Rotating transmission device data processing method and system based on 5G and edge computing

Abstract

The present invention discloses a data processing method and system for a rotating transmission device based on 5G and edge computing, which belongs to the field of data processing technology and includes the following steps: S1, analyzing and obtaining a light-load state node; S2, obtaining a target light-load state node for data processing; S3, analyzing and obtaining a fluctuation amplitude value of the rotating transmission data, thereby adjusting the filter parameters of the target light-load state node, and processing to obtain data to be uploaded; S4, obtaining an initial compression result; S5, obtaining a first compression execution result; S6, obtaining a second compression execution result, and after performing corresponding compression processing, uploading the data to be uploaded in the transfer cache station to the cloud again, 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 field of data processing technology, 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 used in fields such as automobile manufacturing. During the production process, high requirements are placed on the operating status of rotary transmission devices and the timeliness of data analysis. Existing data processing methods are implemented through centralized cloud computing centers.

[0003] For example, the data processing method and data processing device announced in the invention patent with announcement number 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, and 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 the second resource for the second sub-task based on the second sub-resource, and the second resource is a resource in the first resource other than the first sub-resource.

[0004] However, in the process of implementing the technical solutions of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0005] In the existing technology, the data obtained by edge nodes is usually processed centrally. However, for the batch data generated by the rotating transmission device, the amount of data generated is large and the frequency is high. Therefore, 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 centralized cloud computing centers, with high data transmission delay and large bandwidth occupancy, which is difficult to meet the strict requirements of the rotating transmission device for real-time and accuracy. Therefore, there is a problem 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 solve the problems of low data processing efficiency and poor timeliness caused by insufficient flexibility of processing resources in the prior art by providing a data processing method and system for a rotating transmission device based on 5G and edge computing, and realize timely processing and uploading 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, comprising the following steps: S1. After a detection device receives a data acquisition signal from the rotary transmission device, the detection device obtains resource status parameters of each edge node, thereby analyzing and obtaining a light-load state node; S2. The rotation transmission data rate change parameter within a preset time period is obtained, and the resource status requirement value of the data acquisition port is analyzed, thereby matching and obtaining a target light-load state node for data processing; S3. The rotation transmission data fluctuation amplitude parameter within a preset time period is obtained, and the fluctuation amplitude value of the rotation transmission data is analyzed, thereby adjusting the filter parameters of the target light-load state node, and processing to obtain data to be uploaded; S4. The data to be uploaded is uploaded to a transit cache station, the data volume of the transit cache station is obtained, and an initial compression determination is performed to obtain an initial compression result; S5. The data time span of the transit cache station is obtained, and it is determined whether to perform corrective compression, thereby obtaining a primary compression execution result; S6. The 5G bandwidth utilization rate of the transit cache station is obtained, and the secondary compression execution result is obtained by processing. After performing corresponding compression processing, the data to be uploaded in the transit cache station is uploaded to the cloud again.

[0008] Furthermore, it also includes adjusting the data acquisition frequency of the rotary transmission device based on the analysis of the change measurement value and the fluctuation amplitude value of the rotary transmission data, and the specific method is: obtaining the rate change threshold interval and the fluctuation amplitude threshold interval preset in the database; comparing the change measurement value of the rotary transmission data with the rate change threshold interval, if the change measurement value of the rotary transmission data is within the rate change threshold interval, then the data acquisition frequency adjustment is not performed, otherwise the first sampling frequency adjustment coefficient is obtained, and the first data sampling frequency adjustment result is obtained by analysis; if the change measurement value of the rotary transmission data is greater than the maximum value of the rate change threshold interval, then the sampling frequency is increased based on the first sampling frequency adjustment coefficient, and if the change measurement value of the rotary transmission data is less than the minimum value of the rate change threshold interval, then the sampling frequency is adjusted based on the first sampling frequency adjustment coefficient. The sampling frequency is reduced by the first adjustment coefficient of the sampling frequency; the fluctuation amplitude value of the rotation transmission data is compared with the fluctuation amplitude threshold interval. If the fluctuation amplitude value of the rotation transmission data is within the fluctuation amplitude threshold interval, the data acquisition frequency adjustment is not performed; otherwise, the second adjustment coefficient of the sampling frequency is obtained, and the second adjustment result of the data sampling frequency is obtained by analysis; if the fluctuation amplitude value of the rotation transmission data is greater than the maximum value of the fluctuation amplitude threshold interval, the sampling frequency is increased based on the second adjustment coefficient of the sampling frequency; if the fluctuation amplitude value of the rotation transmission data is less than the minimum value of the fluctuation amplitude threshold interval, the sampling frequency is reduced based on the second adjustment coefficient of the sampling frequency; 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 rotation transmission device is adjusted.

[0009] The present application provides a data processing system for a rotary transmission device based on 5G and edge computing, including: a light-load state node analysis module, a target light-load state node matching module, a data to be uploaded analysis module, an initial compression module, a modified compression judgment module, and a secondary compression execution module; wherein the light-load state node analysis module is configured to obtain resource state parameters of each edge node after a detection device receives a data acquisition signal of the rotary transmission device, thereby analyzing and obtaining a light-load state node; the target light-load state node matching module is configured to obtain a rotation transmission data rate change parameter within a preset time period, analyze and obtain a resource state requirement value of a data acquisition port, thereby matching and obtaining a target light-load state node for data processing; the data to be uploaded analysis module is configured to obtain a rotation transmission data fluctuation amplitude parameter within a preset time period, analyze and obtain a fluctuation amplitude value of the rotation transmission data, thereby adjusting a target light-load state node filtering parameter, and processing to obtain data to be uploaded; the initial compression module is configured to upload the data to be uploaded to a transfer cache station, obtain the data volume of the transfer cache station, thereby performing an initial compression judgment and obtaining an initial compression result; the modified compression judgment module is configured to obtain the data time span of the transfer cache station, determine whether to perform modified compression, and thereby obtain a primary compression execution result;

[0010] The secondary compression execution module is used to obtain the 5G bandwidth utilization of the transfer cache station, process the secondary compression execution results, and perform corresponding compression processing before uploading the data to be uploaded in the transfer cache station to the cloud.

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

[0012] 1. The data processing method for a rotary transmission device based on 5G and edge computing provided by the present invention optimizes resource allocation and task scheduling by identifying light-loaded nodes at edge nodes based on resource status parameters and matching the actual operating parameters of the rotary transmission device, thereby realizing 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 initial compression, correction compression and secondary compression, thereby dynamically adjusting the compression ratio according to the data volume, data time span and 5G bandwidth utilization of the transit cache station. While ensuring data quality, it reduces data transmission volume and storage requirements, optimizes the utilization 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 fluctuation amplitude of the rotation transmission data, the data quality is improved, the impact of noise and interference is reduced, and the data uploaded to the cloud is guaranteed to have higher accuracy and reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A flow chart of a data processing method for a rotary transmission device based on 5G and edge computing provided in an embodiment of the present application;

[0016] Figure 2 A flowchart of a data processing method for a rotary transmission device based on 5G and edge computing provided in an embodiment of the present application;

[0017] Figure 3 A specific flow chart for uploading data to be uploaded from a transfer cache station to the cloud provided in an embodiment of the present application;

[0018] Figure 4 A schematic structural diagram of a rotation transmission device data processing system based on 5G and edge computing provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] The embodiments of the present application solve the problems of low data processing efficiency and poor timeliness caused by insufficient flexibility of processing resources in the prior art by providing a data processing method and system for a rotating transmission device based on 5G and edge computing, thereby achieving timely processing and uploading of data.

[0020] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0021] like Figure 1 As shown, a flow chart of a data processing method for a rotary transmission device based on 5G and edge computing provided by an embodiment of the present application is provided. The method includes the following steps: S1. When the detection device receives the data acquisition signal of the rotary transmission device, the resource status parameter of each edge node is obtained, and the light-load state node is obtained by analysis. S2. The rotation transmission data rate change parameter within a preset time period is obtained, and the resource status requirement value of the data acquisition port is obtained by analysis, thereby matching the target light-load state node for data processing. S3. The rotation transmission data fluctuation amplitude parameter within the preset time period is obtained, and the fluctuation amplitude value of the rotation transmission data is obtained by analysis, thereby adjusting the filter parameter of the target light-load state node, and processing to obtain the data to be uploaded. S4. The data to be uploaded is uploaded to a transfer cache station, and the data volume of the transfer cache station is obtained, thereby performing an initial compression judgment and obtaining an initial compression result. S5. The data time span of the transfer cache station is obtained, and it is determined whether to perform corrective compression, thereby obtaining a primary compression execution result. S6. The 5G bandwidth utilization rate of the transfer cache station is obtained, and the secondary compression execution result is obtained by processing. After performing the corresponding compression processing, the data to be uploaded in the transfer cache station is uploaded to the cloud.

[0022] In this embodiment, if Figure 2As shown, this is a flow chart for implementing the data processing method for a rotating transmission device based on 5G and edge computing provided in an embodiment of the present application. From this analysis, it is obtained that: when the data acquisition signal of the rotating transmission device is received, the node currently in the light-load state is obtained, the light-load state node is matched with the resource state requirement value of the data acquisition port to determine the most suitable target light-load state node, and then it is judged whether the fluctuation amplitude value of the rotating transmission data is greater than the 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 cutoff frequency and increase the filtering strength. If not, the data is uploaded to the transit cache station, and the data to be uploaded in the transit cache station is processed before being uploaded to the cloud.

[0023] like Figure 3 As shown, it is a specific flow chart of uploading the data to be uploaded of the transit cache station to the cloud provided by an embodiment of the present application, and the specific process of processing the data to be uploaded of the transit cache station and then uploading it to the cloud is obtained by analysis: at the transit cache station, the time span of the data is judged to check whether the time span of the data is within the preset threshold range. If so, the initial compression result is used as the one-time compression execution result. If otherwise, the initial compression result is corrected and compressed according to the time span threshold range to obtain the one-time compression execution result; the 5G bandwidth utilization of the transit cache station is judged to check whether the 5G bandwidth utilization is lower than the preset threshold. If so, the one-time compression execution result is marked as the secondary compression execution result. If otherwise, the secondary compression correction processing is performed to obtain the secondary compression execution result, and finally, the processed data is uploaded to the cloud.

[0024] It should be noted that the target light-load state node is matched and data processing is performed in the following manner: the available resource value of the light-load state node is obtained (which can be obtained through the performance monitoring device built into the rotary transmission device), and compared with the resource state requirement value of the data acquisition port. The light-load state node whose available resource value is greater than the resource state requirement value of the data acquisition port and closest to the resource state requirement value of the data acquisition port is obtained as the target light-load state node. The data processing specifically includes: the target light-load state node first receives the rotary transmission data rate change parameter within a preset time period and performs preliminary processing, including denoising, to ensure data quality. The available resource value of the light-load state node is compared with the resource state requirement value of the data acquisition port. If the requirements are met, resources are reasonably allocated and a data processing task is created, which is added to the task queue for execution. During the execution of the task, the resource usage of the node is continuously monitored.

[0025] Furthermore, the resource status parameters of each edge node are obtained, and the lightly loaded state nodes are obtained by analysis. The specific method is as follows: the resource status parameters of each edge node are obtained, and the resource status parameters include CPU utilization and 5G bandwidth utilization; the CPU utilization threshold and 5G bandwidth utilization threshold preset in the database are obtained, and compared with the resource status parameters. If the CPU utilization is above the CPU utilization threshold or the 5G bandwidth utilization is above the 5G bandwidth utilization threshold, the edge node is a heavily loaded state node; otherwise, the edge node is a lightly loaded state node.

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

[0027] By obtaining the resource status parameters of each edge node to analyze the lightly loaded nodes, data processing tasks can be assigned to nodes with sufficient resources, avoiding resource competition and congestion during the data processing process, effectively improving the efficiency of data processing. At the same time, tasks are not reallocated to the heavy loaded nodes to avoid problems such as system errors caused by resource overload and excessive data volume. To a certain extent, the reasonable allocation of resources is improved, and the resource utilization efficiency and data processing efficiency are improved.

[0028] Furthermore, a rotation transmission data rate change parameter within a preset time period is obtained, and the resource state requirement value of the data acquisition port is obtained by analysis. The specific method is as follows: the rotation transmission data rate change parameter within the preset time period is obtained through the data acquisition port, and the rotation transmission data rate change parameter includes the transmission ratio change rate, the torque fluctuation frequency and the load inertia change rate; the rotation transmission data rate change calibration set preset in the database is obtained, and compared and analyzed with the transmission ratio change rate, the torque fluctuation frequency and the load inertia change rate respectively to obtain the comparative analysis results, and the corresponding weighting factors are introduced to obtain the change measurement value of the rotation transmission data; the rotation transmission data rate change calibration set includes the transmission ratio change rate calibration value, the torque fluctuation frequency calibration value and the load inertia change rate calibration value; each rotation transmission data rate change value interval preset in the database and the resource state requirement reference value corresponding to each rotation transmission data rate change value interval are obtained, and compared with the change measurement value of the rotation transmission data. If the change measurement value of the rotation transmission data is within a certain preset rotation transmission data rate change value interval, the resource state requirement reference value corresponding to the interval is obtained as the resource state requirement value of the data acquisition port.

[0029] In this embodiment, the transmission ratio change rate refers to the maximum amplitude of the transmission ratio change within a preset time period (specifically, the rate of change between the maximum and minimum values ​​of the transmission ratio), which is obtained by monitoring the rotation speeds of the driving wheel and the driven wheel in real time using a speed sensor and an encoder installed on the rotary transmission device to obtain the transmission ratio. The torque fluctuation frequency is the frequency of change of the torque during the fluctuation process. The torque fluctuation frequency indicates the highest frequency of the torque fluctuation and can be obtained by monitoring the torque output of the rotary transmission device in real time using a torque sensor. The load inertia change rate is the rate of change of the load inertia with time. The load inertia change rate is the maximum amplitude of the change of the load inertia per unit time within a preset time period. The load inertia change rate can be obtained by detection using an inertial measurement unit built into the rotary transmission device.

[0030] The change measurement value of the rotation transmission data is obtained by:

[0031] ;

[0032] Where, Indicates the change measurement value of the rotation transmission data, represents the rate of change of transmission ratio, Indicates the transmission ratio change rate calibration value, represents the torque fluctuation frequency, Indicates the torque fluctuation frequency calibration value, Indicates the load inertia change rate, Indicates the load inertia change rate calibration value, represents the transmission ratio change rate weighting factor, represents the torque fluctuation frequency weighting factor, Indicates the weighting factor of the load inertia change rate.

[0033] The transmission ratio change rate weighting factor, torque fluctuation frequency weighting factor and load inertia change rate weighting factor can be obtained from the database. For example, the transmission ratio change rate weighting factor can be obtained by obtaining the historical transmission ratio change rates stored in the database, and the transmission ratio change rate weighting factors corresponding to the historical transmission ratio change rates, thereby constructing a transmission ratio change rate mapping set, wherein there is a one-to-one or many-to-one correspondence in the mapping set. The transmission ratio change rate weighting factor can be obtained by inputting the transmission ratio change rate data to be used into the transmission ratio change rate mapping set. The acquisition method of other weighting factors is the same as the acquisition method of the transmission ratio change rate weighting factor, and they can all be matched in the corresponding mapping set, wherein the torque fluctuation frequency weighting factor corresponds to the torque fluctuation frequency mapping set, and the load inertia change rate weighting factor corresponds to the load inertia change rate mapping set.

[0034] By analyzing the parameters of the rotating transmission data rate (gear ratio change rate, torque ripple frequency, and load inertia change rate), we consider the interplay between these parameters. For example, the gear ratio change rate affects the equivalent change in torque transmission and load inertia. Excessive gear ratio change rates can increase the torque ripple frequency and load inertia change rate. The torque ripple frequency reflects the effects of shock or imbalance on the rotating transmission system, which can increase the gear ratio change rate and exacerbate load inertia instability. The load inertia change rate affects the inertial characteristics and stability of the transmission system. Rapid changes in load inertia can lead to increased gear ratio change rates and increased torque ripple frequency.

[0035] By obtaining the rotation transmission data rate change parameters within a preset time period, it is possible to ensure that data processing tasks are assigned to edge nodes with appropriate resource status, improve resource utilization efficiency, avoid processing delays or failures caused by unreasonable resource allocation, and optimize task scheduling efficiency. By accurately analyzing the operating status of the rotation transmission device and quickly determining the corresponding edge node resource status requirements, it is possible to quickly respond to the data processing needs of the rotation transmission device under different working conditions. The selection of edge nodes with appropriate resource status based on the rotation transmission data rate change parameters within a preset time period takes into account that the data at some collection points may fluctuate greatly or the data volume is large. Therefore, it is necessary to select edge nodes with good data processing capabilities for processing. This can avoid insufficient edge node resources due to excessive data volume, which may cause edge node crashes.

[0036] This step focuses on the rotational transmission data rate change parameters. These parameters are mainly used to quantify the rate of change of various data during the operation of the rotational transmission device, such as the dynamic changes of parameters such as transmission ratio, torque, and load inertia. These parameters can reflect the changes in the resource requirements of the rotational transmission device for data processing under different working conditions, helping to reasonably allocate and schedule the resources of edge computing nodes.

[0037] Furthermore, the fluctuation amplitude parameters of the rotation transmission data within a preset time period are obtained, and the fluctuation amplitude value of the rotation transmission data is obtained by analysis. The specific method is: the fluctuation amplitude parameters of the rotation transmission data within a preset time period are obtained through the data acquisition port, and the fluctuation amplitude parameters of the rotation transmission data include torque mutation frequency, vibration energy entropy, maximum angular displacement deviation, maximum torque fluctuation amplitude, maximum phase deviation and speed fluctuation frequency; a fluctuation amplitude fair set preset in the database is obtained, and compared and analyzed with the torque mutation frequency, vibration energy entropy, angular displacement deviation, torque fluctuation amplitude, phase deviation and speed fluctuation frequency respectively to obtain comparative analysis results, and a corresponding weighting factor is introduced to quantify the comparative analysis results to obtain the fluctuation amplitude value of the rotation transmission data; the fluctuation amplitude fair set includes the fair value of torque mutation frequency, the fair value of vibration energy entropy, the fair value of angular displacement deviation, the fair value of torque fluctuation amplitude, the fair value of phase deviation and the fair value of 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 period of time, reflecting the most extreme frequency of torque change when the rotary transmission device is affected by an impact load or a sudden change in working conditions during operation. It can be obtained by extracting features from the torque signal collected by the torque sensor.

[0039] Vibration energy entropy indicates the degree of concentration of the energy distribution of the vibration signal; larger values ​​indicate less concentration. It is obtained through simulation using molecular dynamics simulation software (such as CPMD). The maximum torque fluctuation amplitude refers to the maximum amplitude of change in torque during fluctuations within a preset time period (the difference between the maximum and minimum torques). It reflects the stability of the torque output of the rotary transmission device during operation. It is obtained by real-time monitoring of the torque output of the rotary transmission device through a torque sensor. The maximum phase deviation refers to the maximum phase difference between the axes in a multi-axis collaborative rotary transmission system within a preset time period. It is obtained by synchronously collecting the phase information of each axis through a high-precision phase sensor. The speed fluctuation frequency refers to the highest frequency of change in speed during fluctuations within a preset time period. It reflects the severity of the speed change during operation of the rotary transmission device and can be obtained by real-time acquisition of the speed signal of the rotary transmission device through a speed sensor.

[0040] The fluctuation amplitude value of the rotation transmission data is obtained by:

[0041] ;

[0042] Where, Indicates the fluctuation amplitude value of the rotation transmission data, represents the torque mutation frequency, Indicates the fair value of torque mutation frequency, represents the vibration energy entropy, represents the fair value of vibration energy entropy, Indicates the maximum angular displacement deviation, Indicates the fair value of angular displacement deviation, Indicates the maximum torque fluctuation amplitude, Indicates the fair value of torque fluctuation amplitude, Indicates the maximum phase deviation, represents the fair value of phase deviation, Indicates the speed fluctuation frequency, Indicates the fair value of speed fluctuation frequency, represents the torque mutation frequency weighting factor, represents the vibration energy entropy weighting factor, represents the angular displacement deviation weighting factor, represents the torque fluctuation amplitude weighting factor, represents the phase deviation weighting factor, Represents the speed fluctuation frequency weighting factor.

[0043] By analyzing the fluctuation amplitude parameters of the rotational transmission data (including torque mutation frequency, vibration energy entropy, maximum angular displacement deviation, maximum torque fluctuation amplitude, maximum phase deviation, and speed fluctuation frequency), the interplay between these parameters is considered. An increase in the torque mutation frequency is typically accompanied by an increase in the torque fluctuation amplitude, leading to increased vibration in the rotational transmission system and, consequently, an increase in vibration energy entropy. Furthermore, torque mutations and fluctuations affect rotational smoothness, leading to an increase in the speed fluctuation frequency. Torque and speed fluctuations can lead to angular displacement deviation and phase deviation. Greater angular displacement and phase deviation indicate greater deviations in the rotational transmission system's positioning accuracy and multi-axis synchronization, further exacerbating system instability. For example, frequent torque mutations lead to increased angular displacement deviation, which in turn affects the phase synchronization of the multi-axis system, reducing 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 speed fluctuation frequency weighting factor can be obtained from the database. For example, the torque mutation frequency weighting factor can be obtained by obtaining the historical torque mutation frequencies stored in the database, and the torque mutation frequency weighting factors corresponding to the historical torque mutation frequencies, thereby constructing a torque mutation frequency mapping set, wherein there is a one-to-one or many-to-one correspondence in the mapping set. The torque mutation frequency weighting factor can be obtained by inputting the required torque mutation frequency data into the torque mutation frequency mapping set. The other weighting factors are obtained in the same way as the torque mutation frequency weighting factor, and can all be matched in the corresponding mapping set. 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 speed fluctuation frequency weighting factor corresponds to the speed fluctuation frequency mapping set.

[0045] Analyzing the fluctuation amplitude parameters of rotary drive data reveals different aspects of the device's dynamic characteristics and stability, helping to promptly identify potential problems. Based on this analysis of fluctuation amplitude values, the system can adjust control strategies in real time, optimizing the operation of the rotary drive device and improving production efficiency and product quality.

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

[0047] By evaluating rotating transmission devices from two dimensions, namely, rate of change and fluctuation amplitude, we can gain a more comprehensive understanding of the device's operating characteristics and requirements. The rate of change parameter helps optimize resource allocation and task scheduling, while the fluctuation amplitude parameter focuses on improving system stability and reliability.

[0048] By determining the measurement value of the rotational transmission data change based on the rotational transmission data rate change parameter, the reference value of the resource status requirement of the adapted processing node is accurately matched. This process ensures that data processing tasks are properly allocated to edge nodes with the corresponding resources, preventing processing delays or failures caused by unreasonable resource allocation, optimizing task scheduling efficiency, and ensuring the timeliness and accuracy of data processing. By analyzing the rotational transmission data fluctuation amplitude parameter, the fluctuation amplitude value of the rotational transmission data is determined, and based on this value, targeted filtering and adjustment are performed on the data allocated to the corresponding node, ensuring the high accuracy and reliability of the data uploaded to the cloud. It also allows the target light-load state node to better adapt to changes in the current rotational transmission device data and make timely adjustments.

[0049] Furthermore, the target light-load state node filtering parameters are adjusted, and the data to be uploaded is obtained by processing. The specific method is as follows: obtaining the current target light-load state node filtering parameters, the target light-load state node filtering parameters include the filtering cutoff frequency and filtering strength of the target light-load state node; obtaining the preset reference value of the rotation transmission data fluctuation amplitude in the database, and comparing it with the fluctuation amplitude value of the rotation transmission data; if the fluctuation amplitude value of the rotation transmission data is greater than the rotation transmission data fluctuation amplitude reference value, the data of the target light-load state node is directly transmitted to the cloud, and the light-load state node filtering parameters are synchronously adjusted; if the fluctuation amplitude value of the rotation transmission data is below the rotation transmission data fluctuation amplitude reference value, the data of the target light-load state node is marked as data to be uploaded; if the target light-load state node filtering parameters are synchronously adjusted, a proportional analysis is performed based on the fluctuation amplitude value of the rotation transmission data and the rotation transmission data fluctuation amplitude reference value to obtain the target light-load state node filtering adjustment coefficient; based on the target light-load state node filtering adjustment coefficient, the filtering cutoff frequency of the target light-load state node is reduced and the filtering strength is increased.

[0050] In this embodiment, a proportional analysis is performed based on the fluctuation amplitude value of the rotation transmission data and the fluctuation amplitude reference value of the rotation transmission data to obtain a target light-load state node filtering adjustment coefficient. The specific method is: the fluctuation amplitude value of the rotation transmission data and the fluctuation amplitude reference value of the rotation transmission data are subjected to difference processing to obtain a fluctuation amplitude difference, and then the fluctuation amplitude difference is divided by the fluctuation amplitude reference value of the rotation transmission data to obtain a target light-load state node filtering adjustment coefficient.

[0051] Based on the target light load state node filter adjustment coefficient, the filter cutoff frequency of the target light load state node is reduced and the filter strength is increased. The specific steps are: obtain the filter adjustment coefficient intervals of each target light load state node preset in the database and the reference filter cutoff frequency adjustment coefficient and the reference filter strength adjustment coefficient corresponding to each target light load state node filter adjustment coefficient interval, and compare them with the target light load state node filter adjustment coefficient. If the target light load state node filter adjustment coefficient is within a certain target light load state node filter adjustment coefficient interval, then obtain the reference filter cutoff frequency adjustment coefficient and the reference filter strength adjustment coefficient corresponding to the interval as the filter cutoff frequency adjustment coefficient and the filter strength adjustment coefficient.

[0052] Obtain the filter cutoff frequency of the initial target light-load state node, and reduce the filter cutoff frequency of the target light-load state node based on the filter cutoff frequency adjustment coefficient. The specific method is as follows: Where, Indicates the filter cutoff frequency of the target light-load state node after adjustment, Indicates the filter cutoff frequency of the initial target light-load state node, Represents the filter cutoff frequency adjustment coefficient. Get the initial target light-load state node's filter strength and improve the target light-load state node's filter strength based on the filter strength adjustment coefficient. The specific method is: Where, Indicates the filtering strength of the target light-load state node after adjustment, Indicates the filtering strength of the initial target light-load state node, Indicates the filter strength adjustment coefficient.

[0053] By dynamically adjusting the filter cutoff frequency and strength of the target light-load state node, the data processing process can be adaptively optimized based on changes in the fluctuation amplitude of the rotational transmission data, improving the accuracy and reliability of data processing and making the target light-load state node more adaptable to the output data of the current rotational transmission device. When the fluctuation amplitude exceeds the reference value, the data is directly transmitted to the cloud and the filter parameters are adjusted synchronously during the transmission process, ensuring the timely upload and processing of important data and avoiding data loss. The data processing method is determined based on the fluctuation amplitude comparison results, rationally utilizing the resources of the cloud and edge nodes, avoiding unnecessary data upload, and improving the overall efficiency of the system. By introducing the filter adjustment coefficient, the filter parameters are precisely controlled, effectively reducing noise and interference, and improving the quality of uploaded data.

[0054] Furthermore, an initial compression judgment is performed to obtain an initial compression result. The specific method is: uploading the data to be uploaded to the transit cache station to obtain the data volume of the transit cache station; obtaining the cache data volume threshold preset in the database, and comparing it with the data volume of the transit cache station. If the data volume of the transit cache station is above the cache data volume threshold, it is determined to perform initial data compression to obtain an initial compression result; if the data volume of the transit cache station is less than the cache data volume threshold, initial compression is not performed, and uploading to the cloud is not performed.

[0055] In this embodiment, it should be noted that a preset cache data volume threshold in the database is obtained and compared with the data volume in the transit cache station. If the data volume in the transit cache station is above the cache data volume threshold, a determination is made to perform initial data compression, resulting in an initial compression result, where the initial compression result is the compression ratio of the initial compression. It should be understood that a larger compression ratio indicates a smaller compressed file size relative to the original file, and therefore a smaller memory (or storage space) footprint. 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. Initial compression to a certain extent can ensure that more data is uploaded at the same time, reduce the amount of data uploaded to the cloud, and save bandwidth. The initial compression ratio can be obtained by mapping the cache data volume threshold preset in the database. The specific method is: based on the cache data volume threshold, analysis is performed, and the cache data volume intervals preset in the database and the reference initial compression ratio corresponding to each cache data volume interval are obtained, and compared with the cache data volume threshold. If the cache data volume threshold is within a preset cache data volume interval, the reference initial compression ratio corresponding to the cache data volume interval is obtained as the initial compression ratio.

[0057] By temporarily storing data in the transit cache and determining 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, initial data compression is triggered, optimizing bandwidth utilization efficiency. For smaller data volumes, frequent compression and upload operations are avoided, reducing system overhead; for larger data volumes, compression is used to ensure that data can be efficiently transmitted and stored. By monitoring and compressing the data volume in the transit cache, the total amount of data uploaded to the cloud is reduced, the burden on cloud storage is reduced, and the utilization of storage resources is improved. By determining the data volume and performing initial compression in the transit cache, data processing delays are reduced, the overall response speed of the system is improved, the timeliness and effectiveness of data are ensured, bandwidth load is avoided, and the efficiency and stability of data upload are guaranteed.

[0058] Further, the processing obtains a compression execution result, and the specific method is: obtain the data time span of the transit cache station; obtain the time span threshold interval preset in the database, and compare it with the data time span of the transit cache station, if the data time span of the transit cache station is greater than the upper limit value of the time span threshold interval, then the initial compression result is corrected and compressed according to the upper limit value of the time span threshold interval to obtain a compression execution result; if the data time span of the transit cache station is less than the lower limit value of the time span threshold interval, then the initial compression result is corrected and compressed according to the lower limit value of the time span threshold interval to obtain a compression execution result; otherwise, no correction and compression are performed, and the initial compression result is used as the compression execution result.

[0059] In this embodiment, the initial compression result is corrected and compressed according to the upper limit value of the time span threshold interval. The specific method is: obtain the value corresponding to the upper limit value of the time span threshold interval of the transit cache station, mark it as the maximum time length, and match it with the database, thereby obtaining a first correction compression ratio coefficient, thereby performing correction compression and obtaining a one-time compression execution result. The first correction compression ratio coefficient is obtained in a specific method as follows: obtain each time length interval preset in the database and the reference correction compression ratio coefficient corresponding to each time length interval, and compare them with the maximum time length. If the maximum time length is within a certain time length interval, obtain the reference correction compression ratio coefficient corresponding to the time length interval as the first correction compression ratio coefficient. Based on the first correction compression ratio coefficient, the initial compression ratio is compressed to reduce the compression ratio to obtain a one-time compression execution ratio, thereby obtaining a one-time compression execution result. The specific method is as follows: Where, Indicates the ratio of one compression execution. Indicates the initial compression ratio, Indicates the first correction compression ratio coefficient.

[0060] The initial compression result is corrected and compressed according to the lower limit value of the time span threshold interval. The specific method is: obtain the value corresponding to the lower limit value of the time span threshold interval of the transit cache station, mark it as the minimum time length, and match it with the database, thereby obtaining the correction compression ratio coefficient, thereby performing correction compression and obtaining a compression execution result. The correction compression ratio coefficient is obtained in the following specific method: obtain each time length interval preset in the database and the reference correction compression ratio coefficient corresponding to each time length interval, and compare them with the minimum time length. If the minimum time length is within a certain time length interval, obtain the reference correction compression ratio coefficient corresponding to the time length interval as the second correction compression ratio coefficient. Based on the second correction compression ratio coefficient, the initial compression ratio is compressed to reduce the compression ratio to obtain a compression execution ratio, thereby obtaining a compression execution result. The specific method is: Where, Indicates the ratio of one compression execution. Indicates the initial compression ratio, Indicates the second correction compression ratio coefficient.

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

[0062] Furthermore, the 5G bandwidth utilization of the transit cache station is obtained, and the secondary compression execution result is obtained by processing. The specific method is: obtaining the real-time 5G bandwidth utilization of the transit cache station; based on the 5G bandwidth utilization threshold and comparing it with the real-time 5G bandwidth utilization of the transit cache station, if the real-time 5G bandwidth utilization is below the 5G bandwidth utilization threshold, the secondary compression correction processing is not performed, and the primary compression execution result is marked as the secondary compression execution result; if the real-time 5G bandwidth utilization is greater than the 5G bandwidth utilization threshold, the secondary compression correction processing is performed, and analysis is performed based on the real-time 5G bandwidth utilization and the 5G bandwidth utilization threshold to obtain the bandwidth difference coefficient; obtaining each bandwidth difference coefficient interval preset in the database and the secondary compression reference adjustment ratio corresponding to each bandwidth difference coefficient interval, and comparing them with the bandwidth difference coefficient; if the bandwidth difference coefficient is within a certain preset bandwidth difference coefficient interval, obtaining the secondary compression reference adjustment ratio corresponding to the interval as the secondary compression adjustment ratio; performing proportional adjustment based on the secondary compression reference adjustment ratio and the primary compression execution result to obtain the secondary compression execution result.

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

[0064] Based on the secondary compression reference adjustment ratio and the proportional adjustment with the primary compression execution result, the secondary compression execution result is obtained. The specific method is: 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 recorded as the secondary compression execution result.

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

[0066] Based on the comparison of 5G bandwidth utilization thresholds and real-time utilization, data compression strategies can be flexibly adjusted. When bandwidth is sufficient, unnecessary compression can be avoided to preserve more data details. When bandwidth is limited, effective compression can be performed to rationally utilize limited bandwidth resources. When bandwidth permits, over-compression can be avoided to ensure data authenticity. By pre-compressing data at the edge based on bandwidth availability, the amount of data uploaded to the cloud is reduced, reducing the cloud's storage and processing burden.

[0067] Furthermore, it also includes adjusting the data acquisition frequency of the rotary transmission device based on the analysis of the change measurement value and the fluctuation amplitude value of the rotary transmission data, and the specific method is: obtaining the rate change threshold interval and the fluctuation amplitude threshold interval preset in the database; comparing the change measurement value of the rotary transmission data with the rate change threshold interval, if the change measurement value of the rotary transmission data is within the rate change threshold interval, then the data acquisition frequency adjustment is not performed, otherwise the first sampling frequency adjustment coefficient is obtained, and the first data sampling frequency adjustment result is obtained by analysis; if the change measurement value of the rotary transmission data is greater than the maximum value of the rate change threshold interval, then the sampling frequency is increased based on the first sampling frequency adjustment coefficient, and if the change measurement value of the rotary transmission data is less than the minimum value of the rate change threshold interval, then the sampling frequency is adjusted based on the first sampling frequency adjustment coefficient. The sampling frequency is reduced by the first adjustment coefficient of the sampling frequency; the fluctuation amplitude value of the rotation transmission data is compared with the fluctuation amplitude threshold interval. If the fluctuation amplitude value of the rotation transmission data is within the fluctuation amplitude threshold interval, the data acquisition frequency adjustment is not performed; otherwise, the second adjustment coefficient of the sampling frequency is obtained, and the second adjustment result of the data sampling frequency is obtained by analysis; if the fluctuation amplitude value of the rotation transmission data is greater than the maximum value of the fluctuation amplitude threshold interval, the sampling frequency is increased based on the second adjustment coefficient of the sampling frequency; if the fluctuation amplitude value of the rotation transmission data is less than the minimum value of the fluctuation amplitude threshold interval, the sampling frequency is reduced based on the second adjustment coefficient of the sampling frequency; 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 rotation transmission device is adjusted.

[0068] In this embodiment, the data acquisition frequency of the rotating transmission device is adjusted 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 specific steps are: obtaining the data acquisition frequency of the rotating transmission device, and making 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 analyzing to obtain the data acquisition frequency of the rotating transmission device after adjustment. Where, Indicates the data acquisition frequency of the rotary transmission device after adjustment, represents the data acquisition frequency of the initial rotating transmission device, Indicates the first adjustment coefficient of the 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 decrease the sampling frequency, then an analysis is performed to obtain the data acquisition frequency of the rotary transmission device after adjustment. Where, Indicates the data acquisition frequency of the rotary transmission device after adjustment, represents the data acquisition frequency of the initial rotating transmission device, Indicates the first adjustment coefficient of the sampling frequency, Represents 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 the data acquisition frequency of the rotary transmission device after adjustment is obtained by analysis. Where, Indicates the data acquisition frequency of the rotary transmission device after adjustment, represents the data acquisition frequency of the initial rotating transmission device, Indicates the first 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, an analysis is performed to obtain the data acquisition frequency of the rotary transmission device after adjustment. Where, Indicates the data acquisition frequency of the rotary transmission device after adjustment, represents the data acquisition frequency of the initial rotating transmission device, Indicates the first adjustment coefficient of the sampling frequency, Indicates the second adjustment coefficient of the sampling frequency.

[0069] like Figure 4As shown, it is a structural diagram of the rotation transmission device data processing system based on 5G and edge computing provided by an embodiment of the present application. The rotation transmission device data processing system 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, a data analysis module to be uploaded, an initial compression module, a correction compression judgment module and a secondary compression execution module; wherein 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 transmission device data acquisition signal, thereby analyzing and obtaining the light-load state node; the target light-load state node matching module is used to obtain the rotation transmission data rate change parameter within a preset time period, analyze and obtain the resource state requirement value of the data acquisition port, thereby matching and obtaining the target The light-load state node processes the data; the data analysis module to be uploaded is used to obtain the fluctuation amplitude parameters of the rotation transmission data within a preset time period, analyze the fluctuation amplitude value of the rotation transmission data, thereby adjusting the target light-load state node filtering parameters, and processing to obtain the data to be uploaded; the initial compression module is used to upload the data to be uploaded to the transit cache station, obtain the data volume of the transit cache station, thereby performing an initial compression judgment and obtaining an initial compression result; the correction compression judgment module is used to obtain the data time span of the transit cache station, determine whether to perform correction compression, and thereby obtain a primary compression execution result; the secondary compression execution module is used to obtain the 5G bandwidth utilization rate of the transit cache station, process the secondary compression execution result, and after performing the corresponding compression processing, upload the data to be uploaded of the transit cache station to the cloud again.

[0070] In summary, this embodiment optimizes resource allocation and task scheduling by identifying light-load state nodes at edge nodes based on resource state parameters and matching the actual operating parameters of the rotating transmission device, thereby realizing timely processing and uploading of data, and 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 will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0072] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0073] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0074] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0075] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0076] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A data processing method for a rotary transmission device based on 5G and edge computing, characterized in that: The following steps are involved: S1. After receiving the data acquisition signal of the rotating transmission device, the detection device obtains the resource status parameters of each edge node, and thereby analyzes and obtains the light-load state node; S2. Obtaining a rotation transmission data rate change parameter within a preset period, analyzing and obtaining a resource state requirement value of a data acquisition port, and thereby matching a target light-load state node for data processing; S3. Obtaining a rotation transmission data fluctuation amplitude parameter within a preset time period, analyzing the obtained rotation transmission data fluctuation amplitude value, adjusting the target light-load state node filtering parameter accordingly, and processing the obtained data to be uploaded; S4. Uploading the data to be uploaded to the transfer cache station, obtaining the data volume of the transfer cache station, and performing an initial compression determination based on the data volume to obtain an initial compression result; S5. Obtain the data time span of the transit cache station, determine whether to perform correction compression, and thereby obtain a compression execution result; S6. Obtain the 5G bandwidth utilization of the transfer cache station, process the obtained secondary compression execution result, perform corresponding compression processing, and then upload the data to be uploaded in the transfer cache station to the cloud; The resource status parameters of each edge node are obtained, and the lightly loaded node is obtained by analysis. The specific method is as follows: Obtain resource status parameters of each edge node, including CPU utilization and 5G bandwidth utilization; Obtain the CPU utilization threshold and 5G bandwidth utilization threshold preset in the database and compare them with the resource status parameters. If the CPU utilization is above the CPU utilization threshold or the 5G bandwidth utilization is above the 5G bandwidth utilization threshold, the edge node is a heavily loaded node; otherwise, the edge node is a lightly loaded node.

2. The method for processing data of a rotary transmission device based on 5G and edge computing according to claim 1, wherein: The method of obtaining the rotation transmission data rate change parameter within the preset period and analyzing the resource state requirement value of the data acquisition port is as follows: Acquiring, through a data acquisition port, rotation transmission data rate change parameters within a preset period, wherein the rotation transmission data rate change parameters include a transmission ratio change rate, a torque fluctuation frequency, and a load inertia change rate; Obtain a preset rotation transmission data rate change calibration set in the database, and compare and analyze it with the transmission ratio change rate, torque fluctuation frequency, and load inertia change rate, obtain the comparative analysis results, and introduce corresponding weighting factors to obtain the change measurement value of the rotation transmission data; The rotation transmission data rate change calibration set includes a transmission ratio change rate calibration value, a torque fluctuation frequency calibration value, and a load inertia change rate calibration value; Obtain each rotation transmission data rate change value interval preset in the database and the resource state requirement reference value corresponding to each rotation transmission data rate change value interval, and compare them with the change measurement value of the rotation transmission data. If the change measurement value of the rotation transmission data is within a preset rotation transmission data rate change value interval, obtain the resource state requirement reference value corresponding to the interval as the resource state requirement value of the data acquisition port.

3. The data processing method for a rotary transmission device based on 5G and edge computing according to claim 1, characterized in that: The method of obtaining the fluctuation amplitude parameter of the rotation transmission data within the preset time period and analyzing the fluctuation amplitude value of the rotation transmission data is as follows: Obtaining, through a data acquisition port, rotational transmission data fluctuation amplitude parameters within a preset period, the rotational transmission data fluctuation amplitude parameters including torque mutation frequency, vibration energy entropy, maximum angular displacement deviation, maximum torque fluctuation amplitude, maximum phase deviation, and speed fluctuation frequency; Obtain the preset fair set of fluctuation amplitudes in the database, and perform comparative analysis with torque mutation frequency, vibration energy entropy, angular displacement deviation, torque fluctuation amplitude, phase deviation, and speed fluctuation frequency, respectively. Obtain comparative analysis results, and introduce corresponding weighting factors to quantify the comparative analysis results, thus obtaining the fluctuation amplitude value of the rotation transmission data. The fluctuation amplitude fair set includes a torque mutation frequency fair value, a vibration energy entropy fair value, an angular displacement deviation fair value, a torque fluctuation amplitude fair value, a phase deviation fair value and a speed fluctuation frequency fair value.

4. The data processing method for a rotary transmission device based on 5G and edge computing according to claim 1, characterized in that: The method of adjusting the filtering parameters of the target light-load state node and processing the data to be uploaded is as follows: Obtaining filtering parameters of a current target light-load state node, wherein the filtering parameters of the target light-load state node include a filtering cutoff frequency and a filtering strength of the target light-load state node; Obtain a preset reference value for the fluctuation amplitude of the rotation transmission data in the database and compare it with the fluctuation amplitude value of the rotation transmission data. If the fluctuation amplitude value of the rotation transmission data is greater than the reference value, directly transmit the data of the target light-load state node to the cloud, and synchronously adjust the filtering parameters of the light-load state node. If the fluctuation amplitude value of the rotation transmission data is less than the reference value, mark the data of the target light-load state node as data to be uploaded. If the target light-load state node filter parameter is adjusted synchronously, a proportional analysis is performed based on the fluctuation amplitude value of the rotation transmission data and the reference value of the fluctuation amplitude of the rotation transmission data to obtain the target light-load state node filter adjustment coefficient; The filter cutoff frequency of the target light-load state node is reduced and the filter strength is increased based on the filter adjustment coefficient of the target light-load state node.

5. The data processing method for a rotary transmission device based on 5G and edge computing according to claim 1, characterized in that: The method for performing the initial compression judgment and obtaining the initial compression result is as follows: Upload the data to be uploaded to the transfer cache station and obtain the data volume of the transfer cache station; Obtaining a preset cache data volume threshold in the database and comparing it with the data volume of the transit cache station; if the data volume of the transit cache station is above the cache data volume threshold, determining to perform initial data compression and obtaining an initial compression result; If the data volume of the transit cache station is less than the cache data volume threshold, initial compression will not be performed and the data will not be uploaded to the cloud.

6. The data processing method for a rotary transmission device based on 5G and edge computing according to claim 1, characterized in that: The processing obtains a compression execution result, and the specific method is: Get the data time span of the transfer cache station; Obtain a preset time span threshold interval in the database and compare it with the data time span of the transit cache station. If the data time span of the transit cache station is greater than the upper limit of the time span threshold interval, correct and compress the initial compression result according to the upper limit of the time span threshold interval to obtain a 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 a compression execution result; Otherwise, no correction compression is performed, and the initial compression result is used as the result of one compression execution.

7. The method for processing data of a rotary transmission device based on 5G and edge computing according to claim 1, wherein: The 5G bandwidth utilization rate of the transfer cache station is obtained and processed to obtain the secondary compression execution result. The specific method is as follows: Obtain the real-time 5G bandwidth utilization of the transit cache station; Based on the 5G bandwidth utilization threshold and compared with the real-time 5G bandwidth utilization of the transit cache station, if the real-time 5G bandwidth utilization is below the 5G bandwidth utilization threshold, the secondary compression correction process is not performed, and the primary compression execution result is marked as the secondary compression execution result; If the real-time 5G bandwidth utilization is greater than the 5G bandwidth utilization threshold, secondary compression correction processing is performed, and the real-time 5G bandwidth utilization and the 5G bandwidth utilization threshold are analyzed to obtain a bandwidth difference coefficient; Obtain each bandwidth difference coefficient interval preset in the database and the secondary compression reference adjustment ratio corresponding to each bandwidth difference coefficient interval, and compare them with the bandwidth difference coefficient. If the bandwidth difference coefficient is within a preset bandwidth difference coefficient interval, obtain the secondary compression reference adjustment ratio corresponding to the interval as the secondary compression adjustment ratio; The secondary compression reference adjustment ratio is adjusted based on the ratio with the primary compression execution result to obtain the secondary compression execution result.

8. The method for processing data of a rotary transmission device based on 5G and edge computing according to claim 2, wherein: The method also includes adjusting the data collection frequency of the rotary transmission device based on the analysis of the change measurement value and fluctuation amplitude value of the rotary transmission data. The specific method is as follows: Obtaining the rate change threshold interval and fluctuation amplitude threshold interval preset in the database; Based on the comparison between the change measurement value of the rotation transmission data and the rate change threshold range, if the change measurement value of the rotation transmission data is within the rate change threshold range, the data acquisition frequency adjustment is not performed; otherwise, a first adjustment coefficient of the sampling frequency is obtained, and a first adjustment result of the data sampling frequency is obtained by analysis; If the measurement value of the change in the rotation transmission 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 measurement value of the change in the rotation transmission 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 between the fluctuation amplitude value of the rotation transmission data and the fluctuation amplitude threshold range, if the fluctuation amplitude value of the rotation transmission data is within the fluctuation amplitude threshold range, the data acquisition frequency adjustment is not performed; otherwise, a second sampling frequency adjustment coefficient is obtained, and the second data sampling frequency adjustment result is obtained by analysis; If the fluctuation amplitude value of the rotation transmission 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 rotation transmission 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; The data acquisition frequency of the rotation transmission device is adjusted based on analysis of the first adjustment result of the data sampling frequency and the second adjustment result of the data sampling frequency.

9. A system using the method for processing data of a rotating transmission device based on 5G and edge computing as described in any one of claims 1 to 8, characterized in that: include: Light-load state node analysis module, target light-load state node matching module, to-be-uploaded data analysis module, initial compression module, modified compression judgment module, and secondary compression execution module; The light-load state node analysis module is used to obtain resource state parameters of each edge node after the detection device receives the data acquisition signal of the rotating transmission device, thereby analyzing and obtaining the light-load state node; The target light-load state node matching module is used to obtain the rotation transmission data rate change parameter within a preset period, analyze and obtain the resource state requirement value of the data acquisition port, and thereby match the target light-load state node for data processing; The data analysis module to be uploaded is used to obtain the fluctuation amplitude parameter of the rotation transmission data within a preset time period, analyze the fluctuation amplitude value of the rotation transmission data, adjust the target light-load state node filtering parameter accordingly, and process 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, perform initial compression judgment based on this, and obtain the initial compression result; The correction and compression judgment module is used to obtain the data time span of the transfer cache station and judge whether to perform correction and compression, thereby obtaining a compression execution result; The secondary compression execution module is used to obtain the 5G bandwidth utilization of the transfer cache station, process the secondary compression execution result, and after performing corresponding compression processing, upload the data to be uploaded of the transfer cache station to the cloud.

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