CN115086972A - Distributed wireless signal quality optimization method and system - Google Patents
Distributed wireless signal quality optimization method and system Download PDFInfo
- Publication number
- CN115086972A CN115086972A CN202210846859.9A CN202210846859A CN115086972A CN 115086972 A CN115086972 A CN 115086972A CN 202210846859 A CN202210846859 A CN 202210846859A CN 115086972 A CN115086972 A CN 115086972A
- Authority
- CN
- China
- Prior art keywords
- data
- signal
- user equipment
- wireless signal
- equipment
- 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.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 51
- 238000005457 optimization Methods 0.000 title claims abstract description 40
- 238000004364 calculation method Methods 0.000 claims description 40
- 238000012360 testing method Methods 0.000 claims description 28
- 238000004891 communication Methods 0.000 claims description 17
- 238000009826 distribution Methods 0.000 claims description 11
- 230000008569 process Effects 0.000 claims description 9
- 230000005540 biological transmission Effects 0.000 claims description 8
- 238000011084 recovery Methods 0.000 claims description 6
- 238000001914 filtration Methods 0.000 claims description 4
- 238000004220 aggregation Methods 0.000 claims description 3
- 230000002776 aggregation Effects 0.000 claims description 3
- 230000008054 signal transmission Effects 0.000 claims description 3
- 238000012544 monitoring process Methods 0.000 description 14
- 238000012545 processing Methods 0.000 description 12
- 238000001514 detection method Methods 0.000 description 7
- 238000012423 maintenance Methods 0.000 description 6
- 230000000007 visual effect Effects 0.000 description 6
- 238000004422 calculation algorithm Methods 0.000 description 5
- 238000004458 analytical method Methods 0.000 description 4
- 238000003745 diagnosis Methods 0.000 description 4
- 230000002159 abnormal effect Effects 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000009434 installation Methods 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 239000000523 sample Substances 0.000 description 3
- 238000005070 sampling Methods 0.000 description 3
- 241000282326 Felis catus Species 0.000 description 2
- 241001417527 Pempheridae Species 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 230000003247 decreasing effect Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000005059 dormancy Effects 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000004134 energy conservation Methods 0.000 description 2
- 230000010354 integration Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012216 screening Methods 0.000 description 2
- 238000010408 sweeping Methods 0.000 description 2
- 238000012300 Sequence Analysis Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000002457 bidirectional effect Effects 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 230000000295 complement effect Effects 0.000 description 1
- 230000006835 compression Effects 0.000 description 1
- 238000007906 compression Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000013480 data collection Methods 0.000 description 1
- 238000000354 decomposition reaction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000003111 delayed effect Effects 0.000 description 1
- 230000006866 deterioration Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000009429 distress Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000005111 flow chemistry technique Methods 0.000 description 1
- 235000019580 granularity Nutrition 0.000 description 1
- 230000000977 initiatory effect Effects 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 230000006855 networking Effects 0.000 description 1
- 230000008447 perception Effects 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
- 238000012800 visualization Methods 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
- H04W16/18—Network planning tools
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/02—Arrangements for optimising operational condition
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Mobile Radio Communication Systems (AREA)
Abstract
The invention discloses a distributed wireless signal quality optimization method and a system, wherein the method comprises the following steps: receiving data reported by user equipment, wherein the data comprises signal data obtained by detecting a wireless signal by the user equipment; and determining a quality optimization scheme of the wireless signal according to the data reported by the user equipment, wherein the quality optimization scheme comprises a signal source deployment scheme and a signal source optimization scheme of the wireless signal. The system comprises user equipment, a cloud terminal and a server, wherein the user equipment is used for detecting a wireless signal to obtain signal data and reporting the data comprising the signal data to the cloud terminal equipment; and the cloud equipment is used for executing the distributed wireless signal quality optimization method. The invention reduces the cost of adjusting and maintaining the network quality for users and solves the problem of the labor cost for deploying the network.
Description
Technical Field
The invention relates to the technical field of wireless signal quality monitoring, in particular to a distributed wireless signal quality optimization method and system.
Background
In recent years, wireless services such as mobile communication and the like are rapidly developed, mobile phone internet access services are increasingly growing, more demands are placed on cellular networks and WLAN services, even if various micro base stations and wireless hotspots are deployed in home deployment and office places, the possibility that signal quality is poor exists, connection for internet access cannot be smoothly achieved, stable signals do not exist between a terminal and a signal source, network deployment and equipment connection are often required to be optimized, interference resistance is required, or signal blind areas are required to be eliminated. The problems of large house type, poor building coverage and the like are often caused in the process of pushing village communication broadband and family wireless coverage, and the user experience is influenced. Therefore, in practice, the quality of the network during erection needs to be monitored, the distribution of the network signals needs to be evaluated and adjusted, and the reason for the deterioration of the signal quality needs to be analyzed, so that the user experience is improved.
In the signal quality detection and analysis, operators and common users need to realize the positioning of end-to-end faults of terminals and wireless signal sources, the passive maintenance is changed into active maintenance, after installation and deployment, the users can actively report, meanwhile, the devices with unfixed construction and relocation requirements, such as small base stations or wireless hotspots, and the like, are installed more often, dynamic adjustment is needed, the users are provided to help find the faults and solve the problem of network signals, in the prior similar technology, one-time deployment is mainly used, and a method which cannot be subsequently utilized by the users is adopted, so that the flexibility is greatly reduced, and the problem of the solution efficiency of various types of terminals when the networks are utilized for work cannot be solved.
Disclosure of Invention
Therefore, the technical problem to be solved by the present invention is to overcome the defect that the prior art cannot cope with the terminal device, so as to provide a method for flexibly deploying according to the user equipment data and optimizing through deployment, in particular to a distributed wireless signal quality optimization method.
The invention provides a distributed wireless signal quality optimization method, which comprises the following steps:
receiving data reported by user equipment, wherein the data comprises signal data obtained by detecting a wireless signal by the user equipment;
and determining a quality optimization scheme of the wireless signal according to the data reported by the user equipment, wherein the quality optimization scheme comprises a signal source deployment scheme and a signal source optimization scheme of the wireless signal.
Preferably, the signal data comprises signal strength data and location information data;
the signal source deployment scheme of the wireless signal is obtained according to the following modes:
obtaining map data of a place where user equipment is located, wherein the map data comprises three-dimensional data, wall data and interval data;
calculating the signal reflection condition according to the signal intensity data, the position information data, the three-dimensional data, the wall data and the interval data, and calculating the position information data and the signal intensity data of other undetected positions;
performing signal distribution drawing of a three-dimensional space according to all position information data and signal intensity data of a place to obtain a signal map;
and performing model deduction on the signal map to obtain a signal source deployment scheme.
Preferably, the signal source deployment scenario comprises the location, number and/or antenna direction of the signal sources.
Preferably, the signal strength data is reported through a packet filtering framework and an air interface packet in the user equipment; actively acquiring signal intensity data through cloud equipment; the signal intensity data calculates the signal transmission process of the user equipment in a time-sharing and direction-sharing weighting calculation mode to obtain signal demand fairness, and data indexes of a congestion time period and data indexes of an idle time period are generated according to the signal demand fairness; signal demand fairness is used to determine increasing or decreasing transmit power and integrating wireless signal spatial streams.
Preferably, the data further comprises log data regarding the quality of the wireless signal,
the signal source optimization scheme of the wireless signal is obtained according to the following modes:
performing aggregation calculation on the log data based on time to obtain data with time indexes;
distinguishing a data congestion period and an idle period according to data with time indexes;
and comparing the data indexes of the data congestion period with the data indexes of the idle period to determine a transmission power regulation scheme.
Preferably, the data reported by the user equipment further includes a load alarm threshold, and the load alarm threshold is obtained by setting the user equipment;
the method further comprises the following steps:
based on the load alarm threshold, the cloud calculates the signal data in real time, so that the quality of the wireless signals is monitored, and further, the network fault recovery and scheduling are realized.
Preferably, the data reported by the user equipment further includes test data; the test data is generated by the active test of the user equipment, and the test data is reported at regular time;
the method further comprises the following steps:
and receiving test data reported at fixed time by adopting a flow batch integrated data lake architecture.
Another object of the present invention is to provide a distributed wireless signal quality optimization system, comprising:
the user equipment is used for detecting the wireless signal to obtain signal data and reporting the data comprising the signal data to the cloud equipment; the user equipment comprises a plurality of user equipment;
and the cloud device is used for executing the distributed wireless signal quality optimization method.
Preferably, the communication device further comprises a communication device, wherein the communication device comprises a gateway module and a signal source module;
the gateway module is used for connecting the user equipment and the cloud equipment;
the signal source module is used for connecting the user equipment and acquiring RSSI (received signal strength indicator) transmission signal positioning data by connecting the user equipment;
the RSSI transmits signal positioning data for determining the distance and direction of the user equipment compared with the communication equipment;
the distance and the direction are used for determining the positions of the plurality of user equipment from the communication equipment respectively; the locations are used to correspondingly present a plurality of user devices on the signal map.
Preferably, the system further comprises a user side network device, configured to calculate the number of user devices connected to each signal source, and perform load balancing when the number exceeds a preset threshold.
The technical scheme of the invention has the following advantages: according to the invention, the deployment scheme is obtained according to the data reported by the user equipment, the problems of indoor and outdoor wireless signal source coverage and dynamic adjustment schemes are solved, the cost of user adjustment and network quality maintenance is reduced, and the problem of the labor cost of network deployment is solved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flow chart of an optimization method in the practice of the present invention;
FIG. 2 is a flow chart of a signal source deployment scenario resulting from the practice of the present invention;
fig. 3 is a flow chart of a signal source optimization scheme obtained in the practice of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In addition, the technical features involved in the different embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
The following problems still exist in the practical use process:
1. the first problem is the maintenance difficulty, in the signal quality detection process, professional equipment and professionals are needed to be used for detection in the installation stage, manual reporting is conducted, and triggering reporting of abnormal data of a wide range of full links occurs, so that the problem that network signals are unclear or data are not communicated is solved, troubles are brought to operators, and the problems are examined one by one under a large amount of equipment, so that the method is not friendly to wide users far from a central city in all the world after the village communication network is realized.
2. The second problem is that the monitoring cooperation of real-time data terminal types is lack of help, needs to rely on manpower, and has insufficient flexibility and delay. In the presence of network connection failure or, in the absence of a bidirectional solution for interactive solution, in the downstream section of the logical link of the control plane, if the user can not distinguish which kind of problem is in the absence of the network, the event data can not immediately return to the operator or the user, only when the signal state of the network is recovered, the fault event is transmitted back, whether the user equipment is actively disconnected or passively disconnected can not be distinguished, the reasonable data volume can not be compressed, therefore, in the process, new problems are encountered, for example, the acquired data is delayed greatly, the data is lost greatly, the data is inaccurate, a network connection algorithm of user equipment causes disconnection, early warning abnormity is caused, network signal deployment blind areas are cooperatively detected, network slicing under the shielding of buildings is optimized, and signal quality monitoring of a certain slice area is dynamically adjusted in a targeted mode.
3. The third problem is that monitoring real-time and data report presentation are improved, reporting and user presentation need to be considered, user understanding cost is reduced, the requirement of data volume compression is needed when data reports are reported, only necessary data are uploaded, terminals of operators which are different and closest to users are convenient to optimize, for the same requirement, two teams are needed to develop simultaneously, for example, a base station corresponds to a view under a wide area network, a small base station and an indoor view corresponding to a wireless hotspot, the detection effect of a GPS position on an indoor signal source needs to be adjusted according to visual requirements is limited, and the problem that the user presentation is needed to be solved, and the understanding cost is reduced is brought. On one hand, a strict data format is provided, so that a reporting operator and other central servers are convenient to refer to and store, the logic of the reporting operator and other central servers may be different, and finally, a result table is inconsistent, so that the user needs are not fully understood and protected.
The method aims to solve the problems that the quality problem of accurate positioning information is a network data connection problem, a signal source equipment problem or a signal distribution environment problem, which signal sources, terminal equipment and surrounding environment problems are influenced by the problems, and the problems of real-time and changed deployment of the problems occur in what scene. The idea of this embodiment is: the user equipment uses a self signal detection probe, combines signal source equipment to detect network data connection, reports corresponding data, reports the data to a data platform, original data corresponds GPS position and signal quality through a wide area network, an indoor map needs to be created indoors, an original point and a signal source RSSI transmission signal positioning mode are accessed, RSSI data uploaded by the user equipment are modeled to realize ranging, the placing conditions of fixed wireless terminals are calculated, through unified integration, signal distribution in the whole area is summarized, through calculation, the weak place of the signal is found out, local adjustment of the network is completed, planning is perfected, and service quality is improved. And the user side only needs to combine the signal acquisition module, measure the distance according to GPS and RSSI, record the position and the signal quality at the same time, and the user equipment can use the mobile terminal (which can be a handheld device or an automatic advancing robot) as the acquisition terminal by the map creation module and the presentation module according to the signal quality corresponding to the signal source and the equipment sampling point on the map. When a map is drawn, an indoor map can be manually imported, or a sweeping robot establishes a map model, and a signal source is manually selected, so that the indoor map is created through distributed multi-dimensional cooperation. And the core points of the drawn map comprise signal sources, wall positions and channels, and then the signal intensity data are presented according to coordinates. The adopted network has a topological graph core comprising: the system comprises a cloud server, a plurality of routers or gateway devices networked by a grid (mesh), and a mobile terminal (a mobile phone, a robot or an intelligent home) for transmitting position data and signal intensity data.
As shown in fig. 1, the present embodiment provides a distributed wireless signal quality optimization method, which includes:
s1: receiving data reported by user equipment, wherein the data comprises signal data obtained by detecting a wireless signal by the user equipment;
s2: and determining a quality optimization scheme of the wireless signal according to the data reported by the user equipment, wherein the quality optimization scheme comprises a signal source deployment scheme and a signal source optimization scheme of the wireless signal.
According to the data reported by the user equipment, a quality optimization scheme is obtained, the problems of indoor and outdoor wireless signal source coverage and dynamic adjustment schemes are solved, the cost of adjusting and maintaining network quality by a user is reduced, and the problem of the labor cost for deploying the network is solved.
Wherein the signal data comprises signal strength data and location information data;
as shown in fig. 2, the signal source deployment scheme of the wireless signal is obtained as follows:
obtaining map data of a place where user equipment is located, wherein the map data comprises three-dimensional data, wall data and interval data;
calculating the signal reflection condition according to the signal intensity data, the position information data, the three-dimensional data, the wall data and the interval data, and calculating the position information data and the signal intensity data of other undetected positions;
performing signal distribution drawing of a three-dimensional space according to all position information data and signal intensity data of a place to obtain a signal map;
and performing model deduction on the signal map to obtain a signal source deployment scheme.
In this embodiment, a signal source deployment scheme obtained according to stereo data can be applied to a base station or a wireless hotspot, and the stereo data is data of outdoor facades such as buildings and mountains; the signal source deployment scheme obtained according to the wall data and the interval data can be suitable for indoor use; the map data may include stereo data, wall data, and interval data generated by means of a plan view or a satellite map.
In this way, the blind corner is eliminated, and the detection system of the net in the building is perfected.
The signal source deployment scheme comprises the position and the number of signal sources and/or the antenna direction of the signal sources; and realizing deployment optimization through a signal source deployment scheme.
In this embodiment, the signal strength data is reported through a packet filtering architecture and an air interface packet in the user equipment; the method mainly comprises the steps that collection is carried out through base stations or cloud equipment such as optical cats, reported information of user equipment is collected through a management channel protocol such as the optical cats, the base stations and a router which manage cpe, the method is compatible with the capacity of the existing user equipment, relevant wireless signal quality information is actively obtained and collected, and the user equipment is reported in a timing mode and is actively obtained by the cloud equipment. And the signal intensity data calculates the transmission process of one or more user equipment in a time-interval and direction-dividing weighting calculation mode to obtain the signal demand fairness. In order to provide fairness, signal resources are distributed to meet the requirements of areas and grids with high user equipment density, and meanwhile, data traffic of areas with low density is inquired in a time-sharing mode to judge whether the use of transmitting resources can be reduced or not, so that energy conservation is guaranteed.
Actively acquiring signal intensity data; the signal intensity data calculates the signal transmission process of the user equipment in a time-sharing and direction-sharing weighting calculation mode to obtain signal demand fairness, and data indexes of a congestion time period and data indexes of an idle time period are generated according to the signal demand fairness; signal demand fairness is used to determine increasing or decreasing transmit power and integrating wireless signal spatial streams. Defining signal demand fairness, in a given time period, under the given emission power of a wireless signal, such as full load power, observing a first factor which has a designated time period length and a multiple of a wireless packet loss and a retransmission time period, secondly, taking an RSSI signal intensity of equipment in each angle quadrant, such as from 0-pi/6, divided by an average value of time, wherein the average value is proportional to a theoretical value of the equipment at a distance r from a signal source, and marking a peak downloading time period of the equipment; marking the peak uploading time period of the current equipment, calculating the time length of the peak value of the uploading rate of the equipment reaching the highest uplink rate of the equipment and the ratio of the peak time of the uploading rate to the peak uploading time period; and a fifth factor, namely a time delay factor, wherein the calculated TTL time delay of the ip of the mainstream website is divided by the theoretical TTL average value of the line where the user equipment is positioned in the user internet surfing time period.
Adding the weight of each factor for evaluating the signal demand fairness in the congestion period and then carrying out the signal quality index in the idle period;
when equipment with lower signal requirement fairness is marked, the possibility of congestion caused by the fact that space flow and transmitting power are adjusted to meet the intensive work of more equipment is considered, the part of temporarily promised signal quality influencing data service is completed, basic guarantee can be guaranteed when congestion is relatively high, energy is saved in idle time, and high-performance requirements are met;
the map data also includes key regions, non-key regions, reflection surfaces, homing sample points, finding wave surfaces, fitting signal simulation after multiple reflections, and signal distribution of non-sampled regions.
The cloud processing further comprises data screening: original data are screened, thermodynamic diagrams of one year, abnormal time of signals and relevant position data are transmitted, signals stored in a time sequence are accessed in a log sequence mode, and the signals are stored in a data lake through unified data integration logic. Specifically, a time series log database can be adopted, and a data lake is adopted in the cloud for storage. The uploading module is used for preventing map sensitive data, only keeping the abnormity of the first position where the current signal source is located, hiding and encrypting the past data, not uploading map data, and only keeping the direction, distance, strength information and whether the terminal is disconnected or not.
For a cloud, data of a wide area network is required to be processed in a summary mode, large data processing is carried out on longer-period user perception, the same data processing logic is adopted for stream processing and batch processing to solve the problem that requirements of offline and real-time data logic are different, for wide area network erection, distribution maps of a plurality of wireless signal sources are required to be loaded at the same time for combination processing, and most importantly, dead zones and high-load time periods of key areas are calculated.
Further, the data also includes log data regarding the quality of the wireless signal,
as shown in fig. 3, the signal source optimization scheme of the wireless signal is obtained as follows:
performing aggregation calculation on the log data based on time to obtain data with time indexes;
distinguishing a data congestion period from an idle period according to data with a time index;
and comparing the data indexes of the data congestion period with the data indexes of the idle period to determine a transmission power regulation scheme.
Specifically, for example, a real-time indicator, we output an indicator of 3 minutes, that is, 4: the index at 00 includes 4: 00-4: data of 03, 4: 03 comprises 4: 03-4: 06, which is actually data of a time window and a region, is a signal interference phenomenon when it is a thermal region visible to the outside. Because data continuously comes in real-time calculation, an operator or a family user only cares about reasonable time and whether a key area has blind spots, congestion and offline, and determines the processing priority of a target which greatly influences use experience by combining with an alarm event, and especially for middle-aged and old users who do not live with young people, some help needs to be provided. 4: data for a time window of 00 is from 4: 00 begins and the indicator has already begun to be generated. With the time superposition, the index continuously rises and finally tends to be stable. If the user starts all-round all-weather data collection and diagnosis, the network signal quality and the data congestion situation can be distinguished from time sequence analysis, data characteristics are compared with those of an idle network, and the transmitting power is actively adjusted to save energy. Therefore, the calculation problem of reporting a large amount of data when multiple users are used is solved.
When the network quality analysis is carried out, several important levels are carried out, firstly, wireless signals are disconnected, network data are not available, secondly, the load is too heavy, and thirdly, the types and the signal quality of common network terminals and non-common terminals give corresponding priorities.
And adjusting the wireless network access service, namely reasonably suggesting for constructors and base station control antennas through signal distribution blind spots obtained by measurement and calculation. For example, for small base stations with medium and small encrypted signals, the direction of the antenna can be adjusted, when residents are concentrated at night, the antenna is mainly focused on a key area, or for laying household wireless hotspots, grid (mesh) equipment is used, and when a home user or preliminarily supposing grid (mesh) networking, a grid (mesh) equipment placing suggestion is provided, so that the situation that the mesh (mesh) equipment is too dense is avoided, and meanwhile, when more access equipment are provided, the grid (mesh) equipment can be used for switching to a visitor terminal with reduced network quality, and a grid (mesh) node with smaller burden is connected.
For the indoor part, the map analysis comprises signal path visualization, reflection calculation is carried out according to the walls and the intervals represented by the indoor map, the signal intensity of undetected positions and sampling points is calculated, and the 3d drawing is preferably established by adopting a horizontal gyroscope to finish the signal distribution drawing of the three-dimensional space. After the map and the sampling data uploaded by the user are obtained, model deduction can be performed through the cloud server, so that a better signal source deployment scheme can be obtained by deducting newly installed signal sources and places, reasonable quantity and positions are arranged, and installation and debugging cost is reduced.
In particular, for public police, some remote areas, where there is no change in signal quality, such as a mobile phone that is stationary for a long period of time, a wireless device that is stationary to transmit and receive signals, may be in the event of a police incident. Whether the user tends to be stable or not is judged based on the change rate of a time window, a region and a traffic planning index, and if the user is a human portable device, whether the user has a strong association condition or not is judged, so that the method and the system are complementary with a distress system of satellite communication.
The core of the technical idea of the embodiment is multi-signal source multi-angle comprehensive positioning, the precision of wide area deployment base stations and services is improved, local communication environments are better served, and indoor thermodynamic diagrams are drawn and recorded by measuring and calculating the distance and the signal quality of a receiving end through multiple signal sources such as mesh equipment in multiple angles.
In order to avoid which task on the link has a problem and introduce a real-time calculation task state, when the indexes tend to be stable, whether the calculation tasks on the production link are normal or not is judged, if the calculation tasks are normal, the indexes of the time points of the tasks are stable, and the tasks can provide services for the outside. If the calculation is stuck, piled up or an exception is already existed in the restarting process, the iterative processing is continuously waited.
Further, the data reported by the user equipment also comprises a load alarm threshold, and the load alarm threshold is obtained by the user equipment setting;
the method further comprises the following steps:
based on the load alarm threshold, the cloud calculates the signal data in real time, wireless signal quality monitoring is achieved, and then network fault recovery and scheduling are achieved.
Specifically, based on comparison between a load alarm threshold and a current load, if the current load exceeds the load alarm threshold, decomposition calculation tasks (abstract extraction) are carried out on signal data and reported, the cloud side carries out real-time calculation on the reported abstract simultaneously, and network fault recovery and scheduling are achieved according to comparison between the wireless signal quality, the calculation result of the load and a fault recovery strategy. Make full use of user equipment's idle power of calculating, still meet the power peak value of calculating and cooperate with the high in the clouds equipment.
The method also comprises the steps of positioning and early warning the signal quality, calculating reported data in real time based on a load alarm threshold set by a terminal, monitoring and calculating precision data with specific positions of dozens of centimeters, realizing the frame loss rate of wireless signals and code-second level monitoring, combining QoS diagnosis indexes such as jamming and internet data connection and the like for diagnosis, completing automatic feedback and active diagnosis of the most common network problems of users, finding out the reasons such as video abnormity, app network faults and the like in time, and giving early warning to operation and maintenance to realize fault recovery and scheduling.
In this embodiment, the user equipment itself serves as a basis for sending an alarm when the load alarm threshold value is exceeded, the current load condition calculation result is compared with the load alarm threshold value, whether the alarm is reported to the cloud, the summary of the single signal data is reported in real time, and the cloud further calculates the summary to reduce the data uploading amount;
the cloud side always carries out calculation work (real-time calculation), the work content is different from the calculation work of the user equipment, and network quality monitoring is carried out when the result is greater than a load alarm threshold value;
the real-time calculation result is to judge the quality of the working condition of the current user equipment and judge the quality of the working condition of the current user equipment according to the existing monitoring target (wireless signal quality, such as frame loss rate and wireless network load capacity redundancy); initiating request assisted computing, such as: the cpu occupancy rate and the memory occupancy rate are too high, the cpu or memory occupancy rates of the user equipment and the gateway equipment reach 90%, the user equipment sends an alarm to request the cloud to assist in computing, and partial tasks with heavy performance burden are transferred.
Further, the data reported by the user equipment also includes test data; the test data is generated by the active test of the user equipment, and the test data is reported at regular time;
the method further comprises the following steps:
and receiving test data reported at fixed time by adopting a flow batch integrated data lake architecture.
VR/AR aspect: and assisting to enhance the visual effect. The SLAM technology can construct a map with a more real visual effect, so that the superposition effect of the virtual object is rendered according to the current visual angle, and the virtual object is more real and has no sense of incongruity. SLAM is used as a means of visual enhancement by Microsoft Hololens, Google ProjectTango, and MagicLeap in VR/AR representatives.
Unmanned aerial vehicle field and robot location navigation field: and (5) modeling a map. The SLAM algorithm is applied to unmanned planes and the like (for example, domestic Covos and Tami floor sweepers can draw indoor maps efficiently by applying the SLAM algorithm and combining a laser radar or a camera, so that intelligent analysis and floor sweeping environment planning can be performed), the SLAM can quickly construct local 3D maps, and can be combined with a Geographic Information System (GIS) and a visual object identification technology to assist the unmanned planes in identifying roadblocks and automatically avoiding obstacles and planning paths, and also assist robots in executing tasks such as path planning, autonomous exploration and navigation.
In this embodiment, a distributed wireless signal quality monitoring method is further provided, which includes the steps of:
monitoring the signal quality;
the method comprises the following steps: and collecting data buried points by adopting user equipment to obtain signal data.
Specifically, the method also comprises the steps of carrying out wireless service quality test by installing a soft probe on the user equipment, and respectively collecting intensity information of different positions for establishing a map and selecting a signal source position in advance; a plurality of antennas are adopted to simultaneously carry out ranging on user equipment and collect signal data; the user equipment comprises a mobile phone, a flat panel, a television, a set-top box, a sweeper and network equipment which can be used for daily life and can communicate with a signal source and feed back signal data.
Step two: actively testing the network data connection to obtain test data, and reporting the test data to the cloud for recording;
in this embodiment, the active test includes ping test, Tracert test, Http test, video test, and broadband test; the cloud end adopts a batch-flow integrated data lake structure, test data are reported to the cloud end in a row data form at regular time, and the test data when network congestion occurs are recorded; the test data are reported regularly to generate time sequence data, the cloud end is used for flexibly receiving the time sequence data, the cloud end adopts a flow-batch integrated data lake structure, time sequence data with different granularities can be conveniently and flexibly received, and in a link, a calculation layer uses a data processing logic to express the same service requirement. And the signal quality problem of simultaneous reporting of a large amount of data is integrally processed by using the lake flow of data.
Step three: and (3) detecting the environmental cause of the discharged wireless quality, and realizing passive monitoring according to the position information and the strength information: the method comprises the steps of transmitting corresponding strength information through a PACKET filtering (Netfilter) framework and an air interface PACKET, carrying out edge calculation on a local area network or a terminal with good calculation capacity, automatically identifying data PACKET flow in a data PACKET (PF _ PACKET) acquisition mode to obtain a wireless quality index, and reporting signal data with the wireless quality index to a data acquisition interface when the wireless quality index is abnormal.
In the embodiment, the calculation comprises edge calculation and cloud calculation, the edge calculation is realized through an edge calculation submodule, the cloud calculation is realized through a cloud calculation submodule, and after data is stored in the cloud, the cloud calculation submodule analyzes signal data of the same time period and monthly time period in parallel through batch flow processing and matches the signal data in a festival state to count key network quality data of the whole network; and the cloud computing submodule also adopts a real-time computing mode of flow optimization and stream processing (Streaming) of the access network side.
Specifically, an edge computation submodule, an optical modem, a router and a mesh (mesh) which are deployed at a home subscriber end are used for computing the load of the mesh and optimizing signal distribution, and when a large number of home parties exist, for example, more than 10 terminals are connected to one AP node at the same time, the terminals need to be informed actively, and the users are informed, so that load balancing is performed automatically. Awakening other mesh nodes in a dormant state or an energy-saving state, wherein the energy-saving state needs to be set by depending on learning and monitoring of a walking track of a home user, whether a certain node is connected or not, the energy-saving mode is started in an inactive period, the energy-saving mode is operated in an active mode in a full state, manual setting can be supplemented, and the dormancy is manually cancelled when a signal is required by the user. The specific calculation process is as follows: each load condition and signal condition of a computing grid (mesh) node and a router end are used as inlets of edge computing, only the computing of a local access terminal is carried out, and grid equipment mainly meets the requirement that signal coverage of key areas, such as bedrooms, kitchens and toilets, especially in vast rural buildings and urban large-dwelling families has the requirement of laying the grid equipment, needs dynamic energy conservation, and actively enters dormancy to wait for awakening in the long-term idle load.
Step four: actively reporting the signal data with the fault event and the disconnection warning to a data acquisition interface, and screening main information; the data acquisition interface sends the signal data to a cloud service message queue after acquiring the signal data; in this embodiment, the cloud service message queue is a cloud service kafka message queue. Only main information is transmitted to the cloud end, and the calculation burden is reduced.
Step five: a signal map is constructed based on the signal strength data in the signal data to present the wireless signal quality within the map area.
Preferably, a map is set or a local map is generated, the map is only displayed at a user side, optionally, an algorithm is added, when optimized deployment is carried out, map data can be uploaded, and the most effective signal source deployment scheme is calculated through an algorithm model.
In order to increase interest, by the signal quality monitoring method, an AR map can be drawn, signal intensity data, such as a mesh signal image, can be visually seen directly through a mobile phone or a screen of a mobile eye, the signal intensity data moves with an indoor position, and digital or colored ripples are displayed on a display screen.
The embodiment also provides a distributed wireless signal quality optimization system, which includes:
the user equipment is used for detecting the wireless signal to obtain signal data and reporting the data comprising the signal data to the cloud equipment; the user equipment comprises one or more devices;
and the cloud device is used for executing the distributed wireless signal quality optimization method.
In this embodiment, the cloud device may manage, control, and acquire signal strength data related to the user equipment, the backbone network, and the access network.
Further, the system also comprises communication equipment, wherein the communication equipment comprises a gateway module and a signal source module;
the gateway module is used for connecting the user equipment and the cloud equipment;
the signal source module is used for connecting the user equipment and acquiring RSSI (received signal strength indicator) transmission signal positioning data by connecting the user equipment;
the RSSI transmits signal positioning data for determining the distance and direction of the user equipment compared with the communication equipment;
the distance and the direction are used for determining the positions of the plurality of user equipment from the communication equipment respectively; the locations are used to correspondingly present a plurality of user devices on the signal map.
Furthermore, the system also comprises user side network equipment, which is used for calculating the number of user equipment connected with each signal source and carrying out load balancing when the number exceeds a preset threshold value.
Specifically, the signal data is obtained by the initial calculation of the user equipment, and is uploaded to the cloud to compare a plurality of adjacent user equipment, so as to obtain whether individual user equipment is congested or not and overloaded, and evaluate the signal quality of a total area, and specific indexes are as follows: the lower the frame loss rate, the lower the time delay and the interference, the better, when the frame loss rate in a fixed time slice is that only 2 tcp data packets are lost per ten million, or the time delay is more than 300ms, the poor signal quality of a certain area can be warned, and therefore scheduling control is carried out. The cloud receives the alarm and performs load balancing on the load of the lower-layer network, so that the method is suitable for scheduling in a wider range and signal sources with large coverage and more users, such as a base station. When the number of the base stations or the user equipment exceeds the ideal load number, the wireless signals need to be directionally enhanced towards the direction with large number density of the equipment; or sending an instruction to the user equipment to enable the user equipment to actively connect with other signal sources nearby.
The distributed wireless signal quality optimization method and system provided by the embodiment have the following beneficial effects:
1. the method has the advantages that the signal quality acquisition is completed by one or more user devices, the problems of indoor and outdoor wireless signal source coverage and dynamic adjustment schemes are solved, the cost of adjusting and maintaining network quality by users is reduced, blind corners are eliminated, the problem of network deployment labor cost is solved, and a detection system for network arrangement in a building is perfected; the problem of signal quality of simultaneous reporting of a large amount of data is integrally processed by using a data lake flow batch, the calculation problem of reporting of the large amount of data when multiple users are used is solved, real-time report form query and early warning are realized, only main information is transmitted to a cloud server, and the calculation burden is reduced;
2. the cloud end, the edge and the user equipment adopt the same data processing logic when processing data integrally, share the time-sequenced data format, share and reduce the flow, and reduce the development labor cost and the maintenance cost.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications therefrom are within the scope of the invention.
Claims (10)
1. A method for optimizing distributed wireless signal quality, comprising:
receiving data reported by user equipment, wherein the data comprises signal data obtained by detecting a wireless signal by the user equipment;
and determining a quality optimization scheme of the wireless signal according to the data reported by the user equipment, wherein the quality optimization scheme comprises a signal source deployment scheme and a signal source optimization scheme of the wireless signal.
2. The distributed wireless signal quality optimization method of claim 1, wherein the signal data comprises signal strength data and location information data;
obtaining a signal source deployment scheme of the wireless signal according to the following modes:
obtaining map data of a place where the user equipment is located, wherein the map data comprises three-dimensional data, wall data and interval data;
calculating signal reflection conditions according to the signal intensity data, the position information data, the three-dimensional data, the wall data and the interval data, and calculating position information data and signal intensity data of other undetected positions;
performing signal distribution drawing of a three-dimensional space according to all position information data and signal intensity data of the places to obtain a signal map;
and performing model deduction on the signal map to obtain a signal source deployment scheme.
3. The distributed wireless signal quality optimization method according to claim 2, wherein the signal source deployment scenario includes location, number of signal sources and/or antenna direction of signal sources.
4. The method of claim 3, wherein the signal strength data is reported via a packet filtering structure and an air interface packet in a UE; actively acquiring the signal strength data; the signal intensity data calculates the signal transmission process of the user equipment in a time-sharing and direction-sharing weighting calculation mode to obtain signal demand fairness, and generates a data index of a congestion time period and a data index of an idle time period according to the signal demand fairness; the signal demand fairness is used to determine to increase or decrease transmit power and to integrate wireless signal spatial streams.
5. A distributed wireless signal quality optimization method according to claim 3, wherein said data further includes log data regarding wireless signal quality,
obtaining a signal source optimization scheme of the wireless signal according to the following modes:
performing aggregation calculation on the log data based on time to obtain data with time indexes;
distinguishing a data congestion period from an idle period according to the data with the time index;
and comparing the data indexes of the data congestion period with the data indexes of the idle period to determine a transmission power regulation scheme.
6. The method according to claim 2, wherein the data reported by the ue further includes a load alarm threshold, and the load alarm threshold is set by the ue;
the method further comprises the following steps:
and based on the load alarm threshold, the cloud calculates the signal data in real time, so that the quality of the wireless signal is monitored, and further, the network fault recovery and scheduling are realized.
7. The method of claim 3, wherein the data reported by the UE further comprises test data; the test data is generated by the active test of the user equipment, and the test data is reported at regular time;
the method further comprises the following steps:
and receiving the test data reported at fixed time by adopting a flow batch integrated data lake architecture.
8. A distributed wireless signal quality optimization system, comprising:
the user equipment is used for detecting a wireless signal to obtain signal data and reporting the data comprising the signal data to the cloud equipment; the user equipment comprises one or more devices;
cloud device for performing the distributed wireless signal quality optimization method of any of claims 1-7.
9. The distributed wireless signal quality optimization system of claim 8, further comprising a communication device, the communication device comprising a gateway module and a signal source module;
the gateway module is used for connecting the user equipment and the cloud end equipment;
the signal source module is used for connecting the user equipment and acquiring RSSI (received signal strength indicator) transmission signal positioning data by connecting the user equipment;
the RSSI transmitted signal positioning data is used for determining the distance and direction of the user equipment compared with the communication equipment;
the distance and the direction are used for determining the positions of the user equipment and the communication equipment respectively; the location is used for correspondingly presenting a plurality of the user equipment on a signal map.
10. The distributed wireless signal quality optimization system according to claim 9, further comprising a user side network device, configured to calculate the number of user devices connected to each signal source, and perform load balancing when the number exceeds a preset threshold.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210846859.9A CN115086972B (en) | 2022-07-19 | 2022-07-19 | Distributed wireless signal quality optimization method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210846859.9A CN115086972B (en) | 2022-07-19 | 2022-07-19 | Distributed wireless signal quality optimization method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115086972A true CN115086972A (en) | 2022-09-20 |
| CN115086972B CN115086972B (en) | 2023-04-07 |
Family
ID=83259855
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210846859.9A Active CN115086972B (en) | 2022-07-19 | 2022-07-19 | Distributed wireless signal quality optimization method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115086972B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117395685A (en) * | 2023-10-30 | 2024-01-12 | 中国铁建电气化局集团有限公司 | High-speed railway wireless network optimizing system based on artificial intelligence |
| CN117395685B (en) * | 2023-10-30 | 2024-07-09 | 中国铁建电气化局集团有限公司 | High-speed railway wireless network optimizing system based on artificial intelligence |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102880743A (en) * | 2012-08-24 | 2013-01-16 | 广州天越电子科技有限公司 | WLAN (wireless local area network) drawing design method based on AutoCAD (auto computer aided design) platform |
| US20150222372A1 (en) * | 2014-02-05 | 2015-08-06 | Google Inc. | Methods and Systems for Determining Signal Strength Maps for Wireless Access Points Robust to Measurement Counts |
| CN108668315A (en) * | 2018-05-25 | 2018-10-16 | 中国联合网络通信集团有限公司 | Network test method, terminal and system |
| CN108834058A (en) * | 2018-04-27 | 2018-11-16 | 武汉大学 | A kind of indoor positioning signal source Optimization deployment method based on heredity with fireworks combinational algorithm |
| CN109041084A (en) * | 2018-09-11 | 2018-12-18 | 武汉维力克科技有限公司 | A kind of method of radio network optimization |
| US10849034B1 (en) * | 2019-11-26 | 2020-11-24 | Motorola Mobility Llc | Signal map for wireless connectivity |
| CN112312405A (en) * | 2019-07-25 | 2021-02-02 | 袁帝文 | Method and system for reinforcing wireless signal and sharing and following wireless network load |
| CN113852517A (en) * | 2021-09-06 | 2021-12-28 | 天翼数字生活科技有限公司 | AR-based signal intensity visualization system and method |
| CN114268903A (en) * | 2021-12-28 | 2022-04-01 | 北京航空航天大学 | Geographic information assisted unmanned aerial vehicle relay position deployment and power distribution method |
-
2022
- 2022-07-19 CN CN202210846859.9A patent/CN115086972B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102880743A (en) * | 2012-08-24 | 2013-01-16 | 广州天越电子科技有限公司 | WLAN (wireless local area network) drawing design method based on AutoCAD (auto computer aided design) platform |
| US20150222372A1 (en) * | 2014-02-05 | 2015-08-06 | Google Inc. | Methods and Systems for Determining Signal Strength Maps for Wireless Access Points Robust to Measurement Counts |
| CN108834058A (en) * | 2018-04-27 | 2018-11-16 | 武汉大学 | A kind of indoor positioning signal source Optimization deployment method based on heredity with fireworks combinational algorithm |
| CN108668315A (en) * | 2018-05-25 | 2018-10-16 | 中国联合网络通信集团有限公司 | Network test method, terminal and system |
| CN109041084A (en) * | 2018-09-11 | 2018-12-18 | 武汉维力克科技有限公司 | A kind of method of radio network optimization |
| CN112312405A (en) * | 2019-07-25 | 2021-02-02 | 袁帝文 | Method and system for reinforcing wireless signal and sharing and following wireless network load |
| US10849034B1 (en) * | 2019-11-26 | 2020-11-24 | Motorola Mobility Llc | Signal map for wireless connectivity |
| CN113852517A (en) * | 2021-09-06 | 2021-12-28 | 天翼数字生活科技有限公司 | AR-based signal intensity visualization system and method |
| CN114268903A (en) * | 2021-12-28 | 2022-04-01 | 北京航空航天大学 | Geographic information assisted unmanned aerial vehicle relay position deployment and power distribution method |
Non-Patent Citations (1)
| Title |
|---|
| 吴端坡 等: "无线接入点自适应部署算法仿真", 《实验室研究与探索》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117395685A (en) * | 2023-10-30 | 2024-01-12 | 中国铁建电气化局集团有限公司 | High-speed railway wireless network optimizing system based on artificial intelligence |
| CN117395685B (en) * | 2023-10-30 | 2024-07-09 | 中国铁建电气化局集团有限公司 | High-speed railway wireless network optimizing system based on artificial intelligence |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115086972B (en) | 2023-04-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| KR100813430B1 (en) | System and method for design, tracking, measurement, prediction and optimization of data communication networks | |
| US10064084B1 (en) | System, method, and computer program for performing mobile network related tasks based on performance data acquired by an unmanned vehicle | |
| CN107920355A (en) | The wireless network that machine learning is coordinated | |
| CN102928019A (en) | System and method for monitoring environment based on wireless sensor and geographic information | |
| CN107623925A (en) | A kind of indoor wifi signal detecting methods and system based on floor plan | |
| CN112003938B (en) | Municipal works job site remote management system | |
| CN112367617A (en) | Indoor emergency evacuation method, system and device for building and intelligent equipment | |
| CN113873532B (en) | Intelligent park 5G network planning method | |
| CN108055353A (en) | A kind of electric power construction field informationization supervision system based on wireless sensor network | |
| CN108600372B (en) | Information interaction system based on multiple power transmission equipment inspection devices and control terminal | |
| CN114240031A (en) | 5G network bearing quality evaluation method facing power service | |
| CN115086972B (en) | Distributed wireless signal quality optimization method and system | |
| CN209930512U (en) | Forest and grassland wireless mobile multimedia emergency system | |
| CN110913333B (en) | Outdoor pedestrian flow warning method based on Wi-Fi probe | |
| Ding et al. | Joint communication quality assurance algorithm for UAVs flying over urban LTE networks | |
| CN210488778U (en) | Oil field well site monitoring system based on wireless transmission | |
| CN113852796A (en) | Power grid Ka high-flux satellite network timing image uploading method and device | |
| CN114143331A (en) | Wisdom garden situation perception system based on thing networking | |
| US11317228B2 (en) | System for the detection of noise | |
| CN202340308U (en) | Automatic testing system for wireless network | |
| US11856492B2 (en) | Methods and apparatus for modeling wireless traffic and for using wireless traffic information | |
| CN103813130A (en) | Internet-of-things network video monitoring system | |
| CN216017224U (en) | Navigation positioning equipment and lighting equipment thereof | |
| Wright et al. | Weather Visibility Sensor Network: Platform Characterization | |
| Veric et al. | Characterization of LoRaWAN wireless sensors network in outdoor and indoor conditions |
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 |