A method and system for controlling and managing a cluster of intelligent devices based on the Internet of Things

Abstract

The present invention discloses a method and system for controlling and managing a cluster of intelligent devices based on the Internet of Things, and belongs to the technical field of cluster control and management. The method and system classify intelligent devices into functional groups, interconnect the intelligent devices in the functional groups, collect network traffic data and device power consumption data, calculate the average network traffic data and average device power consumption data of the functional groups, calculate the load balancing degree of the functional groups, and add intelligent devices with unbalanced loads to an abnormal device database. The abnormal device data sets within all hours are obtained, and the frequency of high loads of intelligent devices is analyzed. The probability of high loads of intelligent devices in the future is predicted. A high load probability threshold is preset, and the load is analyzed and adjusted. The present invention can accurately identify high-load devices by setting a load balancing degree threshold and a dual deviation judgment, further calculate the high load frequency of the devices and the probability of high loads in the future, and adjust the load in advance, thereby realizing the optimized management of the intelligent device cluster.

Description

Technical Field

[0001] The present invention relates to the technical field of cluster control management, and in particular to a method and system for cluster control management of intelligent devices based on the Internet of Things. Background Art

[0002] With the rapid development of Internet of Things (IoT) technology, the large-scale deployment of smart devices has become an important foundation for promoting industrial automation, smart cities and smart homes. In industrial production lines, smart homes and public facilities, smart devices are interconnected and work together through the network to achieve data collection, status monitoring and remote control. This technology not only improves work efficiency, but also reduces labor costs and energy consumption. However, with the increase in the number of devices, how to efficiently manage and control smart device clusters has become an urgent problem to be solved. Especially on industrial production lines, the network traffic and power consumption of smart devices fluctuate greatly. How to ensure load balancing between devices, improve equipment operation efficiency, and avoid failures caused by equipment overload has become a research hotspot in related fields.

[0003] Existing smart device management methods mainly rely on centralized control systems, which monitor and manage each smart device in real time through a central control unit. However, the traditional centralized management method has several obvious shortcomings. The existing technology usually lacks real-time monitoring and analysis of load differences between devices, resulting in some devices failing prematurely due to overload when the device load is unbalanced, while other devices are in a resource idle state. More importantly, the existing technology lacks an automatic adjustment mechanism for load-unbalanced devices. The system can often only take countermeasures after the device fails, resulting in a decrease in production efficiency. In particular, for the management of network traffic and power consumption, there is a lack of effective real-time monitoring and prediction methods, which easily leads to load imbalance between devices and increases the risk of system failure. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for controlling and managing a cluster of intelligent devices based on the Internet of Things, so as to solve the problems raised in the above-mentioned background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] A method for controlling and managing a cluster of intelligent devices based on the Internet of Things comprises the following steps: storing the operating status of intelligent devices on an industrial production line in a data block, wherein the data block includes a network traffic database and a device power consumption database; classifying the intelligent devices into functional groups; utilizing an Internet of Things platform to interconnect the intelligent devices in the functional groups, and collecting network traffic data and device power consumption data of the intelligent devices; calculating average network traffic data and average device power consumption data of the functional groups; calculating the load balance of the functional groups based on the average network traffic data and average device power consumption data; presetting a load balance threshold, determining and identifying intelligent devices with unbalanced loads, and adding them to an abnormal device database; obtaining abnormal device data sets within all hours, and analyzing the frequency of high loads on intelligent devices; based on the frequency, predicting the probability of high loads on intelligent devices in the future; presetting a high load probability threshold, and analyzing and performing load adjustment.

[0007] As a preferred solution of the method for controlling and managing a cluster of smart devices based on the Internet of Things described in the present invention, data blocks are divided in an information storage unit based on the number of smart devices on an industrial production line, and the operating status of the smart devices is stored in the data blocks, and one data block is divided corresponding to one smart device; the operating status includes network traffic data and device power consumption data; the data block includes a network traffic database and a device power consumption database, and is used to store network traffic data and device power consumption data, respectively.

[0008] The smart devices are classified into functional groups, where the functional groups are used to group smart devices with the same functional type; functional group labels are added to the network traffic database and the device power consumption database in the data block, and one functional group corresponds to one network traffic database and one device power consumption database.

[0009] As a preferred solution of the method for controlling and managing a smart device cluster based on the Internet of Things described in the present invention, the smart devices in the functional group are interconnected using the Internet of Things platform, and the network traffic data and device power consumption data of the smart devices are collected on an hourly basis and stored in the network traffic database and the device power consumption database, respectively.

[0010] Extract the network traffic data and device power consumption data from the network traffic database and device power consumption database at the tth hour, and record them as ND t (n) and PC t (n), where ND t (n) represents the network traffic data of the nth smart device in the functional group at the tth hour, PC t (n) represents the device power consumption data of the nth smart device in the functional group at the tth hour.

[0011] As a preferred solution of the method for controlling and managing a smart device cluster based on the Internet of Things described in the present invention, the average network traffic data and average device power consumption data of the functional group at the tth hour are calculated using the following calculation formula:

[0012]

[0013] Among them, Avg_ND t (n) represents the average network traffic data of the functional group in the tth hour, Avg_PC t (n) represents the average device power consumption data of the functional group at the tth hour, and N represents the total number of smart devices in the functional group.

[0014] Based on average network traffic data Avg_ND t (n) and average device power consumption data Avg_PC t (n), calculate the load balancing degree of the functional group at the tth hour, the calculation formula is as follows:

[0015]

[0016] Wherein, LE(t) represents the load balancing degree of the functional group at the tth hour.

[0017] In the present invention, the load balancing degree LE(t) reflects the consistency of network traffic and power consumption of all devices in the functional group. The formula is calculated by calculating the actual network traffic ND of each device in the tth hour. t (n) and power consumption PC t (n) and its corresponding average value to obtain the load balancing degree; in the formula and It represents the standardized deviation of the network traffic and power consumption of each device relative to its average value. This standardization allows the load differences of different devices to be compared in the same dimension. The degree of load balancing directly affects the operating efficiency of the system. If the calculated value is high, it means that the load differences of devices in the functional group are large, and some devices may be overloaded or underloaded.

[0018] A load balancing threshold ∈ is preset. If LE(t)>∈, the load balancing degree of the functional group at the tth hour is determined to be high, and the intelligent devices with unbalanced loads are identified, as follows:

[0019] Preset load deviation threshold τ, if and The nth smart device is determined to be a high-load device, an abnormal device data set based on the tth hour is constructed, recorded as AD(t), and the nth smart device is added to the abnormal device database.

[0020] In the present invention, by comparing the standardized deviations of the network traffic and power consumption of a single device relative to the average value, it is determined whether the device is a high-load device. The formula sets a threshold value τ. When the deviation of a device exceeds the threshold value in both items (network traffic and power consumption), the device is determined to be high-loaded; ensuring that the high-load state of the device is not caused by a single factor (such as abnormal increase in traffic or power consumption), but by the combined effect of multiple aspects of abnormalities. When the system detects high-load devices, these abnormal devices can be recorded in time and their data can be stored in the abnormal device database. These data are very important for subsequent maintenance and optimization processes.

[0021] As a preferred solution of the method for controlling and managing a cluster of intelligent devices based on the Internet of Things described in the present invention, a data set of abnormal devices in all hours is obtained; based on the data set of abnormal devices in all hours, the occurrence frequency of high-load devices is analyzed, as follows:

[0022] Obtain the abnormal device data set in the past k hours and calculate the frequency of high load on the nth smart device. The calculation formula is as follows:

[0023]

[0024] Among them, F(n,t) represents the frequency of high load of the nth smart device, AD(ti) represents the abnormal device data set of the tith hour, I() represents the indicator function, and b n Indicates the nth smart device.

[0025] In the present invention, the formula is used to calculate the frequency of high load on a certain device in the past k hours. I() is an indicator function used to determine whether the device appears in the abnormal device data set AD(ti) in the past hour ti. When the device is in AD(ti), the value is 1, otherwise it is 0; by accumulating the high load conditions of the past k hours and dividing it by the total number of hours k, the formula obtains the high load frequency F(n,t) of the device; calculating the high load frequency of the device can identify which devices may have long-term load problems, and these devices are more likely to have load anomalies again in the future.

[0026] Based on the frequency F(n,t) of high load on the nth smart device, the probability of high load on the nth smart device in the t+1th hour is predicted. The calculation formula is as follows:

[0027]

[0028] Among them, P(n,t+1) represents the probability of high load on the nth smart device within the t+1th hour, and α, β and γ represent the preset weight coefficients respectively.

[0029] Preset high load probability threshold like It is determined that the nth smart device will have a high load within the t+1th hour, and the smart device with a low probability of high load is arranged in advance for load adjustment.

[0030] In the present invention, the formula combines three factors: historical high load frequency, current network traffic and power consumption, and balances their impact on high load prediction through weight coefficients α, β and γ; the formula calculates the probability of high load on the nth smart device in the next time period (t+1 hour), and the weight coefficients α, β and γ are set according to the actual application scenario to ensure the sensitivity and accuracy of the model to different factors. For example, when the network traffic data has a greater impact on high load prediction, the weight of β can be increased; the formula enables the system to actively warn of potential high load situations, which not only depends on the frequency of high load in the past, but also comprehensively considers the current device status to make the prediction more accurate. When P(n, t+1) exceeds the preset threshold, other devices with lower load can be arranged in advance to take over, thereby avoiding large-scale device overload in the next hour.

[0031] A smart device cluster control and management system based on the Internet of Things, the system comprises: a device data storage module, a device data acquisition module, a device load analysis module and a device load prediction module.

[0032] The device data storage module stores the operating status of the intelligent devices on the industrial production line in a data block, wherein the data block includes a network traffic database and a device power consumption database; and classifies the intelligent devices into functional groups.

[0033] The device data collection module: utilizes the Internet of Things platform to interconnect the smart devices in the functional group and collect network traffic data and device power consumption data of the smart devices.

[0034] The device load analysis module calculates the average network traffic data and average device power consumption data of the functional group; calculates the load balance of the functional group based on the average network traffic data and the average device power consumption data; presets the load balance threshold, determines and identifies the intelligent devices with unbalanced load, and adds them to the abnormal device database.

[0035] The device load prediction module: obtains abnormal device data sets within all hours, analyzes the frequency of high load on smart devices; based on the frequency, predicts the probability of high load on smart devices in the future; presets a high load probability threshold, analyzes and performs load adjustment.

[0036] Furthermore, the device data storage module includes a data block division unit and a function group classification unit.

[0037] The data block division unit: divides the data blocks in the information storage unit based on the number of smart devices on the industrial production line, stores the operating status of the smart devices in the data blocks, and one data block is divided for each smart device; the operating status includes network traffic data and device power consumption data; the data blocks include a network traffic database and a device power consumption database, and are used to store network traffic data and device power consumption data, respectively.

[0038] The functional group classification unit: classifies the smart devices into functional groups, wherein the functional groups are used to group smart devices with the same functional type; adds functional group labels to the network traffic database and the device power consumption database in the data block, and one functional group corresponds to one network traffic database and one device power consumption database.

[0039] Furthermore, the equipment data acquisition module includes a data interconnection unit and a data acquisition unit.

[0040] The data interconnection unit: utilizes the Internet of Things platform to interconnect the smart devices in the functional group, collects the network traffic data and device power consumption data of the smart devices in units of hours, and stores them in the network traffic database and the device power consumption database respectively.

[0041] The data collection unit extracts the network traffic data and the device power consumption data from the network traffic database and the device power consumption database at the tth hour, respectively denoted as ND t (n) and PC t (n), where ND t (n) represents the network traffic data of the nth smart device in the functional group at the tth hour, PC t (n) represents the device power consumption data of the nth smart device in the functional group at the tth hour.

[0042] Furthermore, the equipment load analysis module includes a load balancing calculation unit and a high load identification unit.

[0043] The load balancing calculation unit calculates the average network traffic data Avg_ND of the functional group at the tth hour. t (n) and average device power consumption data Avg_PC t (n); Based on average network traffic data Avg_ND t (n) and average device power consumption data Avg_PC t (n), calculate the load balancing degree LE(t) of the functional group at the tth hour.

[0044] The high load identification unit: presets a load balancing threshold ∈, if LE(t)>∈, determines that the load balancing degree of the functional group at the tth hour is high, and identifies the intelligent device with unbalanced load; constructs an abnormal device data set based on the tth hour, and adds the intelligent device with unbalanced load to the abnormal device database.

[0045] Furthermore, the equipment load prediction module includes a high load frequency analysis unit and a load probability prediction unit.

[0046] The high-load frequency analysis unit obtains a data set of abnormal devices in the past k hours and calculates the frequency F(n, t) of high load on the n-th smart device.

[0047] The load probability prediction unit: based on the frequency F(n,t) of high load on the nth smart device, predicts the probability P(n,t+1) of high load on the nth smart device within the t+1th hour; presets a high load probability threshold like It is determined that the nth smart device will have a high load within the t+1th hour, and the smart device with a low probability of high load is arranged in advance for load adjustment.

[0048] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: in a smart device cluster control and management method and system based on the Internet of Things provided by the present invention, unified management of similar devices is simplified through functional group classification and additional labels, and data processing efficiency is improved; in the data collection process, the operating status of the device is recorded regularly by the Internet of Things platform to ensure the real-time and continuity of the data; by calculating the average network traffic, power consumption and load balancing, the uneven load of the device can be discovered in time, thereby improving the operating efficiency; by setting the load balancing threshold and double deviation judgment, high-load devices can be accurately identified, providing an important basis for equipment maintenance; based on historical data, the system further calculates the high-load frequency of the device, and predicts the probability of future high load in combination with the current status, and performs load adjustment in advance to avoid equipment overload; the present invention realizes the optimized management of the smart device cluster through real-time monitoring, load analysis and prediction of the device status, and improves the stability and operating efficiency of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0050] Figure 1 It is a schematic diagram of the steps of a smart device cluster control and management method based on the Internet of Things of the present invention;

[0051] Figure 2It is a structural schematic diagram of an intelligent device cluster control and management system based on the Internet of Things of the present invention. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0053] See also Figure 1 In the first embodiment of the present invention, a method for controlling and managing a cluster of smart devices based on the Internet of Things is provided, and the method comprises the following steps:

[0054] Step S1: storing the operating status of the intelligent devices on the industrial production line in a data block, wherein the data block includes a network traffic database and a device power consumption database; and classifying the intelligent devices into functional groups.

[0055] Step S11: Based on the number of smart devices on the industrial production line, divide the data blocks in the information storage unit, store the operating status of the smart devices in the data blocks, and one data block is divided for each smart device; the operating status includes network traffic data and device power consumption data; the data block includes a network traffic database and a device power consumption database, and is used to store network traffic data and device power consumption data, respectively.

[0056] Step S12: classify the smart devices into functional groups, where the functional groups are used to group smart devices with the same functional type; attach functional group labels to the network traffic database and the device power consumption database in the data block, and one functional group corresponds to one network traffic database and one device power consumption database.

[0057] Step S2: using the Internet of Things platform to interconnect the smart devices in the functional group, and collect network traffic data and device power consumption data of the smart devices.

[0058] Step S21: using the Internet of Things platform to interconnect the smart devices in the functional group, collecting network traffic data and device power consumption data of the smart devices in units of hours, and storing them in the network traffic database and the device power consumption database respectively.

[0059] Step S22: extract the network traffic data and device power consumption data from the network traffic database and device power consumption database at the tth hour, and record them as ND t (n) and PC t (n), where ND t(n) represents the network traffic data of the nth smart device in the functional group at the tth hour, PC t (n) represents the device power consumption data of the nth smart device in the functional group at the tth hour.

[0060] Step S3: Calculate the average network traffic data and average device power consumption data of the functional group; calculate the load balance of the functional group based on the average network traffic data and the average device power consumption data; preset the load balance threshold, determine and identify the intelligent devices with unbalanced load, and add them to the abnormal device database.

[0061] Step S31: Calculate the average network traffic data and average device power consumption data of the functional group at the tth hour, and the calculation formula is as follows:

[0062]

[0063] Among them, Avg_ND t (n) represents the average network traffic data of the functional group in the tth hour, Avg_PC t (n) represents the average device power consumption data of the functional group at the tth hour, and N represents the total number of smart devices in the functional group.

[0064] For example, assuming that the total number of smart devices N in the functional group is 4, ND 5 (1) = 5, ND 5 (2) = 6, ND 5 (3) = 7, ND 5 (4) = 9, PC 5 (1) = 6, PC 5 (2) = 8, PC 5 (3) = 10, PC 5 (4)=12, substitute it into the formula to get Avg_ND 5 (n) = 6.75, Avg_PC 5 (n)=9.

[0065] Based on average network traffic data Avg_ND t (n) and average device power consumption data Avg_PC t (n), calculate the load balancing degree of the functional group at the tth hour, the calculation formula is as follows:

[0066]

[0067] Wherein, LE(t) represents the load balancing degree of the functional group at the tth hour.

[0068] For example, Avg_ND 5 (n) = 6.75, Avg_PC5 (n) = 9, substitute it into the formula and we get

[0069] Step S32: preset a load balance threshold ε. If LE(t)>ε, it is determined that the load balance of the functional group at the tth hour is high, and the intelligent device with unbalanced load is identified, as follows:

[0070] Step S32.1: Preset load deviation threshold τ, if and The nth smart device is determined to be a high-load device, an abnormal device data set based on the tth hour is constructed, recorded as AD(t), and the nth smart device is added to the abnormal device database.

[0071] For example, assuming that the load balance threshold ε is 0.3 and the load deviation threshold τ is 0.2, then LE(5)>∈, for the first smart device, and is a high-load device; for the second smart device, Not a high load device; for the 3rd smart device, 0.2, but Not a high load device; for the 4th smart device, and is a high-load device; then the first smart device and the fourth smart device are added to the abnormal device data set of the fifth hour.

[0072] Step S4: Obtain abnormal device data sets for all hours, analyze the frequency of high load on smart devices; based on the frequency, predict the probability of high load on smart devices in the future; preset a high load probability threshold, analyze and perform load adjustment.

[0073] Step S41: Repeat the above steps S31 to S32 to obtain the abnormal device data set in all hours; based on the abnormal device data set in all hours, analyze the occurrence frequency of high-load devices, as follows:

[0074] Obtain the abnormal device data set in the past k hours and calculate the frequency of high load on the nth smart device. The calculation formula is as follows:

[0075]

[0076] Among them, F(n,t) represents the frequency of high load of the nth smart device, AD(ti) represents the abnormal device data set of the tith hour, I() represents the indicator function, and b n Indicates the nth smart device.

[0077] For example, assuming k is 5, the first smart device appears in the abnormal device data set in the first hour, the third hour, and the fifth hour respectively.

[0078] Step S42: Based on the frequency F(n,t) of high load on the nth smart device, predict the probability of high load on the nth smart device in the t+1th hour, and the calculation formula is as follows:

[0079]

[0080] Among them, P(n,t+1) represents the probability of high load on the nth smart device within the t+1th hour, and α, β and γ represent the preset weight coefficients respectively.

[0081] Preset high load probability threshold like It is determined that the nth smart device will have a high load within the t+1th hour, and the smart device with a low probability of high load is arranged in advance for load adjustment.

[0082] For example, assuming that α, β, and γ are 0.7, 0.6, and 0.8, the high load probability threshold is 1, and we can get but It is determined that the first smart device will have a high load within the t+1 hour, and the smart devices with a low probability of high load are arranged in advance for load adjustment.

[0083] See also Figure 2 In the second embodiment of the present invention, a smart device cluster control and management system based on the Internet of Things is provided, the system comprising: a device data storage module, a device data acquisition module, a device load analysis module and a device load prediction module.

[0084] The device data storage module stores the operating status of the intelligent devices on the industrial production line in a data block, wherein the data block includes a network traffic database and a device power consumption database; and classifies the intelligent devices into functional groups.

[0085] The device data collection module: utilizes the Internet of Things platform to interconnect the smart devices in the functional group and collect network traffic data and device power consumption data of the smart devices.

[0086] The device load analysis module calculates the average network traffic data and average device power consumption data of the functional group; calculates the load balance of the functional group based on the average network traffic data and the average device power consumption data; presets the load balance threshold, determines and identifies the intelligent devices with unbalanced load, and adds them to the abnormal device database.

[0087] The device load prediction module: obtains abnormal device data sets within all hours, analyzes the frequency of high load on smart devices; based on the frequency, predicts the probability of high load on smart devices in the future; presets a high load probability threshold, analyzes and performs load adjustment.

[0088] Furthermore, the device data storage module includes a data block division unit and a function group classification unit.

[0089] The data block division unit: divides the data blocks in the information storage unit based on the number of smart devices on the industrial production line, stores the operating status of the smart devices in the data blocks, and one data block is divided for each smart device; the operating status includes network traffic data and device power consumption data; the data blocks include a network traffic database and a device power consumption database, and are used to store network traffic data and device power consumption data, respectively.

[0090] The functional group classification unit: classifies the smart devices into functional groups, wherein the functional groups are used to group smart devices with the same functional type; adds functional group labels to the network traffic database and the device power consumption database in the data block, and one functional group corresponds to one network traffic database and one device power consumption database.

[0091] Furthermore, the equipment data acquisition module includes a data interconnection unit and a data acquisition unit.

[0092] The data interconnection unit: utilizes the Internet of Things platform to interconnect the smart devices in the functional group, collects the network traffic data and device power consumption data of the smart devices in units of hours, and stores them in the network traffic database and the device power consumption database respectively.

[0093] The data collection unit extracts the network traffic data and the device power consumption data from the network traffic database and the device power consumption database at the tth hour, respectively denoted as ND t (n) and PC t (n), where ND t (n) represents the network traffic data of the nth smart device in the functional group at the tth hour, PC t (n) represents the device power consumption data of the nth smart device in the functional group at the tth hour.

[0094] Furthermore, the equipment load analysis module includes a load balancing calculation unit and a high load identification unit.

[0095] The load balancing calculation unit calculates the average network traffic data Avg_ND of the functional group at the tth hour. t (n) and average device power consumption data Avg_PC t(n); Based on average network traffic data Avg_ND t (n) and average device power consumption data Avg_PC t (n), calculate the load balancing degree LE(t) of the functional group at the tth hour.

[0096] The high load identification unit: presets a load balancing threshold ∈, if LE(t)>∈, determines that the load balancing degree of the functional group at the tth hour is high, and identifies the intelligent device with unbalanced load; constructs an abnormal device data set based on the tth hour, and adds the intelligent device with unbalanced load to the abnormal device database.

[0097] Furthermore, the equipment load prediction module includes a high load frequency analysis unit and a load probability prediction unit.

[0098] The high-load frequency analysis unit obtains a data set of abnormal devices in the past k hours and calculates the frequency F(n, t) of high load on the n-th smart device.

[0099] The load probability prediction unit: based on the frequency F(n,t) of high load on the nth smart device, predicts the probability P(n,t+1) of high load on the nth smart device within the t+1th hour; presets a high load probability threshold like It is determined that the nth smart device will have a high load within the t+1th hour, and the smart device with a low probability of high load is arranged in advance for load adjustment.

[0100] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0101] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for controlling and managing a cluster of intelligent devices based on the Internet of Things, characterized in that: The method comprises the following steps: Step S1: storing the operating status of the intelligent devices on the industrial production line in a data block, wherein the data block includes a network traffic database and a device power consumption database; and classifying the intelligent devices into functional groups; Step S2: using the Internet of Things platform to interconnect the smart devices in the functional group, and collect network traffic data and device power consumption data of the smart devices; Step S3: Calculate the average network traffic data and average device power consumption data of the functional group; calculate the load balance of the functional group based on the average network traffic data and the average device power consumption data; preset a load balance threshold, determine and identify the intelligent devices with unbalanced loads, and add them to the abnormal device database; Step S4: Obtain abnormal device data sets within all hours, analyze the frequency of high load on smart devices; based on the frequency, predict the probability of high load on smart devices in the future; preset a high load probability threshold, analyze and adjust the load; The specific implementation process of step S2 includes: Step S21: using the Internet of Things platform to interconnect the smart devices in the functional group, collecting network traffic data and device power consumption data of the smart devices in units of hours, and storing them in a network traffic database and a device power consumption database respectively; Step S22: extract the network traffic data and device power consumption data from the network traffic database and device power consumption database at the tth hour, and record them as ND t (n) and PC t (n), where ND t (n) represents the network traffic data of the nth smart device in the functional group at the tth hour, PC t (n) represents the device power consumption data of the nth smart device in the functional group at the tth hour; The specific implementation process of step S3 includes: Step S31: Calculate the average network traffic data and average device power consumption data of the functional group at the tth hour, and the calculation formula is as follows: Among them, Avg_ND t (n) represents the average network traffic data of the functional group in the tth hour, Avg_PC t (n) represents the average device power consumption data of the functional group in the tth hour, and N represents the total number of smart devices in the functional group; Based on average network traffic data Avg_ND t (n) and average device power consumption data Avg_PC t (n), calculate the load balancing degree of the functional group at the tth hour, the calculation formula is as follows: Wherein, LE(t) represents the load balancing degree of the functional group at the tth hour; Step S32: preset a load balance threshold ∈, if LE(t)>∈, then determine that the load balance of the functional group at the tth hour is high, and identify the intelligent device with unbalanced load, as follows: Step S32.1: Preset load deviation threshold τ, if and The nth smart device is determined to be a high-load device, an abnormal device data set based on the tth hour is constructed, recorded as AD(t), and the nth smart device is added to the abnormal device database; The specific implementation process of step S4 includes: Step S41: Repeat the above steps S31 to S32 to obtain the abnormal device data set in all hours; based on the abnormal device data set in all hours, analyze the occurrence frequency of high-load devices, as follows: Obtain the abnormal device data set in the past k hours and calculate the frequency of high load on the nth smart device. The calculation formula is as follows: Among them, F(n,t) represents the frequency of high load of the nth smart device, AD(ti) represents the abnormal device data set of the tith hour, I() represents the indicator function, and b n Indicates the nth smart device; Step S42: Based on the frequency F(n,t) of high load on the nth smart device, predict the probability of high load on the nth smart device in the t+1th hour, and the calculation formula is as follows: Where P(n,t+1) represents the probability of high load on the nth smart device in the t+1th hour, and α, β, and γ represent the preset weight coefficients respectively; Preset high load probability threshold like It is determined that the nth smart device will have a high load within the t+1th hour, and the smart device with a low probability of high load is arranged in advance for load adjustment.

2. According to the method for controlling and managing a cluster of smart devices based on the Internet of Things in claim 1, it is characterized in that: The specific implementation process of step S1 includes: Step S11: based on the number of smart devices on the industrial production line, the information storage unit is divided into data blocks, and the operating status of the smart devices is stored in the data blocks, and one data block is divided corresponding to one smart device; the operating status includes network traffic data and device power consumption data; the data block includes a network traffic database and a device power consumption database, and is used to store the network traffic data and the device power consumption data respectively; Step S12: classify the smart devices into functional groups, where the functional groups are used to group smart devices with the same functional type; attach functional group labels to the network traffic database and the device power consumption database in the data block, and one functional group corresponds to one network traffic database and one device power consumption database.

3. A smart device cluster control and management system based on the Internet of Things, executing a smart device cluster control and management method based on the Internet of Things as claimed in claim 1, characterized in that: The system comprises: an equipment data storage module, an equipment data acquisition module, an equipment load analysis module and an equipment load prediction module; The device data storage module stores the operating status of the intelligent devices on the industrial production line in a data block, wherein the data block includes a network traffic database and a device power consumption database; and classifies the intelligent devices into functional groups; The device data collection module: uses the Internet of Things platform to interconnect the smart devices in the functional group and collect network traffic data and device power consumption data of the smart devices; The device load analysis module: calculates the average network traffic data and average device power consumption data of the functional group; calculates the load balance of the functional group based on the average network traffic data and the average device power consumption data; presets a load balance threshold, determines and identifies the intelligent devices with unbalanced load, and adds them to the abnormal device database; The device load prediction module: obtains abnormal device data sets within all hours, analyzes the frequency of high load on smart devices; based on the frequency, predicts the probability of high load on smart devices in the future; presets a high load probability threshold, analyzes and performs load adjustment.

4. According to claim 3, a smart device cluster control and management system based on the Internet of Things is characterized by: The device data storage module includes a data block division unit and a function group classification unit; The data block division unit: divides the data blocks in the information storage unit based on the number of intelligent devices on the industrial production line, stores the operation status of the intelligent devices in the data blocks, and one data block is divided corresponding to one intelligent device; the operation status includes network traffic data and device power consumption data; the data block includes a network traffic database and a device power consumption database, and is used to store the network traffic data and the device power consumption data respectively; The functional group classification unit: classifies the smart devices into functional groups, wherein the functional groups are used to group smart devices with the same functional type; adds functional group labels to the network traffic database and the device power consumption database in the data block, and one functional group corresponds to one network traffic database and one device power consumption database.

5. According to claim 4, a smart device cluster control and management system based on the Internet of Things is characterized by: The equipment data acquisition module includes a data interconnection unit and a data acquisition unit; The data interconnection unit: uses the Internet of Things platform to interconnect the smart devices in the functional group, collects network traffic data and device power consumption data of the smart devices in units of hours, and stores them in the network traffic database and the device power consumption database respectively; The data collection unit extracts the network traffic data and the device power consumption data from the network traffic database and the device power consumption database at the tth hour, respectively denoted as ND t (n) and PC t (n), where ND t (n) represents the network traffic data of the nth smart device in the functional group at the tth hour, PC t (n) represents the device power consumption data of the nth smart device in the functional group at the tth hour.

6. The smart device cluster control and management system based on the Internet of Things according to claim 5 is characterized by: The equipment load analysis module includes a load balancing calculation unit and a high load identification unit; The load balancing calculation unit calculates the average network traffic data Avg_ND of the functional group at the tth hour. t (n) and average device power consumption data Avg_PC t (n); Based on average network traffic data Avg_ND t (n) and average device power consumption data Avg_PC t (n), calculate the load balancing degree LE(t) of the functional group at the tth hour; The high load identification unit: presets a load balancing threshold ∈, if LE(t)>∈, determines that the load balancing degree of the functional group at the tth hour is high, and identifies the intelligent device with unbalanced load; constructs an abnormal device data set based on the tth hour, and adds the intelligent device with unbalanced load to the abnormal device database.

7. The smart device cluster control and management system based on the Internet of Things according to claim 6 is characterized by: The equipment load prediction module includes a high load frequency analysis unit and a load probability prediction unit; The high load frequency analysis unit: obtains the abnormal device data set in the past k hours, and calculates the frequency F(n,t) of high load on the nth smart device; The load probability prediction unit: based on the frequency F(n,t) of high load on the nth smart device, predicts the probability P(n,t+1) of high load on the nth smart device within the t+1th hour; presets a high load probability threshold like It is determined that the nth smart device will have a high load within the t+1th hour, and the smart device with a low probability of high load is arranged in advance for load adjustment.

Images