighting factor of the torque fluctuation frequency corresponds to the torque fluctuation frequency mapping set, and the weighting factor of the load inertia change rate corresponds to the load inertia change rate mapping set.

[0034] By analyzing the parameters of the rotational drive data rate change (transmission ratio change rate, torque fluctuation frequency, and load inertia change rate), the mutual influence relationships among these parameters are taken into account. For example, the transmission ratio change rate affects torque transmission and the equivalent change of load inertia. An excessive transmission ratio change rate will cause an increase in the torque fluctuation frequency and an increase in the load inertia change rate. The torque fluctuation frequency reflects the impact of the rotational drive system being subjected to shock or imbalance, which will cause an increase in the transmission ratio change rate and exacerbate the instability of the load inertia. The load inertia change rate affects the inertial characteristics and stability of the drive system. A rapidly changing load inertia will cause an increase in the transmission ratio change rate and an increase in the torque fluctuation frequency.

[0035] By obtaining the parameters of the rotational drive data rate change within a preset time period, it can be ensured that data processing tasks are assigned to edge nodes with appropriate resource states, improving resource utilization efficiency, avoiding processing delays or failures caused by unreasonable resource allocation, and optimizing task scheduling efficiency. By precisely analyzing the operating state of the rotational drive device and quickly determining the resource state requirements of the corresponding edge nodes, it is possible to quickly respond to the data processing requirements of the rotational drive device under different operating conditions. Selecting an edge node with an appropriate resource state based on the parameters of the rotational drive data rate change within a preset time period takes into account that the data fluctuations at some collection points are large or the data volume is large. Therefore, at this time, an edge node with good data processing capabilities needs to be selected for processing, which can avoid the phenomenon of edge node resource shortage and edge node collapse caused by excessive data volume.

[0036] In this step, the focus is on the parameters of the rotational drive data rate change. Such parameters are mainly used to quantify the change rates of various data during the operation of the rotational drive device, such as the dynamic change conditions of parameters such as the transmission ratio, torque, and load inertia. These parameters can reflect the changes in the resource requirements for data processing by the rotational drive device under different operating conditions, and help to reasonably allocate and schedule the resources of edge computing nodes.

[0037] Further, obtain the fluctuation amplitude parameter of the rotational drive data within a preset time period, and analyze to obtain the fluctuation amplitude value of the rotational drive data. The specific method is as follows: Obtain the fluctuation amplitude parameter of the rotational drive data within a preset time period through a data acquisition port. The fluctuation amplitude parameter of the rotational drive data includes the torque mutation frequency, vibration energy entropy, maximum angular displacement deviation, maximum torque fluctuation amplitude, maximum phase deviation, and rotational speed fluctuation frequency. Obtain the preset fair set of fluctuation amplitudes in the database, and compare and analyze them with the torque mutation frequency, vibration energy entropy, angular displacement deviation, torque fluctuation amplitude, phase deviation, and rotational speed fluctuation frequency respectively to obtain the comparative analysis result, and introduce the corresponding weighting factor to quantify the comparative analysis result to obtain the fluctuation amplitude value of the rotational drive data. The fair set of fluctuation amplitudes includes the fair value of the torque mutation frequency, the fair value of the vibration energy entropy, the fair value of the angular displacement deviation, the fair value of the torque fluctuation amplitude, the fair value of the phase deviation, and the fair value of the rotational speed fluctuation frequency.

[0038] In this embodiment, the torque mutation frequency refers to the highest value of the mutation frequency in the torque signal within a preset time period, which reflects the most extreme frequency of torque change when the rotational drive device is affected by impact loads or sudden changes in working conditions during operation. It can be obtained by extracting the characteristics of the torque signal collected by the torque sensor.

[0039] The vibration energy entropy represents the degree of concentration of the energy distribution of the vibration signal. The larger the value, the less concentrated it is. Its acquisition method is obtained through molecular dynamics simulation software (such as CPMD). The maximum torque fluctuation amplitude refers to the maximum change amplitude (the difference between the maximum torque and the minimum torque) during the torque fluctuation within a preset time period, which reflects the stability of the torque output of the rotational drive device during operation. Its acquisition method is as follows: Obtain it by monitoring the torque output of the rotational drive device in real time through a torque sensor. The maximum phase deviation refers to the maximum value of the phase difference between the axes in a rotational drive system with multi-axis collaborative work within a preset time period. Its acquisition method is obtained by synchronously collecting the phase information of each axis through a high-precision phase sensor. The rotational speed fluctuation frequency refers to the highest change frequency during the rotational speed fluctuation within a preset time period, which reflects the severity of the rotational speed change of the rotational drive device during operation. It can be obtained by collecting the rotational speed signal of the rotational drive device in real time through a rotational speed sensor.

[0040] The method for obtaining the fluctuation amplitude value of the rotational drive data is as follows:

[0041] ;

[0042] In the formula, represents the fluctuation amplitude value of the rotational drive data, represents the torque mutation frequency, represents the fair value of the torque mutation frequency, represents the vibration energy entropy, Indicates the fair value of vibration energy entropy Indicates the maximum value of angular displacement deviation Indicates the fair value of angular displacement deviation Indicates the maximum value of torque fluctuation amplitude Indicates the fair value of torque fluctuation amplitude Indicates the maximum value of phase deviation Indicates the fair value of phase deviation Indicates the rotational speed fluctuation frequency Indicates the fair value of rotational speed fluctuation frequency Indicates the weighted factor of torque mutation frequency Indicates the weighted factor of vibration energy entropy Indicates the weighted factor of angular displacement deviation Indicates the weighted factor of torque fluctuation amplitude Indicates the weighted factor of phase deviation Indicates the weighted factor of rotational speed fluctuation frequency

[0043] By analyzing the fluctuation amplitude parameters of rotational drive data (including torque mutation frequency, vibration energy entropy, maximum value of angular displacement deviation, maximum value of torque fluctuation amplitude, maximum value of phase deviation, and rotational speed fluctuation frequency), considering the mutual influence relationships among these parameters, an increase in torque mutation frequency is usually accompanied by an increase in torque fluctuation amplitude, leading to increased vibration of the rotational drive system, thereby increasing the vibration energy entropy. At the same time, torque mutation and fluctuation will affect the smoothness of rotation, resulting in an increase in rotational speed fluctuation frequency. Torque and rotational speed fluctuations will cause angular displacement deviation and phase deviation. The larger the angular displacement deviation and phase deviation, the greater the deviation of the rotational drive system in terms of positioning accuracy and multi-axis synchronization, which will further exacerbate the instability of the rotational drive system. For example, frequent torque mutation will lead to an increase in angular displacement deviation, and the increase in angular displacement deviation will in turn affect the phase synchronization of the multi-axis system, reducing the overall performance.

[0044] The torque mutation frequency weighting factor, vibration energy entropy weighting factor, angular displacement deviation weighting factor, torque fluctuation amplitude weighting factor, phase deviation weighting factor, and rotational speed fluctuation frequency weighting factor can be obtained from a database. For example, the torque mutation frequency weighting factor can be obtained by acquiring the historical torque mutation frequency stored in the database and the corresponding torque mutation frequency weighting factor, thereby constructing a torque mutation frequency mapping set. There is a one-to-one or many-to-one correspondence in this mapping set. By inputting the torque mutation frequency data to be used into the torque mutation frequency mapping set, the torque mutation frequency weighting factor can be obtained. The acquisition methods of other weighting factors are the same as that of the torque mutation frequency weighting factor and can be obtained by matching in the corresponding mapping sets. Among them, the vibration energy entropy weighting factor corresponds to the vibration energy entropy mapping set, the angular displacement deviation weighting factor corresponds to the angular displacement deviation mapping set, the torque fluctuation amplitude weighting factor corresponds to the torque fluctuation amplitude mapping set, the phase deviation weighting factor corresponds to the phase deviation mapping set, and the rotational speed fluctuation frequency weighting factor corresponds to the rotational speed fluctuation frequency mapping set.

[0045] By analyzing the fluctuation amplitude parameters of the rotational transmission data, it reflects the dynamic characteristics and stability of the device from different aspects, which helps to detect potential problems in a timely manner. Based on the analysis of the fluctuation amplitude values, the system can adjust the control strategy in real time, optimize the operation of the rotational transmission device, and improve production efficiency and product quality.

[0046] This step focuses on the fluctuation amplitude parameters of the rotational transmission data. These parameters are used to describe the fluctuation amplitude of various data during the operation of the rotational transmission device, such as the amplitude of parameters like torque mutation, vibration energy, angular displacement deviation, torque fluctuation, phase deviation, and rotational speed fluctuation, and it focuses more on reflecting the operation stability and reliability of the rotational transmission device.

[0047] By evaluating the rotational transmission device from two dimensions of the change rate and the fluctuation amplitude, a more comprehensive understanding of the operation characteristics and requirements of the device can be obtained. The rate change parameters help to optimize resource allocation and task scheduling, while the fluctuation amplitude parameters focus on improving system stability and reliability.

[0048] By determining the change measurement value of the rotational drive data according to the rotational drive data rate change parameter, the reference value of the required state of the processing node resources is accurately matched. This process ensures that the data processing tasks are reasonably allocated to the edge nodes with corresponding resources, prevents processing delays or failures caused by unreasonable resource allocation, optimizes the task scheduling efficiency, and guarantees the timeliness and accuracy of data processing. By analyzing the rotational drive data fluctuation amplitude parameter, the fluctuation amplitude value of the rotational drive data is obtained, and the data allocated to the corresponding node is adjusted by targeted filtering according to this value, ensuring that the data uploaded to the cloud has high accuracy and reliability, and also enabling the target lightly loaded state node to better adapt to the changes in the current rotational drive device data for timely adjustment.

[0049] Furthermore, the filtering parameters of the target lightly loaded state node are adjusted, and the data to be uploaded is processed. The specific method is as follows: Obtain the current filtering parameters of the target lightly loaded state node, where the filtering parameters of the target lightly loaded state node include the filtering cut-off frequency and filtering intensity of the target lightly loaded state node; obtain the preset rotational drive data fluctuation amplitude reference value in the database, and compare it with the fluctuation amplitude value of the rotational drive data. If the fluctuation amplitude value of the rotational drive data is greater than the rotational drive data fluctuation amplitude reference value, directly transmit the data of this target lightly loaded state node to the cloud, and synchronously adjust the filtering parameters of the lightly loaded state node. If the fluctuation amplitude value of the rotational drive data is below the rotational drive data fluctuation amplitude reference value, mark the data of this target lightly loaded state node as data to be uploaded; if the filtering parameters of the target lightly loaded state node are synchronously adjusted, then based on the fluctuation amplitude value of the rotational drive data and the rotational drive data fluctuation amplitude reference value, a proportional analysis is performed to obtain the filtering adjustment coefficient of the target lightly loaded state node; based on the filtering adjustment coefficient of the target lightly loaded state node, reduce the filtering cut-off frequency of the target lightly loaded state node and increase the filtering intensity.

[0050] In this embodiment, based on the fluctuation amplitude value of the rotational drive data and the rotational drive data fluctuation amplitude reference value, a proportional analysis is performed to obtain the filtering adjustment coefficient of the target lightly loaded state node. The specific method is as follows: Perform a difference process on the fluctuation amplitude value of the rotational drive data and the rotational drive data fluctuation amplitude reference value to obtain a fluctuation amplitude difference, and then divide the fluctuation amplitude difference by the rotational drive data fluctuation amplitude reference value to obtain the filtering adjustment coefficient of the target lightly loaded state node.

[0051] Based on the filtering adjustment coefficient of the target light load state node, reduce the filtering cut-off frequency of the target light load state node and increase the filtering intensity. The specific steps are as follows: Obtain the preset filtering adjustment coefficient intervals of each target light load state node in the database, as well as the corresponding reference filtering cut-off frequency adjustment coefficient and reference filtering intensity adjustment coefficient for each target light load state node filtering adjustment coefficient interval, and compare them with the filtering adjustment coefficient of the target light load state node. If the filtering adjustment coefficient of the target light load state node is within a certain target light load state node filtering adjustment coefficient interval, obtain the corresponding reference filtering cut-off frequency adjustment coefficient and reference filtering intensity adjustment coefficient of this interval as the filtering cut-off frequency adjustment coefficient and filtering intensity adjustment coefficient.

[0052] Obtain the filtering cut-off frequency of the initial target light load state node, and reduce the filtering cut-off frequency of the target light load state node based on the filtering cut-off frequency adjustment coefficient. The specific method is ; where represents the filtering cut-off frequency of the adjusted target light load state node, represents the filtering cut-off frequency of the initial target light load state node, represents the filtering cut-off frequency adjustment coefficient. Obtain the filtering intensity of the initial target light load state node and increase the filtering intensity of the target light load state node based on the filtering intensity adjustment coefficient. The specific method is ; where represents the filtering intensity of the adjusted target light load state node, represents the filtering intensity of the initial target light load state node, represents the filtering intensity adjustment coefficient.

[0053] By dynamically adjusting the filtering cut-off frequency and intensity of the target light load state node, it is possible to adaptively optimize the data processing process according to the change in the fluctuation amplitude of the rotational transmission data, improve the accuracy and reliability of data processing, and enable the target light load state node to better adapt to the output data of the current rotational transmission device. When the fluctuation amplitude exceeds the reference value, directly transmit the data to the cloud and synchronously adjust the filtering parameters during the transmission process, which can ensure the timely upload and processing of important data and avoid data loss. Decide the data processing method based on the comparison result of the fluctuation amplitude, rationally utilize the resources of the cloud and edge nodes, avoid unnecessary data upload, improve the overall efficiency of the system, and precisely control the filtering parameters by introducing the filtering adjustment coefficient, effectively reducing noise and interference and improving the quality of the uploaded data.

[0054] Further, perform initial compression determination to obtain an initial compression result. The specific method is as follows: Upload the data to be uploaded to the transfer cache station and obtain the data volume of the transfer cache station; Obtain the preset cache data volume threshold in the database and compare it with the data volume of the transfer cache station. If the data volume of the transfer cache station is above the cache data volume threshold, it is determined that data initial compression is to be performed to obtain an initial compression result; If the data volume of the transfer cache station is less than the cache data volume threshold, initial compression is not executed, and uploading to the cloud is not executed.

[0055] In this embodiment, it should be noted that the preset cache data volume threshold in the database is obtained and compared with the data volume of the transfer cache station. If the data volume of the transfer cache station is above the cache data volume threshold, it is determined that data initial compression is to be performed to obtain an initial compression result, where the initial compression result is the compression ratio of the initial compression. It should be understood that the larger the compression ratio, the smaller the size of the compressed file relative to the original file, and thus the smaller the memory (or storage space) occupancy. For example, a file with a compression ratio of 0.1 (10%) occupies less space than a file with a compression ratio of 0.5 (50%).

[0056] Among them, the purpose of the initial compression result is to reduce storage requirements and save space. Performing initial compression to a certain extent can ensure that more data can be uploaded simultaneously, reduce the amount of data uploaded to the cloud, and save bandwidth. The initial compression ratio can be mapped through the preset cache data volume threshold in the database. The specific method is as follows: Analyze based on the cache data volume threshold, obtain the preset cache data volume intervals in the database and the corresponding reference initial compression ratios for each cache data volume interval, and compare them with the cache data volume threshold. If the cache data volume threshold is within a certain preset cache data volume interval, obtain the reference initial compression ratio corresponding to this cache data volume interval as the initial compression ratio.

[0057] By temporarily storing data in the transfer cache station and judging whether to perform initial compression based on the data volume, the data traffic uploaded to the cloud is effectively controlled. When the data volume exceeds the preset threshold, data initial compression is triggered, optimizing the bandwidth usage efficiency. For the case of a small data volume, frequent compression and upload operations are avoided, reducing system overhead; For the case of a large data volume, compression is used to ensure that the data can be efficiently transmitted and stored. By monitoring and compressing the data volume of the transfer cache station, the total amount of data uploaded to the cloud is reduced, the burden on cloud storage is reduced, and the utilization rate of storage resources is improved. By performing data volume judgment and initial compression in the transfer cache station, the data processing delay is reduced, the overall response speed of the system is improved, the timeliness and effectiveness of the data are ensured, the bandwidth load is avoided, and the efficiency and stability of data upload are guaranteed.

[0058] Further, to obtain the first compression execution result, the specific method is as follows: Obtain the data time span of the transfer cache station; obtain the preset time span threshold interval in the database and compare it with the data time span of the transfer cache station. If the data time span of the transfer cache station is greater than the upper limit value of the time span threshold interval, then correct and compress the initial compression result according to the upper limit value of the time span threshold interval to obtain the first compression execution result; if the data time span of the transfer cache station is less than the lower limit value of the time span threshold interval, then correct and compress the initial compression result according to the lower limit value of the time span threshold interval to obtain the first compression execution result; otherwise, do not perform correction compression and use the initial compression result as the first compression execution result.

[0059] In this embodiment, to correct and compress the initial compression result according to the upper limit value of the time span threshold interval, the specific method is as follows: Obtain the value corresponding to the upper limit value of the time span threshold interval in the transfer cache station, mark it as the maximum time length, and match it with the database to obtain the first correction compression ratio coefficient, and then perform correction compression to obtain the first compression execution result. To obtain the first correction compression ratio coefficient, the specific method is as follows: Obtain the preset time length intervals in the database and the corresponding reference correction compression ratio coefficients for each time length interval, and compare them with the maximum time length. If the maximum time length is within a certain time length interval, then obtain the reference correction compression ratio coefficient corresponding to that time length interval as the first correction compression ratio coefficient. Based on the first correction compression ratio coefficient, reduce the compression ratio of the initial compression ratio to obtain the first compression execution ratio, and thus obtain the first compression execution result. The specific method is as follows: ; where represents the first compression execution ratio, represents the initial compression ratio, represents the first correction compression ratio coefficient.

[0060] To correct and compress the initial compression result according to the lower limit value of the time span threshold interval, the specific method is as follows: Obtain the value corresponding to the lower limit value of the time span threshold interval in the transfer cache station, mark it as the minimum time length, and match it with the database to obtain the correction compression ratio coefficient, and then perform correction compression to obtain the first compression execution result. To obtain the correction compression ratio coefficient, the specific method is as follows: Obtain the preset time length intervals in the database and the corresponding reference correction compression ratio coefficients for each time length interval, and compare them with the minimum time length. If the minimum time length is within a certain time length interval, then obtain the reference correction compression ratio coefficient corresponding to that time length interval as the second correction compression ratio coefficient. Based on the second correction compression ratio coefficient, reduce the compression ratio of the initial compression ratio to obtain the first compression execution ratio, and thus obtain the first compression execution result. The specific method is as follows: ; where Indicates the primary compression execution ratio, Indicates the initial compression ratio, Indicates the second correction compression ratio coefficient.

[0061] By comparing the data time span of the transfer cache station with the preset time span threshold interval, it is possible to accurately determine whether to perform correction compression based on the time characteristics of the data, thereby more precisely controlling the degree of data compression, avoiding unnecessary compression or over-compression, and ensuring the integrity and availability of the data. If the data time span of the transfer cache station is within the time span threshold interval, no correction compression is performed, indicating that the compression effect is good at this time and no adjustment is needed. If the data time span of the transfer cache station is above the upper limit value of the time span threshold interval, the initial compression result is corrected and compressed according to the upper limit value of the time span threshold interval, indicating that the data time span of the transfer cache station is too large and the data is scattered at this time, so there will be a large data difference, and over-compression will cause too many losses, so correction compression is needed. If the data time span of the transfer cache station is less than the lower limit value of the time span threshold interval, the initial compression result is corrected and compressed according to the lower limit value of the time span threshold interval. At this time, it means that the data volume has reached the upload standard under this data time span, but the data time span is small at this time, so the data is concentrated and the data difference is small at this time, so it is necessary to increase the data compression ratio to ensure that more data can be uploaded in one upload.

[0062] Furthermore, obtain the 5G bandwidth utilization rate of the transfer cache station and process to obtain the secondary compression execution result. The specific method is as follows: Obtain the real-time 5G bandwidth utilization rate of the transfer cache station; based on the 5G bandwidth utilization rate threshold and compare it with the real-time 5G bandwidth utilization rate of the transfer cache station. If the real-time 5G bandwidth utilization rate is below the 5G bandwidth utilization rate threshold, no secondary compression correction processing is performed, and the primary compression execution result is marked as the secondary compression execution result; if the real-time 5G bandwidth utilization rate is greater than the 5G bandwidth utilization rate threshold, secondary compression correction processing is performed, and based on the analysis of the real-time 5G bandwidth utilization rate and the 5G bandwidth utilization rate threshold, a bandwidth difference coefficient is obtained; obtain each preset bandwidth difference coefficient interval in the database and the corresponding secondary compression reference adjustment ratio for each bandwidth difference coefficient interval, and compare it with the bandwidth difference coefficient. If the bandwidth difference coefficient is within a certain preset bandwidth difference coefficient interval, obtain the corresponding secondary compression reference adjustment ratio of this interval as the secondary compression adjustment ratio; based on the secondary compression reference adjustment ratio and perform ratio adjustment with the primary compression execution result to obtain the secondary compression execution result.

[0063] In this embodiment, it should be noted that based on the analysis of the real-time 5G bandwidth utilization rate and the 5G bandwidth utilization rate threshold, the bandwidth difference coefficient is obtained. The specific method is as follows: The difference between the real-time 5G bandwidth utilization rate and the 5G bandwidth utilization rate threshold is processed to obtain the 5G bandwidth utilization rate difference, and the 5G bandwidth utilization rate is divided by the 5G bandwidth utilization rate threshold to obtain the bandwidth difference coefficient.

[0064] Based on the secondary compression reference adjustment ratio and the ratio adjustment with the primary compression execution result, the secondary compression execution result is obtained. The specific method is as follows: Obtain the compression ratio of the primary compression execution result and multiply it by the secondary compression reference adjustment ratio to obtain the secondary compression execution ratio, which is denoted as the secondary compression execution result.

[0065] By monitoring the 5G bandwidth utilization rate of the transit cache station in real time, it is dynamically determined whether to perform secondary compression correction processing. When the bandwidth resources are tight, secondary compression is performed to reduce the data volume, thereby optimizing the data transmission efficiency and ensuring that the data can be uploaded to the cloud in a timely and efficient manner.

[0066] Based on the comparison between the 5G bandwidth utilization rate threshold and the real-time utilization rate, the data compression strategy can be flexibly adjusted. When the bandwidth is sufficient, unnecessary compression is avoided to retain more data details; when the bandwidth is limited, effective compression is performed to make reasonable use of the limited bandwidth resources. When the bandwidth permits, avoiding excessive compression of data can ensure the authenticity of the data. By pre-compressing the data at the edge according to the bandwidth situation, the amount of data uploaded to the cloud is reduced, and the storage and processing burden on the cloud is reduced.

[0067] Further, it also includes analyzing the change measurement value and the fluctuation amplitude value of the rotational drive data, and accordingly adjusting the data acquisition frequency of the rotational drive device. The specific method is as follows: Obtain the preset rate change threshold range and the fluctuation amplitude threshold range in the database; compare the change measurement value of the rotational drive data with the rate change threshold range. If the change measurement value of the rotational drive data is within the rate change threshold range, do not perform the adjustment of the data acquisition frequency. Otherwise, obtain the first sampling frequency adjustment coefficient, and accordingly analyze and obtain the first adjustment result of the data sampling frequency. If the change measurement value of the rotational drive data is greater than the maximum value of the rate change threshold range, increase the sampling frequency based on the first sampling frequency adjustment coefficient. If the change measurement value of the rotational drive data is less than the minimum value of the rate change threshold range, decrease the sampling frequency based on the first sampling frequency adjustment coefficient; compare the fluctuation amplitude value of the rotational drive data with the fluctuation amplitude threshold range. If the fluctuation amplitude value of the rotational drive data is within the fluctuation amplitude threshold range, do not perform the adjustment of the data acquisition frequency. Otherwise, obtain the second sampling frequency adjustment coefficient, and accordingly analyze and obtain the second adjustment result of the data sampling frequency. If the fluctuation amplitude value of the rotational drive data is greater than the maximum value of the fluctuation amplitude threshold range, increase the sampling frequency based on the second sampling frequency adjustment coefficient. If the fluctuation amplitude value of the rotational drive data is less than the minimum value of the fluctuation amplitude threshold range, decrease the sampling frequency based on the second sampling frequency adjustment coefficient; analyze based on the first adjustment result of the data sampling frequency and the second adjustment result of the data sampling frequency, and accordingly adjust the data acquisition frequency of the rotational drive device.

[0068] In this embodiment, based on the analysis of the first adjustment result of the data sampling frequency and the second adjustment result of the data sampling frequency, the data acquisition frequency of the rotational drive device is adjusted. The specific steps are as follows: Obtain the data acquisition frequency of the rotational drive device, and make a judgment based on the analysis of the first adjustment result of the data sampling frequency and the second adjustment result of the data sampling frequency. If the first adjustment result of the data sampling frequency is to increase the sampling frequency and the second adjustment result of the data sampling frequency is to increase the sampling frequency, then analyze and obtain the data acquisition frequency of the adjusted rotational drive device. ; In the formula, represents the data acquisition frequency of the adjusted rotational drive device, represents the data acquisition frequency of the initial rotational drive device, represents the first sampling frequency adjustment coefficient, represents the second sampling frequency adjustment coefficient. If the first adjustment result of the data sampling frequency is to increase the sampling frequency and the second adjustment result of the data sampling frequency is to decrease the sampling frequency, then analyze and obtain the data acquisition frequency of the adjusted rotational drive device. ; In the formula, represents the data acquisition frequency of the adjusted rotational drive device, Indicates the data acquisition frequency of the initial rotary transmission device, Indicates the first adjustment coefficient of the sampling frequency, Indicates the second adjustment coefficient of the sampling frequency. If the first adjustment result of the data sampling frequency is to reduce the sampling frequency and the second adjustment result of the data sampling frequency is to increase the sampling frequency, then analyze to obtain the data acquisition frequency of the adjusted rotary transmission device, ; where, Indicates the data acquisition frequency of the adjusted rotary transmission device, Indicates the data acquisition frequency of the initial rotary transmission device, Indicates the first adjustment coefficient of the sampling frequency, Indicates the second adjustment coefficient of the sampling frequency. If the first adjustment result of the data sampling frequency is to reduce the sampling frequency and the second adjustment result of the data sampling frequency is to reduce the sampling frequency, then analyze to obtain the data acquisition frequency of the adjusted rotary transmission device. ; where, Indicates the data acquisition frequency of the adjusted rotary transmission device, Indicates the data acquisition frequency of the initial rotary transmission device, Indicates the first adjustment coefficient of the sampling frequency, Indicates the second adjustment coefficient of the sampling frequency.

[0069] Such as Figure 4As shown in the figure, it is a schematic structural diagram of a data processing system for a rotary drive device based on 5G and edge computing provided by an embodiment of the present application. The data processing system for a rotary drive device based on 5G and edge computing provided by an embodiment of the present application includes: a light-load state node analysis module, a target light-load state node matching module, an analysis module for data to be uploaded, a primary compression module, a correction compression judgment module, and a secondary compression execution module; among them, the light-load state node analysis module is used to obtain the resource status parameters of each edge node when the detection device receives the rotary drive device data acquisition signal, and thus analyze and obtain the light-load state nodes; the target light-load state node matching module is used to obtain the change parameter of the rotary drive data rate within a preset time period, analyze and obtain the resource status demand value of the data acquisition port, and thus match and obtain the target light-load state nodes for data processing; the analysis module for data to be uploaded is used to obtain the fluctuation amplitude parameter of the rotary drive data within a preset time period, analyze and obtain the fluctuation amplitude value of the rotary drive data, and thus adjust the filtering parameters of the target light-load state nodes and process to obtain the data to be uploaded; the primary compression module is used to upload the data to be uploaded to the transfer cache station, obtain the data volume of the transfer cache station, and thus perform a primary compression determination to obtain a primary compression result; the correction compression judgment module is used to obtain the data time span of the transfer cache station, judge whether to perform correction compression, and thus process to obtain a primary compression execution result; the secondary compression execution module is used to obtain the 5G bandwidth utilization rate of the transfer cache station, process to obtain a secondary compression execution result, and after performing the corresponding compression processing, upload the data to be uploaded of the transfer cache station to the cloud again.

[0070] In summary, in this embodiment, by identifying light-load state nodes according to resource status parameters at the edge nodes and matching the actual operation parameters of the rotary drive device, the resource allocation and task scheduling are optimized, and thus the timely processing and uploading of data are realized, effectively solving the problems of low data processing efficiency and poor timeliness caused by insufficient flexibility of processing resources in the prior art.

[0071] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0072] The present invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each flow and / or block in the flowchart illustrations and / or block diagrams, and combinations of flows and / or blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions executed by the processor of the computer or other programmable data processing apparatus create means for implementing the functions specified in the flowchart flow or flows and / or block or blocks. Figure 1 in a flow or flows and / or block or blocks Figure 1 or blocks.

[0073] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means that implement the functions specified in the flowchart flow or flows and / or block or blocks. Figure 1 in a flow or flows and / or block or blocks Figure 1 or blocks.

[0074] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and / or block or blocks. Figure 1 in a flow or flows and / or block or blocks Figure 1 or blocks.

[0075] Although the preferred embodiments of the present invention have been described, additional changes and modifications can be made by those skilled in the art once they learn of the basic inventive concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0076] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A data processing method for a rotary transmission device based on 5G and edge computing, characterized in that It includes the following steps: S1. After the detection device receives the data acquisition signal of the rotary drive device, obtain the resource status parameters of each edge node, and analyze to obtain the lightly loaded state nodes therefrom; S2. Obtain the change parameter of the rotary drive data rate within a preset time period, analyze to obtain the resource status demand value of the data acquisition port, and match to obtain the target lightly loaded state node for data processing therefrom; S3. Obtain the fluctuation amplitude parameter of the rotary drive data within a preset time period, analyze to obtain the fluctuation amplitude value of the rotary drive data, adjust the filtering parameter of the target lightly loaded state node therefrom, and process to obtain the data to be uploaded; S4. Upload the data to be uploaded to the transfer cache station, obtain the data volume of the transfer cache station, and perform preliminary compression determination therefrom to obtain the preliminary compression result; S5. Obtain the data time span of the transfer cache station, determine whether to perform correction compression, and process to obtain the first compression execution result therefrom; S6. Obtain the 5G bandwidth utilization rate of the transfer cache station, process to obtain the second compression execution result, and after performing the corresponding compression process, upload the data to be uploaded of the transfer cache station to the cloud again.

2. The data processing method of the rotary drive device based on 5G and edge computing according to claim 1, characterized in that: The method for obtaining the resource status parameters of each edge node and analyzing to obtain the lightly loaded state nodes therefrom is specifically as follows: Obtain the resource status parameters of each edge node, where the resource status parameters include CPU utilization rate and 5G bandwidth utilization rate; Obtain the preset CPU utilization rate threshold and 5G bandwidth utilization rate threshold in the database, and compare them with the resource status parameters. If the CPU utilization rate is above the CPU utilization rate threshold or the 5G bandwidth utilization rate is above the 5G bandwidth utilization rate threshold, then the edge node is a heavily loaded state node, otherwise the edge node is a lightly loaded state node.

3. The data processing method of the rotary drive device based on 5G and edge computing according to claim 1, wherein: The method for obtaining the change parameter of the rotary drive data rate within a preset time period and analyzing to obtain the resource status demand value of the data acquisition port is specifically as follows: Obtain the change parameter of the rotary drive data rate within a preset time period through the data acquisition port, where the change parameter of the rotary drive data rate includes the transmission ratio change rate, torque fluctuation frequency, and load inertia change rate; Obtain the preset calibration set of the rotary drive data rate change in the database, and perform comparative analysis with the transmission ratio change rate, torque fluctuation frequency, and load inertia change rate respectively to obtain the comparative analysis result, and introduce the corresponding weighting factor to obtain the change measurement value of the rotary drive data; The calibration set of the rotary drive data rate change includes the transmission ratio change rate calibration value, torque fluctuation frequency calibration value, and load inertia change rate calibration value; Obtain the preset value range of each rotary drive data rate change and the corresponding resource status demand reference value of each rotary drive data rate change value range in the database, and compare them with the change measurement value of the rotary drive data. If the change measurement value of the rotary drive data is within a certain preset rotary drive data rate change value range, then obtain the corresponding resource status demand reference value of this range as the resource status demand value of the data acquisition port.

4. The data processing method of the rotary drive device based on 5G and edge computing according to claim 1, characterized in that: The method for obtaining the fluctuation amplitude parameter of the rotary drive data within a preset time period and analyzing to obtain the fluctuation amplitude value of the rotary drive data is specifically as follows: Obtain the amplitude fluctuation parameters of the rotational drive data within a preset time period through the data acquisition port. The amplitude fluctuation parameters of the rotational drive data include torque mutation frequency, vibration energy entropy, maximum angular displacement deviation, maximum torque fluctuation amplitude, maximum phase deviation, and rotational speed fluctuation frequency; Obtain the preset amplitude fairness set in the database, and conduct comparative analysis with the torque mutation frequency, vibration energy entropy, angular displacement deviation, torque fluctuation amplitude, phase deviation, and rotational speed fluctuation frequency respectively to obtain the comparative analysis result, and introduce the corresponding weighting factor to quantify the comparative analysis result to obtain the amplitude value of the rotational drive data; The amplitude fairness set includes torque mutation frequency fairness value, vibration energy entropy fairness value, angular displacement deviation fairness value, torque fluctuation amplitude fairness value, phase deviation fairness value, and rotational speed fluctuation frequency fairness value.

5. The data processing method of the rotary drive device based on 5G and edge computing according to claim 1, characterized in that: Adjust the filtering parameters of the target light load state node and process to obtain the data to be uploaded. The specific method is as follows: Obtain the current filtering parameters of the target light load state node. The filtering parameters of the target light load state node include the filtering cut-off frequency and filtering intensity of the target light load state node; Obtain the preset amplitude reference value of the rotational drive data in the database and compare it with the amplitude value of the rotational drive data. If the amplitude value of the rotational drive data is greater than the amplitude reference value of the rotational drive data, directly transmit the data of this target light load state node to the cloud and synchronously adjust the filtering parameters of the light load state node. If the amplitude value of the rotational drive data is below the amplitude reference value of the rotational drive data, mark the data of this target light load state node as the data to be uploaded; If synchronously adjusting the filtering parameters of the target light load state node, conduct proportional analysis based on the amplitude value of the rotational drive data and the amplitude reference value of the rotational drive data simultaneously to obtain the filtering adjustment coefficient of the target light load state node; Reduce the filtering cut-off frequency and increase the filtering intensity of the target light load state node based on the filtering adjustment coefficient of the target light load state node.

6. The data processing method of the rotary drive device based on 5G and edge computing according to claim 1, characterized in that: Conduct primary compression determination to obtain the primary compression result. The specific method is as follows: Upload the data to be uploaded to the transfer cache station and obtain the data volume of the transfer cache station; Obtain the preset cache data volume threshold in the database and compare it with the data volume of the transfer cache station. If the data volume of the transfer cache station is above the cache data volume threshold, determine to conduct data primary compression to obtain the primary compression result; If the data volume of the transfer cache station is less than the cache data volume threshold, do not perform primary compression and do not perform upload to the cloud.

7. The data processing method of the rotary drive device based on 5G and edge computing according to claim 1, characterized in that: Process to obtain the first compression execution result. The specific method is as follows: Obtain the data time span of the transfer cache station; Obtain the preset time span threshold interval in the database and compare it with the data time span of the transfer cache station. If the data time span of the transfer cache station is greater than the upper limit value of the time span threshold interval, correct and compress the primary compression result according to the upper limit value of the time span threshold interval to obtain the first compression execution result; If the data time span of the transit cache station is less than the lower limit of the time span threshold interval, the initial compression result is corrected and compressed according to the lower limit of the time span threshold interval to obtain the first compression execution result; Otherwise, no correction compression is performed, and the initial compression result is used as the first compression execution result.

8. The data processing method of the rotary drive device based on 5G and edge computing according to claim 2, wherein: The method for obtaining the 5G bandwidth utilization rate of the transit cache station and processing to obtain the second compression execution result is as follows: Obtain the real-time 5G bandwidth utilization rate of the transit cache station; Based on the 5G bandwidth utilization rate threshold and compare it with the real-time 5G bandwidth utilization rate of the transit cache station. If the real-time 5G bandwidth utilization rate is below the 5G bandwidth utilization rate threshold, no second compression correction processing is performed, and the first compression execution result is marked as the second compression execution result; If the real-time 5G bandwidth utilization rate is greater than the 5G bandwidth utilization rate threshold, second compression correction processing is performed, and based on the analysis of the real-time 5G bandwidth utilization rate and the 5G bandwidth utilization rate threshold, a bandwidth difference coefficient is obtained; Obtain each preset bandwidth difference coefficient interval in the database and the corresponding second compression reference adjustment ratio for each bandwidth difference coefficient interval, and compare it with the bandwidth difference coefficient. If the bandwidth difference coefficient is within a certain preset bandwidth difference coefficient interval, obtain the corresponding second compression reference adjustment ratio for this interval as the second compression adjustment ratio; Based on the second compression reference adjustment ratio and perform ratio adjustment with the first compression execution result to obtain the second compression execution result.

9. The data processing method of the rotary drive device based on 5G and edge computing according to claim 3, characterized in that: It also includes analyzing the change measurement value and fluctuation amplitude value of the rotational drive data, and thereby adjusting the data acquisition frequency of the rotational drive device. The specific method is as follows: Obtain the preset rate change threshold interval and fluctuation amplitude threshold interval in the database; Based on the comparison of the change measurement value of the rotational drive data with the rate change threshold interval, if the change measurement value of the rotational drive data is within the rate change threshold interval, no data acquisition frequency adjustment is performed. Otherwise, a first sampling frequency adjustment coefficient is obtained, and thereby a first data sampling frequency adjustment result is analyzed; If the change measurement value of the rotational drive data is greater than the maximum value of the rate change threshold interval, the sampling frequency is increased based on the first sampling frequency adjustment coefficient. If the change measurement value of the rotational drive data is less than the minimum value of the rate change threshold interval, the sampling frequency is decreased based on the first sampling frequency adjustment coefficient; Based on the comparison of the fluctuation amplitude value of the rotational drive data with the fluctuation amplitude threshold interval, if the fluctuation amplitude value of the rotational drive data is within the fluctuation amplitude threshold interval, no data acquisition frequency adjustment is performed. Otherwise, a second sampling frequency adjustment coefficient is obtained, and thereby a second data sampling frequency adjustment result is analyzed; If the fluctuation amplitude value of the rotational drive data is greater than the maximum value of the fluctuation amplitude threshold interval, the sampling frequency is increased based on the second sampling frequency adjustment coefficient. If the fluctuation amplitude value of the rotational drive data is less than the minimum value of the fluctuation amplitude threshold interval, the sampling frequency is decreased based on the second sampling frequency adjustment coefficient; Based on the analysis of the first data sampling frequency adjustment result and the second data sampling frequency adjustment result, the data acquisition frequency of the rotational drive device is adjusted.

10. A system applying the method for processing data of a rotary transmission device based on 5G and edge computing according to any one of claims 1-9, characterized in that, Includes: Light load state node analysis module, target light load state node matching module, data to be uploaded analysis module, initial compression module, correction compression judgment module, and secondary compression execution module; Among them, the light load state node analysis module is used to obtain the resource state parameters of each edge node after the detection device receives the rotation drive device data acquisition signal, and analyze to obtain the light load state nodes; The target light load state node matching module is used to obtain the rotation drive data rate change parameters within a preset time period, analyze to obtain the resource state demand value of the data acquisition port, and match to obtain the target light load state node for data processing; The data to be uploaded analysis module is used to obtain the rotation drive data fluctuation amplitude parameters within a preset time period, analyze to obtain the rotation drive data fluctuation amplitude value, adjust the target light load state node filtering parameters accordingly, and process to obtain the data to be uploaded; The initial compression module is used to upload the data to be uploaded to the transfer cache station, obtain the data volume of the transfer cache station, and perform initial compression determination to obtain the initial compression result; The correction compression judgment module is used to obtain the data time span of the transfer cache station, judge whether correction compression is required, and process to obtain the primary compression execution result; The secondary compression execution module is used to obtain the 5G bandwidth utilization rate of the transfer cache station, process to obtain the secondary compression execution result, and after performing the corresponding compression processing, upload the data to be uploaded in the transfer cache station to the cloud again.

Images