CN115515223A - Fingerprint information processing method and device and network equipment - Google Patents
Fingerprint information processing method and device and network equipment Download PDFInfo
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- CN115515223A CN115515223A CN202110696900.4A CN202110696900A CN115515223A CN 115515223 A CN115515223 A CN 115515223A CN 202110696900 A CN202110696900 A CN 202110696900A CN 115515223 A CN115515223 A CN 115515223A
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Abstract
The invention provides a fingerprint information processing method, a fingerprint information processing device and network equipment, and relates to the technical field of Internet of things. The method comprises the following steps: obtaining a target fingerprint hot spot set according to at least one fingerprint hot spot; acquiring quality information of each fingerprint hotspot according to the characteristic information of each fingerprint hotspot in the target fingerprint hotspot set; and constructing a wireless fidelity WiFi fingerprint database according to the characteristic information and the quality information corresponding to each fingerprint hotspot, wherein the WiFi fingerprint database is used for WiFi fingerprint positioning. The scheme of the invention solves the problem of insufficient fingerprint positioning precision caused by the lack of a uniform fingerprint information processing mode in the prior art.
Description
Technical Field
The invention relates to the technical field of internet of things, in particular to a fingerprint information processing method, a fingerprint information processing device and network equipment.
Background
In the prior art, when a fingerprint of a certain acquisition point is subjected to smoothing processing, the smoothing processing is usually realized by means of averaging, and appropriate weighting processing needs to be performed on fingerprint information processed by different devices, different time periods or different models so as to increase the robustness of a fingerprint library, and average weighting is usually performed if updating is needed.
However, in the prior art, for the collection of fingerprint information, different devices, different time periods or different models need to be distinguished, a unified fingerprint information processing mode is lacking, the statistical characteristics of data are not sufficiently mined and collected for the fingerprint information, and only the average value is directly obtained, so that the fingerprint positioning accuracy is not high enough.
Disclosure of Invention
The invention aims to provide a fingerprint information processing method, a fingerprint information processing device and network equipment, and solves the problem that the fingerprint positioning accuracy is not high enough due to the lack of a unified fingerprint information processing mode in the prior art.
In order to achieve the above object, an embodiment of the present invention provides a method for processing fingerprint information, including:
obtaining a target fingerprint hot spot set according to at least one fingerprint hot spot;
acquiring quality information of each fingerprint hotspot according to the characteristic information of each fingerprint hotspot in the target fingerprint hotspot set;
and constructing a WiFi (Wireless Fidelity) fingerprint database according to the characteristic information and the quality information corresponding to each fingerprint hotspot, wherein the WiFi fingerprint database is used for WiFi fingerprint positioning.
Optionally, the obtaining a target fingerprint hotspot set according to at least one fingerprint hotspot includes:
performing feature extraction on at least one fingerprint hotspot to obtain feature information corresponding to each fingerprint hotspot;
and screening the at least one fingerprint hot spot according to the characteristic information to obtain a target fingerprint hot spot set.
Optionally, the feature information comprises at least one of:
hot spot mean value;
probability of occurrence of hot spots;
area hot spot identification information;
standard deviation of hot spots.
Optionally, the screening operation comprises:
deleting a first fingerprint hotspot of the at least one fingerprint hotspot from the at least one fingerprint hotspot if the first fingerprint hotspot meets a first preset condition;
wherein the first preset condition comprises at least one of:
the hot spot mean value of the first fingerprint hot spot is smaller than or equal to a preset mean value;
the probability of the first fingerprint hotspot occurring is less than or equal to a preset probability.
Optionally, the obtaining, according to the feature information of each fingerprint hotspot in the target fingerprint hotspot set, quality information of each fingerprint hotspot includes:
according to the characteristic information, obtaining a power coefficient corresponding to each piece of characteristic information of each fingerprint hotspot;
and obtaining the quality information of the fingerprint hot spot according to the efficacy coefficient and the weight coefficient corresponding to the characteristic information.
Optionally, the processing method further includes:
setting a weight coefficient for each piece of feature information according to the importance ranking of different pieces of feature information;
wherein the sum of the weight coefficients corresponding to the feature information is 1.
Optionally, before obtaining the target fingerprint hotspot set according to at least one fingerprint hotspot, the processing method further includes:
selecting a hotspot in a preset radius range by taking the first hotspot as a circle center as the at least one fingerprint hotspot; the first hot spot is a fixed hot spot with the maximum RSSI (Received Signal Strength Indication) in a target area; the hot spots comprise fixed hot spots and/or hot spots in the environment containing location information.
Optionally, the WiFi fingerprint database includes at least one of a number, a Media Access Control Address (MAC Address), a recording time, a signal strength mean value, quality information, a recording duration, a longitude and a latitude corresponding to each of the fingerprint hotspots.
Optionally, the processing method further includes:
updating the WiFi fingerprint database;
wherein, in the case that the environment of the fingerprint hotspot is not changed, the updating operation includes at least one of:
according to the time length proportion, the average value of the signal intensity is obtained;
according to the time length proportion, the quality information is averaged;
updating the recording duration;
and updating the recording time.
To achieve the above object, an embodiment of the present invention provides a network device, including a processor and a transceiver, where the processor is configured to:
obtaining a target fingerprint hot spot set according to at least one fingerprint hot spot;
acquiring quality information of each fingerprint hotspot according to the characteristic information of each fingerprint hotspot in the target fingerprint hotspot set;
and constructing a wireless fidelity WiFi fingerprint database according to the characteristic information and the quality information corresponding to each fingerprint hotspot, wherein the WiFi fingerprint database is used for WiFi fingerprint positioning.
Optionally, when the processor obtains the target fingerprint hot spot set according to the at least one fingerprint hot spot, the processor is specifically configured to:
performing feature extraction on at least one fingerprint hotspot to obtain feature information corresponding to each fingerprint hotspot;
and screening the at least one fingerprint hot spot according to the characteristic information to obtain a target fingerprint hot spot set.
Optionally, the feature information comprises at least one of:
hot spot mean value;
probability of occurrence of hot spots;
region hotspot identification information;
standard deviation of hot spots.
Optionally, the screening operation comprises:
deleting a first fingerprint hotspot of the at least one fingerprint hotspot from the at least one fingerprint hotspot if the first fingerprint hotspot meets a first preset condition;
wherein the first preset condition comprises at least one of:
the hot spot mean value of the first fingerprint hot spot is smaller than or equal to a preset mean value;
the probability of the first fingerprint hotspot occurring is less than or equal to a preset probability.
Optionally, when the processor acquires the quality information of each fingerprint hotspot according to the feature information of each fingerprint hotspot in the target fingerprint hotspot set, the processor is specifically configured to:
according to the characteristic information, obtaining an efficacy coefficient corresponding to each characteristic information of each fingerprint hotspot;
and obtaining the quality information of the fingerprint hot spot according to the efficacy coefficient and the weight coefficient corresponding to the characteristic information.
Optionally, the processor is further configured to:
setting a weight coefficient for each piece of feature information according to the importance ranking of different pieces of feature information;
wherein the sum of the weight coefficients corresponding to the feature information is 1.
Optionally, before obtaining the target fingerprint hotspot set according to the at least one fingerprint hotspot, the processor is further configured to:
selecting a hotspot in a preset radius range by taking the first hotspot as a circle center as the at least one fingerprint hotspot; the first hot spot is a fixed hot spot with the maximum RSSI (signal strength indicator) received in a target area; the hot spots comprise fixed hot spots and/or hot spots containing position information in the environment.
Optionally, the WiFi fingerprint database includes at least one of a number, a MAC address, a recording time, a signal strength mean, quality information, a recording duration, a longitude, and a latitude corresponding to each of the fingerprint hotspots.
Optionally, the processor is further configured to:
updating the WiFi fingerprint database;
wherein, in case that the environment of the fingerprint hotspot is not changed, the updating operation comprises at least one of the following:
according to the time length proportion, the average value of the signal intensity is obtained;
according to the time length proportion, the quality information is averaged;
updating the recording duration;
and updating the recording time.
To achieve the above object, an embodiment of the present invention provides a fingerprint information processing apparatus, including:
the first processing module is used for obtaining a target fingerprint hot spot set according to at least one fingerprint hot spot;
the second processing module is used for acquiring the quality information of each fingerprint hotspot according to the characteristic information of each fingerprint hotspot in the target fingerprint hotspot set;
and the third processing module is used for constructing a wireless fidelity WiFi fingerprint database according to the characteristic information and the quality information corresponding to each fingerprint hotspot, and the WiFi fingerprint database is used for WiFi fingerprint positioning.
Optionally, the first processing module includes:
the characteristic extraction unit is used for extracting the characteristics of at least one fingerprint hotspot to obtain characteristic information corresponding to each fingerprint hotspot;
and the data screening unit is used for screening the at least one fingerprint hot spot according to the characteristic information to obtain a target fingerprint hot spot set.
Optionally, the feature information comprises at least one of:
hot spot mean value;
probability of occurrence of hot spots;
region hotspot identification information;
standard deviation of hot spots.
Optionally, the data filtering unit includes:
the data screening subunit is configured to delete a first fingerprint hotspot from the at least one fingerprint hotspot when the first fingerprint hotspot meets a first preset condition;
wherein the first preset condition comprises at least one of:
the hot spot mean value of the first fingerprint hot spot is smaller than or equal to a preset mean value;
the probability of the first fingerprint hotspot occurring is less than or equal to a preset probability.
Optionally, the second processing module includes:
the first processing unit is used for obtaining an efficacy coefficient corresponding to each piece of feature information of each fingerprint hotspot according to the feature information;
and the second processing unit is used for obtaining the quality information of the fingerprint hot spot according to the efficacy coefficient and the weight coefficient corresponding to the characteristic information.
Optionally, the processing apparatus further comprises:
the weight setting module is used for setting a weight coefficient for each piece of characteristic information according to the importance sequence of different pieces of characteristic information;
wherein the sum of the weight coefficients corresponding to the feature information is 1.
Optionally, the processing apparatus further comprises:
the hotspot determining module is used for selecting a hotspot in a preset radius range by taking the first hotspot as a circle center as the at least one fingerprint hotspot; the first hot spot is a fixed hot spot with the maximum RSSI (signal strength indicator) received in a target area; the hot spots comprise fixed hot spots and/or hot spots in the environment containing location information.
Optionally, the WiFi fingerprint database includes at least one of a number, a MAC address, a recording time, a signal strength mean, quality information, a recording duration, a longitude, and a latitude corresponding to each of the fingerprint hotspots.
Optionally, the processing apparatus further comprises:
the database updating module is used for updating the WiFi fingerprint database;
wherein, in the case that the environment of the fingerprint hotspot is not changed, the updating operation includes at least one of:
according to the time length proportion, the average value of the signal intensity is obtained;
according to the time length proportion, an average value is obtained for the quality information;
updating the recording duration;
and updating the recording time.
To achieve the above object, an embodiment of the present invention provides a network device, which includes a transceiver, a processor, a memory, and a program or instructions stored in the memory and executable on the processor; the processor, when executing the program or instructions, implements the processing method as described above.
To achieve the above object, an embodiment of the present invention provides a readable storage medium on which a program or instructions are stored, which when executed by a processor implement the steps in the processing method as described above.
The technical scheme of the invention has the following beneficial effects:
according to the method provided by the embodiment of the invention, the WiFi fingerprint database is obtained by processing the fingerprint hot spots, and various models do not need to be established aiming at the environment, so that the universality is better.
Drawings
FIG. 1 is a flow chart of a processing method according to an embodiment of the present invention;
FIG. 2 is a block diagram of a network device according to an embodiment of the present invention;
FIG. 3 is a block diagram of a processing apparatus according to another embodiment of the present invention;
fig. 4 is a block diagram of a network device according to another embodiment of the present invention.
Detailed Description
In order to make the technical problems, technical solutions and advantages of the present invention more apparent, the following detailed description is given with reference to the accompanying drawings and specific embodiments.
It should be appreciated that reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
In various embodiments of the present invention, it should be understood that the sequence numbers of the following processes do not mean the execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
In addition, the terms "system" and "network" are often used interchangeably herein.
In the embodiments provided herein, it should be understood that "B corresponding to a" means that B is associated with a from which B can be determined. It should also be understood that determining B from a does not mean determining B from a alone, but may be determined from a and/or other information.
Description is made of factors that influence the accuracy of a Received Signal Strength Indication (RSSI).
The existing WiFi-based fingerprint database construction mainly adopts a mode of uploading signal strength of other hotspots (APs) at a collection point, and the RSSI serving as the most main observation information generally considers that influence factors mainly include:
the interference of the electronic equipment with the same frequency, the working frequencies of the Bluetooth equipment, the wireless camera, the ZigBee equipment and the WiFi are the same, and the mutual interference can cause the deviation of the measured parameters (signal strength and the like) of radio waves;
due to the influence of multipath propagation effect, wireless signals in indoor environment are easily blocked by various indoor objects to generate phenomena of diffraction, reflection, diffraction and the like, so that the time delay of signal propagation is caused, and the frequency, amplitude or phase of the signals are changed, thereby causing the multipath effect;
interference of a human body, namely resonance absorption is the strongest when the radiation frequency and the natural frequency of the organism resonate, particularly, when an acquisition person carries out APs acquisition back to back, wiFi signals penetrate through the human body and are received by a mobile terminal, so that the signal intensity is weakened, and the positioning result is influenced;
the positioning environment is complex and diverse, the indoor environment changes due to the back-and-forth movement of people or the continuous change of articles, and in addition, the change of the quantity and the position of distributed APs and the change of the indoor layout can also have great influence on the positioning result.
That is, the environment and the human body constitute important factors affecting the RSSI accuracy.
As shown in fig. 1, a method for processing fingerprint information according to an embodiment of the present invention includes:
Here, the fingerprint hotspot may be a WiFi fixed AP (Wireless Access Point), where the fingerprint hotspot may also be understood as fingerprint data (or fingerprint information), and the fingerprint data may include a fixed hotspot in the target area and a hotspot containing location information in an environment where the target area is located.
Optionally, the step 101 includes:
and firstly, performing feature extraction on at least one fingerprint hotspot to obtain feature information corresponding to each fingerprint hotspot.
In this step, wiFi fingerprint information (i.e., fingerprint hotspots) can be classified by extracting characteristics of at least one fingerprint hotspot, i.e., obtaining characteristic data (i.e., characteristic information). In addition, the feature information may be sorted, for example, according to the importance of the feature information.
The key feature information may include: hotspot mean (i.e., hotspot rssi mean, e.g., expressed in rssi-mean), hotspot occurrence probability (i.e., hotspot rssi occurrence probability, e.g., expressed in rssi-apear), region hotspot identification information (e.g., expressed in rssi-reg), and hotspot standard deviation (i.e., hotspot rssi standard deviation, e.g., expressed in rssi-std).
And secondly, screening the at least one fingerprint hot spot according to the characteristic information to obtain a target fingerprint hot spot set.
In the step, fingerprint hot spots with characteristic information exceeding a threshold can be screened out by setting the threshold on the characteristic information, and a target fingerprint hot spot set is formed. For example, a threshold of the hotspot mean value (rssi-mean) is set to-80 dBm, and if the hotspot mean value (rssi-mean) corresponding to a certain fingerprint hotspot in the at least one fingerprint hotspot is less than or equal to-80 dBm, the fingerprint hotspot is deleted.
And 102, acquiring quality information of each fingerprint hotspot according to the characteristic information of each fingerprint hotspot in the target fingerprint hotspot set.
It should be noted that, after the target fingerprint hotspot set is determined in step 101, the quality information of each fingerprint hotspot may be determined according to the quality information of each fingerprint hotspot in the target fingerprint hotspot set, where the quality information may be comprehensive scoring information of the fingerprint hotspot.
103, according to the characteristic information and the quality information corresponding to each fingerprint hotspot, constructing a wireless fidelity (WiFi) fingerprint database, wherein the WiFi fingerprint database is used for WiFi fingerprint positioning.
In this embodiment, according to the acquired fingerprint data (i.e., the fingerprint hotspot), a fingerprint information database (i.e., a WiFi fingerprint database) may be constructed, so that data brought by various devices need not to be processed, which is simpler and more convenient and has higher versatility.
Optionally, the feature information comprises at least one of:
hot spot mean value;
probability of occurrence of hot spots;
region hotspot identification information;
standard deviation of hot spots.
The area hotspot identification information is used for indicating whether the area hotspot identification is an area hotspot identification.
Optionally, the screening operation comprises:
deleting a first fingerprint hotspot of the at least one fingerprint hotspot from the at least one fingerprint hotspot if the first fingerprint hotspot meets a first preset condition;
wherein the first preset condition comprises at least one of:
the hot spot mean value of the first fingerprint hot spot is smaller than or equal to a preset mean value;
the probability of the first fingerprint hotspot occurring is less than or equal to a preset probability.
For example, the hot spot mean (rssi-mean) and the hot spot occurrence probability (rssi-apear) may be thresholded, as shown in the following table:
| item | Threshold (THD) |
| rssi-mean | -80dBm |
| rssi-appear | 50％ |
According to the above table, the preset mean value (i.e. the threshold of the hot spot mean value) may be set to-80 dBm, the preset probability (i.e. the threshold of the hot spot occurrence probability) may be set to 50%, and the hot spot data (i.e. the fingerprint hot spots) lower than the above threshold may be removed, that is, the fingerprint hot spots with the hot spot mean value (rssi-mean) less than or equal to-80 dBm may be removed, and the fingerprint hot spots with the hot spot occurrence probability (rssi-apear) less than or equal to 50% may be removed.
As an alternative embodiment of the present invention, the hot spot data obtained after the above screening operation is shown in the following table:
| Mac | Lat | Lon | rssi-mean | rssi-appear | rssi-reg | Rssi-std |
| APs1 | Lat1 | Lon1 | -45.42 | 94.4％ | 1 | 1.36 |
| Aps2 | Lat2 | Lon2 | -48.16 | 100％ | 1 | 3.29 |
| … | ||||||
| APsN | LatN | LonN | -79.93 | 51.2％ | 0 | 1.62 |
wherein Mac represents the Mac address of the fingerprint hotspot, lat represents the latitude of the fingerprint hotspot, and Lon represents the longitude of the fingerprint hotspot.
Optionally, the obtaining, according to the feature information of each fingerprint hotspot in the target fingerprint hotspot set, quality information of each fingerprint hotspot includes:
according to the characteristic information, obtaining a power coefficient corresponding to each piece of characteristic information of each fingerprint hotspot;
and obtaining the quality information of the fingerprint hot spot according to the efficacy coefficient and the weight coefficient corresponding to the characteristic information.
For example, the quality information may be obtained by summing products of efficacy coefficients and weight coefficients corresponding to the feature information, and specifically, may be obtained according to the following formula:
wherein, APs score Representing quality information; f. of i Representing the efficacy coefficient; w _ coef i Representing the weight coefficients.
As an optional embodiment of the present invention, the quality information of each fingerprint hotspot is obtained according to the feature information of each fingerprint hotspot in the target fingerprint hotspot set, that is, the specific process of scoring each fingerprint hotspot in the target fingerprint hotspot set is as follows:
first, the hotspot data in the target fingerprint hotspot set is shown in the following table:
| rssi-mean | rssi-appear | rssi-reg | rssi-std | |
| upper limit | -45.42 | 100％ | 1 | 1.36 |
| lower limit | -79.93 | 51.2％ | 0 | 4.75 |
| APs1 | -45.42 | 94.4％ | 1 | 1.36 |
| Aps2 | -48.16 | 100％ | 1 | 2.29 |
| … | ||||
| APsN | -79.93 | 51.2％ | 0 | 1.62 |
wherein the upper limit represents the maximum value of certain characteristic information in the target fingerprint hotspot set; the lower limit represents the minimum value of certain characteristic information in the target fingerprint hotspot set.
The power coefficient may be calculated using a dimensionless first formula, where the first formula is:
wherein f is i Representing the efficacy coefficient; x is the number of i Representing certain characteristic information; x is the number of lower_limit The minimum value corresponding to the characteristic information is shown; x is the number of upper_limit The maximum value corresponding to the characteristic information is indicated.
For example, the hot spot mean (rssi-mean) of the fingerprint hot spot APs2 corresponds to an efficacy coefficient of:
wherein f is APs2_rssimean And representing the efficacy coefficient corresponding to the hot spot mean value of the fingerprint hot spot APs 2.
Similarly, according to the first formula, the corresponding efficacy coefficient is calculated for each index (i.e., feature information) in each fingerprint data.
It should be noted that for parameters with values such as standard deviation of hot spots (rsi-std) that represent a lower limit, the calculation needs to be performed by taking the reciprocal, that is:
wherein f is APs2_rssistd And the efficacy coefficient corresponding to the standard deviation of the hot spot representing the fingerprint hot spot APs 2.
Then, for the fingerprint hotspot Aps2, the power coefficients of the respective categories (i.e. the power coefficients corresponding to the respective feature information) are respectively shown in the following table:
| f APs2_rssimean | 0.9061 |
| f APs2_appear | 1 |
| f APs2_reg | 1 |
| f APs2_std | 0.4310 |
then, adding a weight coefficient to obtain quality information (namely the total score of the fingerprint hotspots) of the fingerprint hotspots (namely the AP):
optionally, the processing method further includes:
setting a weight coefficient for each piece of feature information according to the importance ranking of different pieces of feature information;
wherein the sum of the weight coefficients corresponding to the feature information is 1.
It should be noted that, in the embodiment of the present invention, in consideration of the fact that the four pieces of key feature information have different importance, different weight coefficients (w _ coef) may be respectively assigned to the four pieces of key feature information, where the sum of the weight coefficients is 1, for example, the weight coefficients of each index are assigned as shown in the following table:
| rssi-mean | rssi-appear | rssi-reg | rssi-std | |
| w_coef | 0.3 | 0.4 | 0.2 | 0.1 |
finally, the weighting coefficient is multiplied by the corresponding efficacy coefficient, so as to obtain an AP total score (namely quality information) with an interval between [0,1 ]. Wherein, the total score is calculated by adopting a second formula, and the second formula is as follows:
wherein, APs score Representing quality information; f. of i Representing the efficacy coefficient; w _ coef i Representing the weight coefficients.
Specifically, for the fingerprint hotspot Aps2, the following data can be calculated according to the above process:
wherein total represents the quality information.
In this embodiment, the fingerprint hotspots may be scored, and the weight coefficient may be added to obtain the total score of the fingerprint hotspots.
Optionally, before obtaining the target fingerprint hotspot set according to at least one fingerprint hotspot, the processing method further includes:
selecting a hotspot in a preset radius range by taking the first hotspot as a circle center as the at least one fingerprint hotspot; the first hot spot is a fixed hot spot with the maximum RSSI (signal strength indicator) received in a target area; the hot spots comprise fixed hot spots and/or hot spots in the environment containing location information.
It should be noted that a fingerprint collection point is understood as a collection point where fingerprint data collection can be performed in a certain area, and the collection time at a certain fingerprint collection point is usually 100 seconds or more. The fingerprint data may include a built area fixed hotspot and a hotspot containing location information in the environment, and the fingerprint data in the environment has a certain jump point, which needs to be selected.
Specifically, since the environment contains less data of the location and the fixed hot spots are concentrated, the number of the location jumping points is much smaller than that of the hot spots in the core area. Based on this fact, in the embodiment of the present invention, the centroid may be selected as a hot spot with the largest signal strength RSSI in the fixed hot spots in the area, and the hot spot with a radius smaller than a preset radius (for example, 1 km) may be selected as a fingerprint hot spot with the hot spot as a center, and the rest may be removed.
Optionally, the WiFi fingerprint database includes a number (e.g., represented by Num), a MAC address (e.g., represented by MAC), a recording time (e.g., represented by time), a signal strength mean, and quality information (e.g., represented by APs) corresponding to each of the fingerprint hotspots score Presentation), recording duration (e.g., in Times), longitude, and latitude.
As an alternative embodiment of the present invention, the specific information in the WiFi fingerprint database is shown in the following table:
optionally, the processing method further includes:
updating the WiFi fingerprint database;
wherein, in the case that the environment is not changed, the updating operation comprises at least one of the following:
according to the time length proportion, the average value of the signal intensity is obtained;
according to the time length proportion, an average value is obtained for the quality information;
updating the recording duration;
and updating the recording time.
In this embodiment, the update mechanism of the WiFi fingerprint database may be determined by a change of the environment, where the change of the environment includes: the change of the area fixed hot spot (such as the position change of the fixed hot spot), the change of the main pattern of the area, the relocation of large-sized furniture in the area, and the like.
In case the environment is not changed: for the mean of the signal intensities (rssi-mean) and the total score (i.e. the composite score APs) score ) Calculating an average value of the new fingerprint information and the old fingerprint information according to the time length proportion; accumulating the recording durations (Times); change the recording time (time) to a new time; the remaining parameters were unchanged.
In case of a change of environment: and re-inputting the WiFi fingerprint database according to the new fingerprint information, and covering the original information.
In the embodiment, data brought by various devices do not need to be processed in a distinguishing manner, and an updating mechanism of the WiFi fingerprint database is established for data normalization weights in different time periods, and is simpler and more convenient.
According to the processing method provided by the embodiment of the invention, the fingerprint database is obtained by processing the fingerprint hot spots, and a plurality of models do not need to be established aiming at the environment, so that the universality is better; by normalizing the weight of the data in different time periods, data brought by various devices do not need to be processed differently, and the updating mechanism of the fingerprint database is simpler and more convenient.
As shown in fig. 2, a network device 200 according to an embodiment of the present invention includes a processor 210 and a transceiver 220, where the processor 210 is configured to:
obtaining a target fingerprint hot spot set according to at least one fingerprint hot spot;
acquiring quality information of each fingerprint hotspot according to the characteristic information of each fingerprint hotspot in the target fingerprint hotspot set;
and constructing a wireless fidelity WiFi fingerprint database according to the characteristic information and the quality information corresponding to each fingerprint hotspot, wherein the WiFi fingerprint database is used for WiFi fingerprint positioning.
Optionally, when obtaining the target fingerprint hot spot set according to at least one fingerprint hot spot, the processor 210 is specifically configured to:
performing feature extraction on at least one fingerprint hotspot to obtain feature information corresponding to each fingerprint hotspot;
and screening the at least one fingerprint hot spot according to the characteristic information to obtain a target fingerprint hot spot set.
Optionally, the feature information comprises at least one of:
hot spot mean value;
probability of occurrence of hot spots;
region hotspot identification information;
standard deviation of hot spots.
Optionally, the screening operation comprises:
deleting a first fingerprint hotspot of the at least one fingerprint hotspot from the at least one fingerprint hotspot if the first fingerprint hotspot meets a first preset condition;
wherein the first preset condition comprises at least one of:
the hot spot mean value of the first fingerprint hot spot is smaller than or equal to a preset mean value;
the probability of the first fingerprint hotspot occurring is less than or equal to a preset probability.
Optionally, when the processor 210 acquires the quality information of each fingerprint hotspot according to the feature information of each fingerprint hotspot in the target fingerprint hotspot set, the processor is specifically configured to:
according to the characteristic information, obtaining an efficacy coefficient corresponding to each characteristic information of each fingerprint hotspot;
and obtaining the quality information of the fingerprint hot spot according to the efficacy coefficient and the weight coefficient corresponding to the characteristic information.
Optionally, the processor 210 is further configured to:
setting a weight coefficient for each piece of feature information according to the importance ranking of different pieces of feature information;
and the sum of the weight coefficients corresponding to the characteristic information is 1.
Optionally, before obtaining the target fingerprint hotspot set according to the at least one fingerprint hotspot, the processor 210 is further configured to:
selecting a hotspot in a preset radius range by taking the first hotspot as a circle center as the at least one fingerprint hotspot; the first hot spot is a fixed hot spot with the maximum RSSI (signal strength indicator) received in a target area; the hot spots comprise fixed hot spots and/or hot spots in the environment containing location information.
Optionally, the WiFi fingerprint database includes at least one of a number, a MAC address, a recording time, a signal strength mean, quality information, a recording duration, a longitude, and a latitude corresponding to each of the fingerprint hotspots.
Optionally, the processor 210 is further configured to:
updating the WiFi fingerprint database;
wherein, in the case that the environment of the fingerprint hotspot is not changed, the updating operation includes at least one of:
according to the time length proportion, the average value of the signal intensity is obtained;
according to the time length proportion, an average value is obtained for the quality information;
updating the recording duration;
and updating the recording time.
The network device of the embodiment obtains the fingerprint database by processing the fingerprint hotspots without establishing various models aiming at the environment, so that the universality is better; by normalizing the weight of the data in different time periods, data brought by various devices do not need to be processed differently, and the updating mechanism of the fingerprint database is simpler and more convenient.
As shown in fig. 3, an apparatus for processing fingerprint information according to an embodiment of the present invention includes:
the first processing module 301 is configured to obtain a target fingerprint hot spot set according to at least one fingerprint hot spot;
a second processing module 302, configured to obtain quality information of each fingerprint hotspot according to feature information of each fingerprint hotspot in the target fingerprint hotspot set;
a third processing module 303, configured to construct a WiFi fingerprint database for WiFi fingerprint positioning according to the feature information and the quality information corresponding to each fingerprint hotspot.
Optionally, the first processing module 301 includes:
the characteristic extraction unit is used for extracting the characteristics of at least one fingerprint hotspot to obtain characteristic information corresponding to each fingerprint hotspot;
and the data screening unit is used for screening the at least one fingerprint hot spot according to the characteristic information to obtain a target fingerprint hot spot set.
Optionally, the feature information comprises at least one of:
hot spot mean value;
probability of occurrence of hot spots;
region hotspot identification information;
standard deviation of hot spots.
Optionally, the data filtering unit includes:
the data screening subunit is configured to, when a first fingerprint hotspot in the at least one fingerprint hotspot meets a first preset condition, delete the first fingerprint hotspot from the at least one fingerprint hotspot;
wherein the first preset condition comprises at least one of:
the hot spot mean value of the first fingerprint hot spot is smaller than or equal to a preset mean value;
the probability of the first fingerprint hotspot occurring is less than or equal to a preset probability.
Optionally, the second processing module 302 includes:
the first processing unit is used for obtaining an efficacy coefficient corresponding to each piece of feature information of each fingerprint hotspot according to the feature information;
and the second processing unit is used for obtaining the quality information of the fingerprint hot spot according to the efficacy coefficient and the weight coefficient corresponding to the characteristic information.
Optionally, the processing apparatus further comprises:
the weight setting module is used for setting a weight coefficient for each piece of characteristic information according to the importance sequence of different pieces of characteristic information;
wherein the sum of the weight coefficients corresponding to the feature information is 1.
Optionally, the processing apparatus further comprises:
the hotspot determining module is used for selecting a hotspot in a preset radius range by taking the first hotspot as a circle center as the at least one fingerprint hotspot; the first hot spot is a fixed hot spot with the maximum RSSI (signal strength indicator) received in a target area; the hot spots comprise fixed hot spots and/or hot spots in the environment containing location information.
Optionally, the WiFi fingerprint database includes at least one of a number, a MAC address, a recording time, a signal strength mean, quality information, a recording duration, a longitude, and a latitude corresponding to each of the fingerprint hotspots.
Optionally, the processing apparatus further comprises:
the database updating module is used for updating the WiFi fingerprint database;
wherein, in the case that the environment of the fingerprint hotspot is not changed, the updating operation includes at least one of:
according to the time length proportion, the average value of the signal intensity is obtained;
according to the time length proportion, the quality information is averaged;
updating the recording duration;
and updating the recording time.
According to the processing device, the fingerprint database is obtained by processing the fingerprint hotspots, multiple models do not need to be established aiming at the environment, and the universality is better; by normalizing the weight of the data in different time periods, data brought by various devices do not need to be processed differently, and the updating mechanism of the fingerprint database is simpler and more convenient.
A network device according to another embodiment of the present invention, as shown in fig. 4, includes a transceiver 410, a processor 400, a memory 420, and a program or instructions stored in the memory 420 and executable on the processor 400; the processor 400, when executing the program or instructions, implements the methods described above.
The transceiver 410 is used for receiving and transmitting data under the control of the processor 400.
Where in fig. 4, the bus architecture may include any number of interconnected buses and bridges, with various circuits of one or more processors, represented by processor 400, and memory, represented by memory 420, being linked together. The bus architecture may also link together various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. The bus interface provides an interface. The transceiver 410 may be a number of elements including a transmitter and a receiver that provide a means for communicating with various other apparatus over a transmission medium. The processor 400 is responsible for managing the bus architecture and general processing, and the memory 420 may store data used by the processor 400 in performing operations.
The readable storage medium of the embodiment of the present invention stores a program or an instruction thereon, and the program or the instruction, when executed by the processor, implements the steps in the processing method described above, and can achieve the same technical effects, and in order to avoid repetition, the details are not described here again. The computer-readable storage medium may be a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk.
It is further noted that the terminals described in this specification include, but are not limited to, smart phones, tablets, etc., and that many of the functional components described are referred to as modules in order to more particularly emphasize their implementation independence.
In embodiments of the present invention, modules may be implemented in software for execution by various types of processors. An identified module of executable code may, for instance, comprise one or more physical or logical blocks of computer instructions which may, for instance, be constructed as an object, procedure, or function. Nevertheless, the executables of an identified module need not be physically located together, but may comprise disparate instructions stored in different bits which, when joined logically together, comprise the module and achieve the stated purpose for the module.
Indeed, a module of executable code may be a single instruction, or many instructions, and may even be distributed over several different code segments, among different programs, and across several memory devices. Similarly, operational data may be identified within modules, and may be embodied in any suitable form and organized within any suitable type of data structure. The operational data may be collected as a single data set, or may be distributed over different locations including over different storage devices, and may exist, at least partially, merely as electronic signals on a system or network.
When a module can be implemented by software, considering the level of existing hardware technology, a module implemented by software may build a corresponding hardware circuit to implement a corresponding function, without considering cost, and the hardware circuit may include a conventional Very Large Scale Integration (VLSI) circuit or a gate array and an existing semiconductor such as a logic chip, a transistor, or other discrete components. A module may also be implemented in programmable hardware devices such as field programmable gate arrays, programmable array logic, programmable logic devices or the like.
The exemplary embodiments described above are described with reference to the drawings, and many different forms and embodiments of the invention may be made without departing from the spirit and teaching of the invention, therefore, the invention is not to be construed as limited to the exemplary embodiments set forth herein. Rather, these exemplary embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. In the drawings, the size and relative sizes of elements may be exaggerated for clarity. The terminology used herein is for the purpose of describing particular example embodiments only and is not intended to be limiting. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. Unless otherwise indicated, a range of values, when stated, includes the upper and lower limits of the range and any subranges therebetween.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (13)
1. A method for processing fingerprint information, comprising:
obtaining a target fingerprint hot spot set according to at least one fingerprint hot spot;
acquiring quality information of each fingerprint hotspot according to the characteristic information of each fingerprint hotspot in the target fingerprint hotspot set;
and constructing a wireless fidelity WiFi fingerprint database according to the characteristic information and the quality information corresponding to each fingerprint hotspot, wherein the WiFi fingerprint database is used for WiFi fingerprint positioning.
2. The processing method according to claim 1, wherein the obtaining a target fingerprint hotspot set according to at least one fingerprint hotspot comprises:
performing feature extraction on at least one fingerprint hotspot to obtain feature information corresponding to each fingerprint hotspot;
and screening the at least one fingerprint hot spot according to the characteristic information to obtain a target fingerprint hot spot set.
3. The processing method of claim 2, wherein the feature information comprises at least one of:
hot spot mean value;
probability of occurrence of hot spots;
area hot spot identification information;
standard deviation of hot spots.
4. The process of claim 3, wherein the screening operation comprises:
deleting a first fingerprint hotspot of the at least one fingerprint hotspot from the at least one fingerprint hotspot if the first fingerprint hotspot meets a first preset condition;
wherein the first preset condition comprises at least one of:
the hot spot mean value of the first fingerprint hot spot is smaller than or equal to a preset mean value;
the probability of the first fingerprint hotspot occurring is less than or equal to a preset probability.
5. The processing method according to claim 1, wherein the obtaining quality information of each fingerprint hotspot according to the feature information of each fingerprint hotspot in the target fingerprint hotspot set comprises:
according to the characteristic information, obtaining an efficacy coefficient corresponding to each characteristic information of each fingerprint hotspot;
and obtaining the quality information of the fingerprint hot spot according to the efficacy coefficient and the weight coefficient corresponding to the characteristic information.
6. The processing method of claim 1, further comprising:
setting a weight coefficient for each piece of feature information according to the importance ranking of different pieces of feature information;
wherein the sum of the weight coefficients corresponding to the feature information is 1.
7. The processing method according to claim 1, wherein before obtaining the target fingerprint hotspot set based on at least one fingerprint hotspot, the processing method further comprises:
selecting a hotspot in a preset radius range by taking the first hotspot as a circle center as the at least one fingerprint hotspot; the first hot spot is a fixed hot spot with the maximum RSSI (signal strength indicator) received in a target area; the hot spots comprise fixed hot spots and/or hot spots containing position information in the environment.
8. The processing method of claim 1, wherein the WiFi fingerprint database comprises at least one of a number, a MAC address, a recording time, a signal strength mean, quality information, a recording duration, a longitude, and a latitude corresponding to each of the fingerprint hotspots.
9. The processing method according to claim 8, further comprising:
updating the WiFi fingerprint database;
wherein, in the case that the environment of the fingerprint hotspot is not changed, the updating operation includes at least one of:
according to the time length proportion, the average value of the signal intensity is obtained;
according to the time length proportion, the quality information is averaged;
updating the recording duration;
and updating the recording time.
10. A fingerprint information processing device is characterized by comprising
The first processing module is used for obtaining a target fingerprint hot spot set according to at least one fingerprint hot spot;
the second processing module is used for acquiring the quality information of each fingerprint hotspot according to the characteristic information of each fingerprint hotspot in the target fingerprint hotspot set;
and the third processing module is used for constructing a wireless fidelity WiFi fingerprint database according to the characteristic information and the quality information corresponding to each fingerprint hotspot, and the WiFi fingerprint database is used for WiFi fingerprint positioning.
11. A network device, comprising: a transceiver and a processor; the processor is configured to:
obtaining a target fingerprint hot spot set according to at least one fingerprint hot spot;
acquiring quality information of each fingerprint hotspot according to the characteristic information of each fingerprint hotspot in the target fingerprint hotspot set;
and constructing a wireless fidelity WiFi fingerprint database according to the characteristic information and the quality information corresponding to each fingerprint hotspot, wherein the WiFi fingerprint database is used for WiFi fingerprint positioning.
12. A network device, comprising: a transceiver, a processor, a memory, and a program or instructions stored on the memory and executable on the processor; wherein the processor, when executing the program or instructions, implements the processing method of any of claims 1 to 9.
13. A readable storage medium on which a program or instructions are stored, which, when executed by a processor, implement the steps in the processing method of any one of claims 1 to 9.
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