CN118382024B - Method and system for collecting energy consumption of textile equipment of 5G network in real time - Google Patents
Method and system for collecting energy consumption of textile equipment of 5G network in real time Download PDFInfo
- Publication number
- CN118382024B CN118382024B CN202410842659.5A CN202410842659A CN118382024B CN 118382024 B CN118382024 B CN 118382024B CN 202410842659 A CN202410842659 A CN 202410842659A CN 118382024 B CN118382024 B CN 118382024B
- Authority
- CN
- China
- Prior art keywords
- adjustment
- equipment
- network
- energy consumption
- gap
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000005265 energy consumption Methods 0.000 title claims abstract description 67
- 238000000034 method Methods 0.000 title claims abstract description 38
- 239000004753 textile Substances 0.000 title claims abstract description 28
- 238000011156 evaluation Methods 0.000 claims abstract description 66
- 230000008859 change Effects 0.000 claims abstract description 39
- 238000007405 data analysis Methods 0.000 claims abstract description 12
- 230000009467 reduction Effects 0.000 claims description 38
- 238000012545 processing Methods 0.000 claims description 15
- 238000012360 testing method Methods 0.000 claims description 10
- 230000008569 process Effects 0.000 claims description 7
- 238000012423 maintenance Methods 0.000 claims description 4
- 238000004458 analytical method Methods 0.000 claims description 3
- 238000012544 monitoring process Methods 0.000 description 10
- 238000003860 storage Methods 0.000 description 7
- 230000003247 decreasing effect Effects 0.000 description 6
- 230000005540 biological transmission Effects 0.000 description 5
- 238000004590 computer program Methods 0.000 description 4
- 230000006870 function Effects 0.000 description 4
- 238000004519 manufacturing process Methods 0.000 description 4
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 238000007726 management method Methods 0.000 description 3
- 230000003044 adaptive effect Effects 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 230000003828 downregulation Effects 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000002035 prolonged effect Effects 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 238000005070 sampling Methods 0.000 description 2
- 239000004065 semiconductor Substances 0.000 description 2
- 238000012549 training Methods 0.000 description 2
- 230000003827 upregulation Effects 0.000 description 2
- 238000012795 verification Methods 0.000 description 2
- 230000002411 adverse Effects 0.000 description 1
- 230000032683 aging Effects 0.000 description 1
- 230000010485 coping Effects 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000003064 k means clustering Methods 0.000 description 1
- 238000013486 operation strategy Methods 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- BULVZWIRKLYCBC-UHFFFAOYSA-N phorate Chemical compound CCOP(=S)(OCC)SCSCC BULVZWIRKLYCBC-UHFFFAOYSA-N 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000008646 thermal stress Effects 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04Q—SELECTING
- H04Q9/00—Arrangements in telecontrol or telemetry systems for selectively calling a substation from a main station, in which substation desired apparatus is selected for applying a control signal thereto or for obtaining measured values therefrom
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/14—Network analysis or design
- H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/16—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L43/00—Arrangements for monitoring or testing data switching networks
- H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
- H04L43/0823—Errors, e.g. transmission errors
- H04L43/0829—Packet loss
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L43/00—Arrangements for monitoring or testing data switching networks
- H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
- H04L43/0852—Delays
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L43/00—Arrangements for monitoring or testing data switching networks
- H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
- H04L43/0852—Delays
- H04L43/087—Jitter
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L43/00—Arrangements for monitoring or testing data switching networks
- H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
- H04L43/0876—Network utilisation, e.g. volume of load or congestion level
- H04L43/0894—Packet rate
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/08—Testing, supervising or monitoring using real traffic
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04Q—SELECTING
- H04Q2209/00—Arrangements in telecontrol or telemetry systems
- H04Q2209/40—Arrangements in telecontrol or telemetry systems using a wireless architecture
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04Q—SELECTING
- H04Q2209/00—Arrangements in telecontrol or telemetry systems
- H04Q2209/80—Arrangements in the sub-station, i.e. sensing device
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Environmental & Geological Engineering (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Databases & Information Systems (AREA)
- Evolutionary Computation (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- General Factory Administration (AREA)
- Testing And Monitoring For Control Systems (AREA)
Abstract
The invention discloses a real-time acquisition method and a real-time acquisition system for energy consumption of textile equipment of a 5G network, which relate to the field of data acquisition and are used for solving the problem of acquisition misjudgment caused by the bandwidth variable of the 5G network, acquiring equipment characteristic parameter information and 5G network characteristic information, establishing a data analysis model, generating an adjustment evaluation coefficient, comparing the adjustment evaluation coefficient with primary and secondary adjustment thresholds to obtain a comparison result, determining a gap adjustment result according to the comparison result, obtaining an adjustment coefficient ratio and an equipment energy consumption change rate according to the gap adjustment result, determining an adjustment deviation result by using fuzzy logic according to the adjustment coefficient ratio and the equipment energy consumption change rate, carrying out fuzzy reasoning according to the fuzzy rule, determining the adjustment deviation to consider the change of the time interval length of real-time acquisition caused by the change of the bandwidth variable of the 5G network, improving the acquisition efficiency, avoiding misjudgment possibility and ensuring the energy consumption acquisition of textile equipment to have self-adaptability.
Description
Technical Field
The invention relates to the field of data acquisition, in particular to a method and a system for acquiring energy consumption of textile equipment of a 5G network in real time.
Background
With the development of intelligent manufacturing, the requirements of the textile industry on real-time monitoring and management of equipment energy consumption are increasingly increased, however, the traditional energy consumption monitoring system has the problems of untimely data acquisition, low transmission efficiency, poor system integration level and the like, so that the high-efficiency management requirements of modern textile production are difficult to meet, the energy consumption of textile equipment in the production process is huge, and how to realize the real-time monitoring and optimization management of the energy consumption becomes a key for improving the enterprise competitiveness and realizing green production; the 5G network provides new technical support for energy consumption monitoring by virtue of high bandwidth, low delay and large-scale connection, and particularly, the energy consumption monitoring method of the 5G network can effectively solve a plurality of problems caused by the limitation of transmission speed and data processing capacity in the traditional method;
the prior art has the following defects:
at present, although the energy consumption monitoring method of the 5G network can solve the problem that the requirements of real-time performance and accuracy are met due to the limitation of transmission speed and data processing capacity, the time interval length for real-time acquisition is not changed according to the change of the bandwidth variable of the 5G network, so that the possibility of misjudgment of acquired data is increased, and misjudgment is caused. Therefore, a method and a system for collecting energy consumption of textile equipment in a 5G network in real time are provided.
The above information disclosed in the background section is only for enhancement of understanding of the background of the disclosure and therefore it may include information that does not form the prior art that is already known to a person of ordinary skill in the art.
Disclosure of Invention
In order to overcome the above-mentioned drawbacks of the prior art, embodiments of the present invention provide a method and a system for collecting energy consumption of a textile device in a 5G network in real time, which solve the problems set forth in the above-mentioned background art by applying different product inspection methods.
The invention provides a method for acquiring the energy consumption of textile equipment of a 5G network in real time, which comprises the following steps of S1, acquiring equipment characteristic parameter information and 5G network characteristic information, and obtaining equipment instantaneous power, equipment operating frequency and equipment ambient temperature difference through data processing; network instantaneous bandwidth, network instantaneous delay, network instantaneous jitter, and network packet loss rate;
s2: acquiring equipment characteristic parameter information and 5G network characteristic information, establishing a data analysis model, and generating an adjustment evaluation coefficient;
S3: obtaining an adjustment evaluation coefficient, comparing and analyzing the adjustment evaluation coefficient with a primary adjustment threshold value and a secondary adjustment threshold value to obtain a comparison result, and determining a gap adjustment result according to the comparison result;
s4: obtaining a gap adjustment result to obtain an adjustment coefficient ratio and an equipment energy consumption change rate;
S5, determining an adjustment deviation result by using fuzzy logic according to the adjustment coefficient ratio and the equipment energy consumption change rate;
S6: and carrying out fuzzy reasoning according to the fuzzy rule, and determining the adjustment deviation.
In a preferred embodiment, the device characteristic parameter information includes device instantaneous power, device operating frequency, and device ambient temperature difference, and the device instantaneous power is derived based on current and voltage data acquired in real timeT represents the acquired time stamps, t=1, 2,3, 4, … … M, M being the total number of time stamps;
By installing an operation state sensor for detecting the operation state (operation or standby) of the equipment, recording the operation time and standby time of the equipment, and collecting the ratio of the current equipment operation time to the total standby time to obtain the equipment operation frequency ；
By installing temperature sensors at the motor port of the textile equipment and the surrounding environment of the equipment, acquiring the operation temperature of the equipment and the surrounding environment temperature of the equipment under the timestamp consistent with the instantaneous power acquisition of the equipment to obtain the surrounding temperature difference of the equipment。
In a preferred embodiment, the 5G network characteristic information includes network instantaneous bandwidth, network instantaneous delay, network instantaneous jitter, and network packet loss rate;
Obtaining the instantaneous bandwidth, instantaneous delay, instantaneous jitter and packet loss rate of network through a series of network diagnosis tools, bandwidth test tools and the variation between continuous data packets, and calculating the network state index according to the instantaneous bandwidth, the instantaneous delay, the instantaneous jitter and the packet loss rate 。
In a preferred embodiment, the instantaneous power of the device, the operating frequency of the device, the temperature difference around the device and the network status indicator are obtained to generate an adjustment evaluation coefficientThe formula according to is:
In the method, in the process of the invention, It is the adjustment of the evaluation coefficient,、、AndRespectively the instantaneous power of the devicesFrequency of operation of the deviceAmbient temperature difference of the deviceNetwork status indicatorsIs a preset proportionality coefficient of (1), and、、AndAre all greater than 0.
In a preferred embodiment, the adjustment evaluation coefficients are obtainedThen, comparing the adjustment evaluation coefficient with a first-level and second-level adjustment threshold value which is iterated continuously for analysis;
if the adjustment evaluation coefficient is greater than or equal to the first-level adjustment threshold, marking the current time gap as a reduction gap, and generating a reduction signal;
if the adjustment evaluation coefficient is smaller than the second-level adjustment threshold, marking the current time gap as an increased gap, and generating an increased gap signal;
If the adjustment evaluation coefficient is smaller than the primary adjustment threshold and greater than or equal to the secondary adjustment threshold, marking the current time gap as a maintenance gap, and generating an end signal.
In a preferred embodiment, a corresponding operation method is adopted according to the generated signals, specifically, the adjustment evaluation coefficient results generated by counting the first t time stamps are calibrated to obtain a plurality of increase signals, n is the number of the increase signals, n=1, 2,3, 4, … … Q, Q is the total number of the increase signals, a threshold is set according to the number of the increase signals, and a specific reduction amount is evaluated;
specifically, a reduction threshold evaluation is set;
If the number of the increased signals is larger than or equal to the reduction threshold, setting the increased signals as a first-stage reduction amount, and carrying out first-stage reduction operation on the time gap;
if the number of the increased signals is smaller than the reduction threshold, setting the increased signals as a secondary reduction amount, and performing a secondary reduction operation on the time gap;
Specifically, when the gap reduction signal is generated, the total number of the increase signals counted in the first t time stamps is cleared, and the number of the increase signals is counted again.
In a preferred embodiment, the adjustment coefficient ratio is the ratio of the adjustment evaluation coefficient of the next time stamping device to the adjustment evaluation coefficient of the previous time stamping device by the ratio thereofObtaining the adjustment coefficient ratio；
The change rate of the equipment energy consumption is the ratio of the equipment energy consumption data acquired by the next time stamp to the equipment energy consumption data acquired by the last time stamp。
In a preferred embodiment, the adjustment coefficient ratio and the device energy consumption change rate are defined as input variables, which are respectively divided into different fuzzy sets;
Defining an adjustment deviation result as an output variable, and dividing the adjustment deviation result into fuzzy sets;
Formulating a fuzzy rule, and describing the influence of the adjustment coefficient ratio and the equipment energy consumption change rate on the adjustment deviation result;
and carrying out fuzzy reasoning according to the fuzzy rule, and determining the adjustment deviation.
The system for acquiring the energy consumption of the textile equipment in the 5G network in real time comprises an acquisition module, a processing module, an adjusting module, a fuzzy deviation module and an output window;
The acquisition module is used for acquiring the equipment characteristic parameter information and the 5G network characteristic information and sending the equipment characteristic parameter information and the 5G network characteristic information to the processing module;
The processing module is used for acquiring the equipment characteristic parameter information and the 5G network characteristic information to establish a data analysis model, generating an adjustment evaluation coefficient and sending the adjustment evaluation coefficient to the adjustment module;
the adjusting module is used for acquiring an adjusting evaluation coefficient, comparing and analyzing the adjusting evaluation coefficient with a primary adjusting threshold value and a secondary adjusting threshold value to obtain a comparison result, determining a gap adjusting result according to the comparison result and sending the gap adjusting result to the fuzzy deviation module;
the fuzzy deviation module is used for obtaining a gap adjustment result, obtaining an adjustment coefficient ratio and an equipment energy consumption change rate, determining an adjustment deviation result by using fuzzy logic, and sending the adjustment deviation result to an output window;
the output window is used for receiving the deviation adjustment result and reporting the deviation adjustment result.
The invention has the technical effects and advantages that:
1. according to the invention, a data analysis model is established through the equipment characteristic parameter information and the 5G network characteristic information, an adjustment evaluation coefficient is generated and compared with the primary adjustment threshold value and the secondary adjustment threshold value, an increased gap result, a decreased gap result and a maintained gap result are obtained, the decreased gap amount is evaluated according to the number of the increased gap results, the equipment acquisition efficiency is improved, the acquisition time gap is reduced, the possibility of erroneous judgment of acquired data is reduced, the erroneous judgment rate is reduced, and the service life of the equipment is prolonged.
2. According to the invention, a set of fuzzy rules are formulated through the adjustment coefficient ratio and the equipment energy consumption change rate to carry out fuzzy reasoning, an adjustment deviation result is determined, and after the adjustment deviation is determined, the adjustment deviation is subjected to down-regulation or up-regulation operation, so that the change of the time interval length of real-time acquisition caused by the change of the bandwidth variable of the 5G network is considered, the acquisition efficiency is improved, the possibility of misjudgment is avoided, and the energy consumption acquisition of the textile equipment is more adaptive.
Drawings
Fig. 1 is a flow chart of a method for collecting energy consumption of a textile device in real time in a 5G network.
Fig. 2 is a schematic block diagram of a system for collecting energy consumption of a 5G network textile device in real time according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Referring to fig. 1, a method for collecting energy consumption of a 5G network textile device in real time specifically includes the following operation procedures:
S1, acquiring equipment characteristic parameter information and 5G network characteristic information, and obtaining equipment instantaneous power, equipment operating frequency and equipment ambient temperature difference through data processing; network instantaneous bandwidth, network instantaneous delay, network instantaneous jitter, and network packet loss rate;
the equipment characteristic parameter information comprises equipment instantaneous power, equipment operating frequency and equipment ambient temperature difference;
the logic for obtaining the instantaneous power of the equipment is that a current sensor and a voltage sensor are arranged on a power line of the textile equipment, the sensors are responsible for measuring the current and the voltage of the equipment, the two parameters are the basis for calculating the instantaneous power, the sampling frequency of the sensor, namely the number of data points collected per second, is set again, the higher sampling frequency can improve the accuracy of the data, and the instantaneous power of the equipment Based on current and voltage data acquired in real time, ac is generally used in industrial applications according to textile equipment, according to the formula:
Wherein, Is the value of the instantaneous voltage and,Is an instantaneous current value, and,Is the phase difference between the voltage and the current,The power factor is the ratio of effective power to apparent power, in practical application, the phase difference between voltage and current can be measured by a sensor or by a power factor meter, t represents the acquired time stamp, t=1, 2,3,4, … … M, and M is the total number of time stamps;
When the instantaneous power of the device is larger, a large amount of electric energy is consumed in a short time, so that the whole energy is increased, the data volume acquired and transmitted in real time is increased, and a smaller time interval is needed for acquisition, so that the real-time property and accuracy of the data are ensured;
the logic for acquiring the running frequency of the equipment is to install a running state sensor for detecting the working state (running or standby) of the equipment, record the running time and standby time of the equipment, acquire the ratio of the running time of the current equipment to the total standby time, and calculate the ratio by the following formula:
Wherein, Is the frequency of operation of the device,Is the length of time the device is operated,Is the standby time of the equipment;
It should be noted that, when the operating frequency of the device is higher, the higher the utilization rate of the device is, the higher the aging degree of the device is, and the smaller time interval is needed for acquisition so as to ensure the real-time property and accuracy of the data;
the acquisition logic of the temperature difference around the equipment is to install temperature sensors at the motor port of the textile equipment and the surrounding environment of the equipment, and to monitor the running temperature of the equipment and the surrounding environment temperature of the equipment under the timestamp consistent with instantaneous power acquisition, the temperature with highest running efficiency of the monitoring equipment at specific temperature is calibrated to be the optimal running temperature value, the temperature when the equipment runs and the surrounding environment temperature of the equipment are mutually offset, the equipment temperature offset is obtained by subtracting the absolute value of the running temperature of the equipment from the surrounding environment temperature of the equipment, and the equipment temperature offset is subtracted from the optimal running temperature value to obtain the surrounding temperature difference of the equipment ；
When the temperature difference around the equipment is larger, the operation efficiency of the current equipment is reduced, the thermal stress is increased, deformation and damage of structural parts of the equipment are easy to occur, the maintenance cost and the downtime of the equipment are increased, and a smaller time interval is needed for acquisition so as to ensure the real-time performance and the accuracy of data;
The 5G network characteristic information comprises network instantaneous bandwidth, network instantaneous delay, network instantaneous jitter and network packet loss rate;
The acquisition logic for instantaneous network bandwidth, instantaneous network delay, instantaneous network jitter, and network packet loss rate is implemented using bandwidth testing tools (e.g.) 、Etc.) testing in the time stamp corresponding to the instantaneous power of the device, obtaining the bandwidth by the ratio of the transmission data quantity to the transmission time, summarizing the bandwidth test results of a plurality of time points, and obtaining the instantaneous bandwidth；
Sending ICMP echo requests to the target IP address by using a Ping command, recording the sending and receiving time of the requests, calculating the round trip time of each ICMP echo request, carrying out multiple Ping test results and taking an average value to obtain instantaneous delay；
The instantaneous jitter refers to the variation of delay between continuous data packets, a plurality of data packets are continuously transmitted by using a ping command or other network monitoring tools, the delay difference of each pair of continuous data packets is calculated, the absolute value is taken, the calculated result of multiple jitter is averaged, and the instantaneous jitter is obtained；
The packet loss rate refers to the proportion of lost data packets to total transmitted data packets in a specific time period, a group of data packets are transmitted by using a ping command or other network monitoring tools, the number of transmitted data packets and the number of received data packets are recorded, the number of lost data packets is calculated, and the average value of multiple packet loss test results is taken to obtain the instantaneous packet loss rate；
The ping command is a network diagnostic tool for testing connectivity of a host to a target IP address and measuring Round Trip Time (RTT), and the ping command is used for determining response speed and stability of a network by sending ICMP (Internet Control Message Protocol) a echo request and waiting for the target host to send an echo response, and the network detection tool used for the ping command is not limited, but is set to be consistent with the network detection tool according to specific implementation conditions;
Calculating network state indexes through the network instantaneous bandwidth, the network instantaneous delay, the network instantaneous jitter and the network packet loss rate :
When the network state index value is larger, the network performance is worse, and the real-time acquisition of the energy consumption data of the textile equipment is possibly negatively influenced, the interval of the acquisition time interval is required to be increased so as to ensure the accuracy of the data;
s2: acquiring equipment characteristic parameter information and 5G network characteristic information, establishing a data analysis model, and generating an adjustment evaluation coefficient;
acquiring device instantaneous power Frequency of operation of the deviceAmbient temperature difference of the deviceNetwork status indicatorsGenerating adjustment evaluation coefficientsThe formula according to is:
In the method, in the process of the invention, It is the adjustment of the evaluation coefficient,、、AndRespectively the instantaneous power of the devicesFrequency of operation of the deviceAmbient temperature difference of the deviceNetwork status indicatorsIs a preset proportionality coefficient of (1), and、、AndAre all greater than 0;
Wherein the device instantaneous power Frequency of operation of the deviceAmbient temperature difference of the deviceNetwork status indicatorsThe method is a data embodiment for directly expressing whether the time gap needs to be adjusted or not;
the formula shows that if the instantaneous power of the equipment, the operating frequency of the equipment and the temperature difference around the equipment are larger, the adjusting and evaluating coefficient is larger, the time gap is required to be reduced, otherwise, if the network state index is larger, the adjusting and evaluating coefficient is smaller, the time gap is required to be increased;
S3: obtaining an adjustment evaluation coefficient, comparing and analyzing the adjustment evaluation coefficient with a primary adjustment threshold value and a secondary adjustment threshold value to obtain a comparison result, and determining a gap adjustment result according to the comparison result;
The acquisition logic of the primary and secondary adjustment thresholds is to adjust the distribution sample set of the database by collecting the historical time interval, divide the data set into a training set and a test set, set an evaluation index and a clustering algorithm, train a model on the training set and evaluate the performance of the model on the test set in each iteration of cross verification, and then control the adjustment thresholds according to the performance of the verification set, so that the primary and secondary adjustment thresholds are constantly updated in an iteration manner;
In the invention, the clustering algorithm is an unsupervised learning algorithm and is used for dividing the time gap adjustment result into groups or clusters with similarity; the common K-means clustering is that a table in a data set is divided into K clusters, so that the distance between each gap length and the center point (centroid) of the cluster to which the gap length belongs is minimized, and finally the adjustment degree between gaps is measured through Euclidean distance, so that a primary adjustment threshold value and a secondary adjustment threshold value are set;
obtaining adjustment evaluation coefficients Then, comparing the adjustment evaluation coefficient with a first-level and second-level adjustment threshold value which is iterated continuously for analysis;
if the adjustment evaluation coefficient is greater than or equal to the first-level adjustment threshold, marking the current time gap as a reduction gap, and generating a reduction signal;
if the adjustment evaluation coefficient is smaller than the second-level adjustment threshold, marking the current time gap as an increased gap, and generating an increased gap signal;
If the adjustment evaluation coefficient is smaller than the primary adjustment threshold and larger than or equal to the secondary adjustment threshold, marking the current time gap as a maintenance gap, and generating an end signal;
When the gap increasing signal is generated, the gap value is increased, and it can be understood that when the collecting time gap is increased, the current state of the equipment is good, the collecting time gap can be increased to save resources, and the specific value of the gap can be judged according to the history gap increasing result obtained by the experimenter;
when the gap reducing signal is generated, the time gap is immediately reduced, and it can be understood that if the gap needs to be reduced, the equipment may have higher energy consumption or safety problems, the acquired time gap needs to be reduced, the energy consumption state of the equipment is timely fed back, and the like, and the specific reduction is evaluated according to the results of all time stamps before the operation period, and the specific operation is as follows:
specifically, the adjustment evaluation coefficient results generated by counting the first t time stamps are calibrated to obtain a plurality of increase signals, n is the number of the increase signals, n=1, 2, 3, 4 and … … Q, Q is the total number of the increase signals, a threshold is set according to the number of the increase signals, and a specific reduction amount is evaluated;
Specifically, setting a reduction threshold value to evaluate, wherein the reduction threshold value is obtained by continuously iterating historical time gap adjustment results;
If the number of the increased signals is larger than or equal to the reduction threshold, setting the increased signals as a first-stage reduction amount, and carrying out first-stage reduction operation on the time gap;
if the number of the increased signals is smaller than the reduction threshold, setting the increased signals as a secondary reduction amount, and performing a secondary reduction operation on the time gap;
Specifically, when a gap reduction signal is generated, the total number of the increment signals counted in the first t time stamps is cleared, and the number of the increment signals is counted again;
Specifically, the first-level and second-level reduction operations are implemented by the experimenter according to specific equipment states, and are not limited herein;
For example, the first 5 time-stamped adjustment evaluation coefficient results are calibrated to A, B, C, D and E, as shown in Table 1 below:
the corresponding primary and secondary reductions are as shown in table 2 below:
as can be seen from the table, the above table is a coping operation method in which the comparison result is a reduced time gap, and the reduction amount is estimated according to the increased signal quantity, so that the conditions of untimely acquisition or misjudgment of the return information are reduced;
According to the invention, a data analysis model is established through the equipment characteristic parameter information and the 5G network characteristic information, an adjustment evaluation coefficient is generated and compared with the primary adjustment threshold value and the secondary adjustment threshold value, an increased gap result, a decreased gap result and a maintained gap result are obtained, the decreased gap amount is evaluated according to the number of the increased gap results, the equipment acquisition efficiency is improved, the acquisition time gap is reduced, the possibility of erroneous judgment of acquired data is reduced, the erroneous judgment rate is reduced, and the service life of the equipment is prolonged.
Example 2
In the embodiment 1 of the invention, a data analysis model is established through equipment characteristic parameter information and 5G network characteristic information, adjustment evaluation coefficients are generated and compared with primary and secondary adjustment thresholds, an increased gap result, a decreased gap result and a maintained gap result are obtained, and an operation strategy for decreasing the gap amount is evaluated according to the number of the increased gap results; however, in embodiment 1, the decrease in the gap amount is judged only from the increase in the number of gap results, and it is obvious that for a device operated for a long time, if the decrease in the early evaluation is not evaluated for the feasibility, there may be an increase in the subsequent judgment, resulting in an increase in the subsequent decrease, which may adversely affect the accuracy of the subsequent judgment; in view of the above, embodiment 2 of the present invention is further refined;
s4: obtaining a gap adjustment result to obtain an adjustment coefficient ratio and an equipment energy consumption change rate;
Wherein the adjustment coefficient ratio refers to the ratio of the adjustment evaluation coefficient of the next time stamping device to the adjustment evaluation coefficient of the previous time stamping device; the equipment energy consumption change rate refers to the ratio of the equipment energy consumption data acquired by the next time stamp to the equipment energy consumption data acquired by the last time stamp;
the logic for obtaining the ratio of the adjustment evaluation coefficient of the next time stamping device to the adjustment evaluation coefficient of the previous time stamping device is to build a data analysis model at the time of the next time stamping to obtain the adjustment evaluation coefficient of the next time stamping And the last time stamp's adjustment evaluation coefficientAnd ratio thereofObtaining the adjustment coefficient ratio; Wherein z is the ratio number of times, z=1, 2,3,4, … … P, P is the ratio total;
The acquisition logic of the ratio of the equipment energy consumption data acquired by the next time stamp to the equipment energy consumption data acquired by the previous time stamp is that the equipment energy consumption data acquired by the next time stamp and the equipment energy consumption data acquired by the previous time stamp are respectively acquired by monitoring and recording the equipment energy consumption data in real time through a sensor arranged on the equipment, and the ratio is calculated to obtain the equipment energy consumption change rate ；
The method for obtaining the adjustment evaluation coefficient and the device energy consumption data is described in embodiment 1, and is not described here again;
S5, determining an adjustment deviation result by using fuzzy logic according to the adjustment coefficient ratio and the equipment energy consumption change rate;
For example, "high", "low", "medium" for adjustment coefficient ratio, "significant change", "medium change", "slight change" for device energy consumption rate of change;
A set of fuzzy rules is formulated to describe the influence of different input variables on the output variables. The definition of rules may be based on expertise or may be obtained through data analysis and experimentation. For example:
Marking the adjustment coefficient ratio as X, the equipment energy consumption change rate as U, and the adjustment deviation result as D_result;
Then it is possible to define:
Rule 1 if (X is high) and (U is a significant change) then (D_result deviates significantly)
Rule 2 if (X is low) and (U is a small change) then (D_result small deviation)
...
S6: performing fuzzy reasoning according to the fuzzy rule, and determining adjustment deviation;
it should be noted that, the division of the fuzzy sets may be adjusted according to the actual situation, for example, although the embodiment uses three fuzzy sets as examples, the adjustment coefficient ratio and the device energy consumption change rate may be actually divided into more than three sets, so as to facilitate better accurate adjustment according to different situations.
Further, for the judgment of the high, middle and low of the adjustment coefficient ratio and the device energy consumption change rate, the threshold value can be set according to the actual situation to judge, for example, when the adjustment coefficient ratio exceeds 70%, the adjustment coefficient ratio is marked as "high", when the device energy consumption change rate is higher than 65%, the adjustment coefficient ratio is marked as "significant change", when the adjustment deviation result is "substantial deviation", the adjustment coefficient ratio is adjusted by 25%, and the like, which will not be described herein.
According to the invention, a set of fuzzy rules are formulated through the adjustment coefficient ratio and the equipment energy consumption change rate to carry out fuzzy reasoning, an adjustment deviation result is determined, and after the adjustment deviation is determined, the adjustment deviation is subjected to down-regulation or up-regulation operation, so that the change of the time interval length of real-time acquisition caused by the change of the bandwidth variable of the 5G network is considered, the acquisition efficiency is improved, the possibility of misjudgment is avoided, and the energy consumption acquisition of the textile equipment is more adaptive.
Example 3
Referring to fig. 2, a real-time collection system for energy consumption of a 5G network textile device includes a collection module, a processing module, an adjustment module, a fuzzy deviation module, and an output window;
The acquisition module is used for acquiring the equipment characteristic parameter information and the 5G network characteristic information and sending the equipment characteristic parameter information and the 5G network characteristic information to the processing module;
The processing module is used for acquiring the equipment characteristic parameter information and the 5G network characteristic information to establish a data analysis model, generating an adjustment evaluation coefficient and sending the adjustment evaluation coefficient to the adjustment module;
the adjusting module is used for acquiring an adjusting evaluation coefficient, comparing and analyzing the adjusting evaluation coefficient with a primary adjusting threshold value and a secondary adjusting threshold value to obtain a comparison result, determining a gap adjusting result according to the comparison result and sending the gap adjusting result to the fuzzy deviation module;
the fuzzy deviation module is used for obtaining a gap adjustment result, obtaining an adjustment coefficient ratio and an equipment energy consumption change rate, determining an adjustment deviation result by using fuzzy logic, and sending the adjustment deviation result to an output window;
the output window is used for receiving the deviation adjustment result and reporting the deviation adjustment result.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with embodiments of the present application are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center by wired or wireless means (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more sets of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (3)
1. A method for collecting energy consumption of textile equipment in a 5G network in real time is characterized by comprising the following steps:
S1, acquiring equipment characteristic parameter information and 5G network characteristic information, and obtaining equipment instantaneous power, equipment operating frequency and equipment ambient temperature difference through data processing; network instantaneous bandwidth, network instantaneous delay, network instantaneous jitter, and network packet loss rate;
s2: acquiring equipment characteristic parameter information and 5G network characteristic information, establishing a data analysis model, and generating an adjustment evaluation coefficient;
S3: obtaining an adjustment evaluation coefficient, comparing and analyzing the adjustment evaluation coefficient with a primary adjustment threshold value and a secondary adjustment threshold value to obtain a comparison result, and determining a gap adjustment result according to the comparison result;
s4: obtaining a gap adjustment result to obtain an adjustment coefficient ratio and an equipment energy consumption change rate;
S5, determining an adjustment deviation result by using fuzzy logic according to the adjustment coefficient ratio and the equipment energy consumption change rate;
S6: performing fuzzy reasoning according to the fuzzy rule, and determining adjustment deviation;
The equipment characteristic parameter information comprises equipment instantaneous power, equipment operating frequency and equipment ambient temperature difference, and the equipment instantaneous power is obtained based on current and voltage data acquired in real time T represents the acquired time stamps, t=1, 2,3, 4, … … M, M being the total number of time stamps;
the running state sensor is arranged for detecting the working state of the equipment, the running time and the standby time of the equipment are recorded, and the ratio of the running time of the current equipment to the total standby time is acquired to obtain the running frequency of the equipment ；
By installing temperature sensors at the motor port of the textile equipment and the surrounding environment of the equipment, acquiring the operation temperature of the equipment and the surrounding environment temperature of the equipment under the timestamp consistent with the instantaneous power acquisition of the equipment to obtain the surrounding temperature difference of the equipment；
The 5G network characteristic information comprises a network instantaneous bandwidth, a network instantaneous delay, a network instantaneous jitter and a network packet loss rate;
Obtaining instantaneous bandwidth of network through a series of network diagnostic tools, bandwidth test tools and variation among continuous data packets Network instantaneous delayNetwork instantaneous jitterNetwork packet loss rateAnd calculates the network state index according to the formulaThe specific formula is expressed as follows:
；
acquiring instantaneous power of the equipment, operating frequency of the equipment, ambient temperature difference of the equipment and network state index, and generating adjustment evaluation coefficient The formula according to is:
；
In the method, in the process of the invention, It is the adjustment of the evaluation coefficient,、、AndRespectively the instantaneous power of the devicesFrequency of operation of the deviceAmbient temperature difference of the deviceNetwork status indicatorsIs a preset proportionality coefficient of (1), and、、AndAre all greater than 0;
obtaining adjustment evaluation coefficients Then, comparing the adjustment evaluation coefficient with a first-level and second-level adjustment threshold value which is iterated continuously for analysis;
if the adjustment evaluation coefficient is greater than or equal to the first-level adjustment threshold, marking the current time gap as a reduction gap, and generating a reduction signal;
if the adjustment evaluation coefficient is smaller than the second-level adjustment threshold, marking the current time gap as an increased gap, and generating an increased gap signal;
If the adjustment evaluation coefficient is smaller than the primary adjustment threshold and larger than or equal to the secondary adjustment threshold, marking the current time gap as a maintenance gap, and generating an end signal;
The adjustment coefficient ratio is the ratio of the adjustment evaluation coefficient of the next time stamping device to the adjustment evaluation coefficient of the previous time stamping device by the ratio thereof Obtaining the adjustment coefficient ratio；
The change rate of the equipment energy consumption is the ratio of the equipment energy consumption data acquired by the next time stamp to the equipment energy consumption data acquired by the last time stamp；
Defining an adjustment coefficient ratio and a device energy consumption change rate as input variables, and dividing the input variables into different fuzzy sets respectively;
Defining an adjustment deviation result as an output variable, and dividing the adjustment deviation result into fuzzy sets;
Formulating a fuzzy rule, and describing the influence of the adjustment coefficient ratio and the equipment energy consumption change rate on the adjustment deviation result;
and carrying out fuzzy reasoning according to the fuzzy rule, and determining the adjustment deviation.
2. The method for collecting energy consumption of textile equipment in a 5G network in real time according to claim 1, wherein the method comprises the following steps: according to the generated signals, adopting a corresponding operation method, specifically, counting the adjustment evaluation coefficient results generated by the first t time stamps, obtaining a plurality of increase signals, calibrating the increase signals as n, wherein n is the number of the increase signals, n=1, 2,3, 4 and … … Q, Q is the total number of the increase signals, setting a threshold according to the number of the increase signals, and evaluating the specific reduction amount;
specifically, a reduction threshold evaluation is set;
If the number of the increased signals is larger than or equal to the reduction threshold, setting the increased signals as a first-stage reduction amount, and carrying out first-stage reduction operation on the time gap;
if the number of the increased signals is smaller than the reduction threshold, setting the increased signals as a secondary reduction amount, and performing a secondary reduction operation on the time gap;
Specifically, when the gap reduction signal is generated, the total number of the increase signals counted in the first t time stamps is cleared, and the number of the increase signals is counted again.
3. A 5G network textile equipment energy consumption real-time acquisition system, configured to implement a 5G network textile equipment energy consumption real-time acquisition method according to any one of claims 1-2, wherein: the device comprises an acquisition module, a processing module, an adjustment module, a fuzzy deviation module and an output window;
The acquisition module is used for acquiring the equipment characteristic parameter information and the 5G network characteristic information and sending the equipment characteristic parameter information and the 5G network characteristic information to the processing module;
The processing module is used for acquiring the equipment characteristic parameter information and the 5G network characteristic information to establish a data analysis model, generating an adjustment evaluation coefficient and sending the adjustment evaluation coefficient to the adjustment module;
the adjusting module is used for acquiring an adjusting evaluation coefficient, comparing and analyzing the adjusting evaluation coefficient with a primary adjusting threshold value and a secondary adjusting threshold value to obtain a comparison result, determining a gap adjusting result according to the comparison result and sending the gap adjusting result to the fuzzy deviation module;
the fuzzy deviation module is used for obtaining a gap adjustment result, obtaining an adjustment coefficient ratio and an equipment energy consumption change rate, determining an adjustment deviation result by using fuzzy logic, and sending the adjustment deviation result to an output window;
the output window is used for receiving the deviation adjustment result and reporting the deviation adjustment result.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410842659.5A CN118382024B (en) | 2024-06-27 | 2024-06-27 | Method and system for collecting energy consumption of textile equipment of 5G network in real time |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410842659.5A CN118382024B (en) | 2024-06-27 | 2024-06-27 | Method and system for collecting energy consumption of textile equipment of 5G network in real time |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN118382024A CN118382024A (en) | 2024-07-23 |
| CN118382024B true CN118382024B (en) | 2024-10-18 |
Family
ID=91906143
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410842659.5A Active CN118382024B (en) | 2024-06-27 | 2024-06-27 | Method and system for collecting energy consumption of textile equipment of 5G network in real time |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN118382024B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN119002415B (en) * | 2024-08-05 | 2025-02-18 | 上海芯港信息科技有限责任公司 | Factory energy consumption management system and method based on big data |
| CN119358986B (en) * | 2024-12-26 | 2025-03-25 | 深圳市瑞意博医疗设备有限公司 | An intelligent management platform and management method for consumables based on the Internet of Things |
| CN119669234B (en) * | 2025-02-11 | 2025-07-22 | 东北大学秦皇岛分校 | Big data storage and backup management system based on cloud service |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102460981A (en) * | 2009-05-07 | 2012-05-16 | 瑞典爱立信有限公司 | Managing A Power Consumption Of A Mobile Communication Device |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6307868B1 (en) * | 1995-08-25 | 2001-10-23 | Terayon Communication Systems, Inc. | Apparatus and method for SCDMA digital data transmission using orthogonal codes and a head end modem with no tracking loops |
-
2024
- 2024-06-27 CN CN202410842659.5A patent/CN118382024B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102460981A (en) * | 2009-05-07 | 2012-05-16 | 瑞典爱立信有限公司 | Managing A Power Consumption Of A Mobile Communication Device |
Also Published As
| Publication number | Publication date |
|---|---|
| CN118382024A (en) | 2024-07-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN118382024B (en) | Method and system for collecting energy consumption of textile equipment of 5G network in real time | |
| CN117093879B (en) | Intelligent operation management method and system for data center | |
| EP3557819B1 (en) | Server failure detection method and system | |
| CN117560300B (en) | Intelligent internet of things flow prediction and optimization system | |
| CN110942137A (en) | Power grid information operation and maintenance monitoring method based on deep learning | |
| CN118612059B (en) | An Internet of Things device management system | |
| CN118233945A (en) | Intelligent wireless data acquisition system | |
| CN117936955A (en) | Energy storage system and battery internal resistance detection method | |
| CN117574305A (en) | A method and system for real-time monitoring of equipment operating status | |
| CN117891688A (en) | Operation and maintenance management system and method for generated AI (advanced technology attachment) equipment | |
| CN118693939A (en) | A method and system for charging electric tools capable of realizing communication protocol conversion | |
| CN119863234A (en) | Machine room equipment remote operation and maintenance management method and system based on Internet of things technology | |
| CN119420051A (en) | Intelligent automatic acquisition system for substation information | |
| CN119211980B (en) | A deep learning-based fault detection method for wireless sensor networks | |
| CN118503051B (en) | A marketing system omni-channel unified access system | |
| CN105224733B (en) | The method that wind power abandons wind data feature recognition | |
| CN117870779B (en) | Remote wireless intelligent monitoring method and system for automation equipment | |
| CN119376320A (en) | A remote automatic monitoring system for secondary water supply | |
| CN118331144B (en) | Centralized management system of informationized equipment | |
| CN118250031A (en) | Hydropower plant network safety early warning method and system based on deep learning | |
| CN118501721A (en) | Battery detection method based on multidimensional data analysis | |
| CN118473967A (en) | Home wide network quality degradation identification method | |
| CN110020000B (en) | Method and device for determining abnormal wind speed data | |
| CN117614124A (en) | A power information collection system based on dual-mode communication | |
| CN118192286B (en) | Intelligent household appliance control system based on Internet of things |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |