CN112770255A - Adaptive parameter learning method for low-power-consumption positioning label - Google Patents
Adaptive parameter learning method for low-power-consumption positioning label Download PDFInfo
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- 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
- H04W4/023—Services making use of location information using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
- H04W52/02—Power saving arrangements
- H04W52/0203—Power saving arrangements in the radio access network or backbone network of wireless communication networks
- H04W52/0206—Power saving arrangements in the radio access network or backbone network of wireless communication networks in access points, e.g. base stations
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
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- 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
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Abstract
A self-adaptive parameter learning method for a low-power-consumption positioning tag is provided, a mobile base station and a plurality of positioning tags are provided, a communication module of each positioning tag is configured with a sleep mode and a working mode, the positioning tags are configured with basic sleep time and basic communication time, the basic sleep time reflects the sleep time interval of the positioning tag under a static condition, and the basic communication time reflects the working time of the positioning tag under the static condition; the method specifically comprises a time correction step and a time execution step: the time executing step includes acquiring the sleep modification time and the work modification time from the time modification step, and calculating the actual sleep time and the actual work time according to a time allocation algorithm. Has the advantages that: and calculating the actual sleep time and the time working time of the positioning label by adopting a time configuration algorithm, so that the positioning label is adaptive to a continuously changing mobile base station, and energy is saved on the premise of obtaining sufficient data.
Description
Technical Field
The invention relates to a dynamic base station label positioning technology, in particular to a self-adaptive parameter learning method for a low-power-consumption positioning label.
Background
Currently, the positioning technology is daily needed by people, and is often particularly important for finding valuables and establishing a communication protocol, and in actual positioning, a problem occurs, and generally, a position is positioned through at least two base stations, as shown in fig. 1 and 2, a BS in the figure represents a base station, or fine positioning of an envelope surface is performed through return communication time of three base stations, or positioning is performed through ray corners and ray angles of two base stations, but currently, due to popularization of a 5G technology and angle of data security and data response efficiency of the base stations, a concept of a mobile base station appears, and under the concept of the mobile base station, positioning is relatively complicated, positioning points are difficult to determine, and actually, a problem is that positioning strategies applicable due to different network topologies formed under the mobile base station are different, and an object or a target to be positioned is also moved in practice, the positioning tag (MS in the figure) generally uses a low-power consumption communication module because no external power supply is needed to ensure its service life, but the current low-power consumption communication module realizes saving electric energy by configuring a sleep mode, but in many scenarios, the communication module needs to wake up by itself, and in the case of a mobile base station with an uncertain position, the time length and time interval of self-wake-up are uncertain, different distribution results are caused by different physical distributions of different mobile base stations, and if the position change of the mobile base station cannot be predicted, the communication module cannot be woken up by itself, so that low power consumption cannot be realized.
Disclosure of Invention
In view of the above, the present invention provides an adaptive parameter learning method for a low power consumption positioning tag.
In order to solve the technical problems, the technical scheme of the invention is as follows: a self-adaptive parameter learning method for low-power-consumption positioning tags is characterized in that a mobile base station and a plurality of positioning tags are provided, the mobile base station is provided with a positioning strategy for positioning the positioning tags, and a communication module of each positioning tag is provided with a sleep mode and a working mode. The positioning tag is configured with a number index table and a strategy routing table, the number index table stores a plurality of model numbers, each model number takes dynamic feedback information as an index, the strategy routing table stores a plurality of time configuration strategies, and each time configuration strategy takes the model number as an index; the positioning tag is configured with basic dormancy time and basic communication time, the basic dormancy time reflects dormancy time intervals of the positioning tag under a static condition, and the basic communication time reflects working time of the positioning tag under the static condition;
the method specifically comprises a time correction step and a time execution step:
the time executing step includes acquiring sleep modification time and work modification time from the time modification step, and calculating actual sleep time and actual work time according to a time allocation algorithm, where the time allocation algorithm includes:
Tn＝at1+0.25Ts+0.5Tn-1
Tm＝bt2+0.25Tw+0.5Tm-1
wherein, TnFor the actual sleep time, Tn-1The sleep time at the previous moment is 0.25T as the initial values，t2For sleep modification time, TsBased on the sleep time, TmFor actual working time, Tm-1The initial value of the working time at the previous moment is 0.25Tw，t2For working correction time, TwAs a base operating time; the actual sleep time reflects the sleep time interval of the positioning tag under the actual condition, the actual working time reflects the working duration of the positioning tag under the actual condition, a represents a preset first adjusting parameter, and b represents a preset second adjusting parameter;
the time correction step includes:
an information acquisition step, namely configuring the positioning tag in a working mode, wherein the positioning tag is communicated with a mobile base station to acquire actual measurement feedback information, and the actual measurement feedback information reflects the relative position of the positioning tag and the mobile base station;
processing the plurality of measured feedback information to determine and define the position of the mobile base station in a pre-configured static coordinate system to form a plurality of coordinate system mark information, wherein the static coordinate system takes the positioning mark as an origin;
a characteristic identification step, configured with an identification strategy, wherein the identification strategy determines corresponding dynamic feedback information according to the coordinate system mark information;
a number indexing step, namely determining a model number from the number index table according to the obtained dynamic feedback information;
and a strategy positioning step, namely determining a corresponding time configuration strategy from a strategy routing table according to the obtained model number, wherein the time configuration strategy comprises dormancy correction time and working correction time.
Preferably, the information processing step further includes obtaining a relative distance between each positioning tag and each mobile base station, the second adjustment parameter is proportional to the relative distance, and the first adjustment parameter is inversely proportional to the relative distance.
Preferably, the information processing step further includes acquiring a remaining power of the positioning tag, and calculating a first adjustment parameter according to the remaining power, where the first adjustment parameter is inversely proportional to the remaining power.
Preferably, the measured feedback information includes a relative distance and a relative angle, the relative distance reflects the distance between the mobile base station and the positioning tag, and the relative angle reflects the angle between the mobile base station and the positioning tag.
Preferably, the positioning tag stores a plurality of positioning features in advance, each positioning feature is associated with corresponding dynamic feedback information, and the identification strategy comprises generating features to be compared according to coordinate system marking information and comparing each positioning feature with the features to be compared to determine the corresponding dynamic feedback information.
Preferably, the identification strategy includes calculating a deviation value between each positioning feature and the feature to be compared, and screening dynamic feedback information corresponding to the positioning feature with the smallest deviation value.
Preferably, the measured feedback information includes an actual communication frequency band corresponding to each mobile base station.
Preferably, the measured feedback information includes a signal strength range, the signal strength range reflects the signal strength sent by the mobile base station, and the signal strength range is obtained by calculating a measured signal strength value and a relative distance value.
Preferably, a central control end is further provided, and the central control end is configured to generate a dynamic model, where the dynamic model reflects the distribution of the mobile base station, and each time the central control end generates a dynamic model, generates a model number and corresponding model features, and sends the model number, the time allocation policy, and the model features to the positioning identifier.
Preferably, the positioning tag generates dynamic feedback information according to the received model characteristics.
Compared with the prior art, the invention has the advantages that: the method calculates the actual sleep time according to the sleep time of the previous moment and the basic sleep time, and corrects the actual sleep time through the first adjusting parameter and the sleep adjustment time. The actual sleep time of the invention is adjusted in real time according to the sleep time of the previous moment, and the predicted actual sleep time is very close to the actual sleep time under the premise of small calculation amount without long-time deep learning. The calculation of the actual working time is the same. In order to adapt to different distribution results caused by different physical distributions of different mobile base stations, the invention has the advantages of extremely short working time increased by the positioning labels and low power consumption.
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FIG. 1 is a schematic diagram of positioning three base stations in the background art;
FIG. 2 is a diagram illustrating two base station locations in the background art;
FIG. 3 is a schematic flow chart of the present invention.
Detailed Description
The following detailed description of the embodiments of the present invention is provided in order to make the technical solution of the present invention easier to understand and understand.
As shown in fig. 3, an adaptive parameter learning method for low power consumption positioning tags provides a mobile base station and a plurality of positioning tags, the mobile base station is configured with a positioning policy for positioning the positioning tags, and a communication module of the positioning tags is configured with a sleep mode and a working mode. The positioning tag is configured with a number index table and a strategy routing table, the number index table stores a plurality of model numbers, each model number takes dynamic feedback information as an index, the strategy routing table stores a plurality of time configuration strategies, and each time configuration strategy takes the model number as an index; the positioning tag is configured with basic dormancy time and basic communication time, the basic dormancy time reflects dormancy time intervals of the positioning tag under a static condition, and the basic communication time reflects working time of the positioning tag under the static condition;
the method specifically comprises a time correction step and a time execution step:
the time executing step includes acquiring sleep modification time and work modification time from the time modification step, and calculating actual sleep time and actual work time according to a time allocation algorithm, where the time allocation algorithm includes:
Tn＝at1+0.25Ts+0.5Tn-1
Tm＝bt2+0.25Tw+0.5Tm-1
wherein, TnFor the actual sleep time, Tn-1The sleep time at the previous moment is 0.25T as the initial values，t2For sleep modification time, TsBased on the sleep time, TmFor actual working time, Tm-1The initial value of the working time at the previous moment is 0.25Tw，t2For working correction time, TwAs a base operating time; the actual sleep time reflects the sleep time interval of the positioning tag under the actual condition, the actual working time reflects the working duration of the positioning tag under the actual condition, a represents a preset first adjusting parameter, and b represents a preset second adjusting parameter; the method calculates the actual sleep time according to the sleep time of the previous moment and the basic sleep time, and corrects the actual sleep time through the first adjusting parameter and the sleep adjustment time. The actual sleep time of the invention is adjusted in real time according to the sleep time of the previous moment, and the predicted actual sleep time is very close to the actual sleep time under the premise of small calculation amount without long-time deep learning. The calculation of the actual working time is the same. In order to adapt to different distribution results caused by different physical distributions of different mobile base stations, the invention has the advantages of extremely short working time increased by the positioning labels and low power consumption.
The time correction step includes:
an information acquisition step, namely configuring the positioning tag in a working mode, wherein the positioning tag is communicated with a mobile base station to acquire actual measurement feedback information, and the actual measurement feedback information reflects the relative position of the positioning tag and the mobile base station;
processing the plurality of measured feedback information to determine and define the position of the mobile base station in a pre-configured static coordinate system to form a plurality of coordinate system mark information, wherein the static coordinate system takes the positioning mark as an origin;
a characteristic identification step, configured with an identification strategy, wherein the identification strategy determines corresponding dynamic feedback information according to the coordinate system mark information;
a number indexing step, namely determining a model number from the number index table according to the obtained dynamic feedback information;
and a strategy positioning step, namely determining a corresponding time configuration strategy from a strategy routing table according to the obtained model number, wherein the time configuration strategy comprises dormancy correction time and working correction time.
The information processing step further comprises the step of obtaining the relative distance between each positioning tag and each mobile base station, wherein the second adjusting parameter is in direct proportion to the relative distance, and the first adjusting parameter is in inverse proportion to the relative distance.
The information processing step also comprises the steps of obtaining the residual electric quantity of the positioning label and calculating a first adjusting parameter according to the residual electric quantity, wherein the first adjusting parameter is in inverse proportion to the residual electric quantity.
The measured feedback information comprises a relative distance and a relative angle, the relative distance reflects the distance between the mobile base station and the positioning tag, and the relative angle reflects the angle between the mobile base station and the positioning tag.
The positioning tag is prestored with a plurality of positioning features, each positioning feature is associated with corresponding dynamic feedback information, and the identification strategy comprises the steps of generating features to be compared according to coordinate system mark information and comparing each positioning feature with the features to be compared to determine the corresponding dynamic feedback information.
The identification strategy comprises calculating deviation values of each positioning feature and the features to be compared and screening dynamic feedback information corresponding to the positioning feature with the minimum deviation value.
The measured feedback information includes actual communication frequency bands corresponding to each mobile base station.
The measured feedback information comprises a signal intensity range, the signal intensity range reflects the signal intensity sent by the mobile base station, and the signal intensity range is obtained by calculating a measured signal intensity value and a relative distance value.
And the central control end is used for generating a dynamic model, the dynamic model reflects the distribution condition of the mobile base station, and the central control end generates a model number and corresponding model characteristics at the same time when generating a dynamic model and sends the model number, the time configuration strategy and the model characteristics to the positioning identification.
And the positioning label generates dynamic feedback information according to the received model characteristics.
The above are only typical examples of the present invention, and besides, the present invention may have other embodiments, and all the technical solutions formed by equivalent substitutions or equivalent changes are within the scope of the present invention as claimed.
Claims (10)
1. A self-adaptive parameter learning method for low-power-consumption positioning labels is provided, a mobile base station and a plurality of positioning labels are provided, the mobile base station is provided with a positioning strategy for positioning the positioning labels, and a communication module of each positioning label is provided with a sleep mode and a working mode, and is characterized in that: the positioning tag is configured with a number index table and a strategy routing table, the number index table stores a plurality of model numbers, each model number takes dynamic feedback information as an index, the strategy routing table stores a plurality of time configuration strategies, and each time configuration strategy takes the model number as an index; the positioning tag is configured with basic dormancy time and basic communication time, the basic dormancy time reflects dormancy time intervals of the positioning tag under a static condition, and the basic communication time reflects working time of the positioning tag under the static condition;
the method specifically comprises a time correction step and a time execution step:
the time executing step includes acquiring sleep modification time and work modification time from the time modification step, and calculating actual sleep time and actual work time according to a time allocation algorithm, where the time allocation algorithm includes:
Tn＝at1+0.25Ts+0.5Tn-1
Tm＝bt2+0.25Tw+0.5Tm-1
wherein, TnFor the actual sleep time, Tn-1The sleep time at the previous moment is 0.25T as the initial values，t2For sleep modification time, TsBased on the sleep time, TmFor actual working time, Tm-1The initial value of the working time at the previous moment is 0.25Tw，t2For working correction time, TwAs a base operating time; the actual sleep time reflects the sleep time interval of the positioning tag under the actual condition, the actual working time reflects the working duration of the positioning tag under the actual condition, a represents a preset first adjusting parameter, and b represents a preset second adjusting parameter;
the time correction step includes:
an information acquisition step, namely configuring the positioning tag in a working mode, wherein the positioning tag is communicated with a mobile base station to acquire actual measurement feedback information, and the actual measurement feedback information reflects the relative position of the positioning tag and the mobile base station;
processing the plurality of measured feedback information to determine and define the position of the mobile base station in a pre-configured static coordinate system to form a plurality of coordinate system mark information, wherein the static coordinate system takes the positioning mark as an origin;
a characteristic identification step, configured with an identification strategy, wherein the identification strategy determines corresponding dynamic feedback information according to the coordinate system mark information;
a number indexing step, namely determining a model number from the number index table according to the obtained dynamic feedback information;
and a strategy positioning step, namely determining a corresponding time configuration strategy from a strategy routing table according to the obtained model number, wherein the time configuration strategy comprises dormancy correction time and working correction time.
2. The adaptive parameter learning method for low power consumption location tags according to claim 1, wherein: the information processing step further comprises the step of obtaining the relative distance between each positioning tag and each mobile base station, wherein the second adjusting parameter is in direct proportion to the relative distance, and the first adjusting parameter is in inverse proportion to the relative distance.
3. The adaptive parameter learning method for low power consumption location tags according to claim 1, wherein: the information processing step also comprises the steps of obtaining the residual electric quantity of the positioning label and calculating a first adjusting parameter according to the residual electric quantity, wherein the first adjusting parameter is in inverse proportion to the residual electric quantity.
4. The adaptive parameter learning method for low power consumption location tags according to claim 1, wherein: the measured feedback information comprises a relative distance and a relative angle, the relative distance reflects the distance between the mobile base station and the positioning tag, and the relative angle reflects the angle between the mobile base station and the positioning tag.
5. The adaptive parameter learning method for low power consumption location tags according to claim 1, wherein: the positioning tag is prestored with a plurality of positioning features, each positioning feature is associated with corresponding dynamic feedback information, and the identification strategy comprises the steps of generating features to be compared according to coordinate system mark information and comparing each positioning feature with the features to be compared to determine the corresponding dynamic feedback information.
6. The adaptive parameter learning method for low power consumption location tags according to claim 5, wherein: the identification strategy comprises calculating deviation values of each positioning feature and the features to be compared and screening dynamic feedback information corresponding to the positioning feature with the minimum deviation value.
7. The adaptive parameter learning method for low power consumption location tags according to claim 1, wherein: the measured feedback information includes actual communication frequency bands corresponding to each mobile base station.
8. The adaptive parameter learning method for low power consumption location tags according to claim 1, wherein: the measured feedback information comprises a signal intensity range, the signal intensity range reflects the signal intensity sent by the mobile base station, and the signal intensity range is obtained by calculating a measured signal intensity value and a relative distance value.
9. The adaptive parameter learning method for low power consumption location tags according to claim 1, wherein: and the central control end is used for generating a dynamic model, the dynamic model reflects the distribution condition of the mobile base station, and the central control end generates a model number and corresponding model characteristics at the same time when generating a dynamic model and sends the model number, the time configuration strategy and the model characteristics to the positioning identification.
10. The adaptive parameter learning method for low power location tags according to claim 9, wherein: and the positioning label generates dynamic feedback information according to the received model characteristics.
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