CN118625360B - A low-power multi-mode positioning platform and positioning method - Google Patents

Abstract

The invention discloses a multimode positioning platform with low power consumption and a positioning method, which comprises a multimode data acquisition module and a positioning enhancement module, relates to the technical field of communication positioning, solves the technical problems that the positioning is delayed and unstable due to external factors, the positioning is inaccurate in a high concurrency period, and further reduces the user experience, and particularly performs fusion processing on acquired data by performing data processing on the acquired data, therefore, the accuracy of the data in the utilization process can be improved, the accuracy of multi-mode positioning can be further improved, the data are classified in different use periods, the data are independently analyzed for high concurrency periods and non-high concurrency periods, the corresponding signal intensity and environment information are combined for comprehensive analysis and processing, meanwhile, the parameter information of the positioning information is combined for selecting different transmission frequency bands, the influence caused by external factors is reduced, and the accuracy of positioning is improved.

Description

A low-power multi-mode positioning platform and positioning method

Technical Field

The present invention relates to the field of communication positioning technology, and in particular to a low-power multi-mode positioning platform and a positioning method.

Background Art

At present, when a mobile terminal is located outdoors or in a scene where global positioning system (GPS) satellites are visible, GPS positioning technology and gpsOne positioning technology can be used to locate the mobile terminal.

According to Chinese patent application number CN201110197912.9, a positioning method and system based on multi-mode signals and a positioning platform are disclosed. The method of the patent includes: the positioning platform receives an uplink message sent by the terminal to be positioned after obtaining the multi-mode signal at the location, the multi-mode signal includes a WiFi signal and a mobile communication network signal, the uplink message includes the multi-mode signal information obtained by the terminal to be positioned, including the type and parameters of the multi-mode signal, the type of the multi-mode signal includes a WiFi signal or a mobile communication network signal, and the corresponding multi-mode signal parameters include WiFi parameters and base station parameters of the mobile communication network; the positioning platform obtains the indoor position coordinates of the building corresponding to the standard feature fingerprint matching the multi-mode signal information, and uses the indoor position coordinates of the building as the indoor positioning result of the terminal to be positioned.

The technical solution of the above patent matches the multi-mode signal information obtained by the terminal to be located with the standard feature fingerprint in the building fingerprint library, obtains the indoor position coordinates of the building corresponding to the standard feature fingerprint matched by the multi-mode signal information, and uses the indoor position coordinates of the building as the indoor positioning result of the terminal to be located. Since the multi-mode signal parameters including WiFi parameters and base station parameters in the mobile communication network are used at the same time to locate the terminal to be located, the accuracy of indoor positioning is improved.

However, the above technical solution does not take into account the impact of positioning during high concurrency periods. For positioning analysis during high concurrency periods, external factors may cause positioning delays or unstable signals. Secondly, due to different positioning environments of users, positioning in more complex environments may result in inaccurate positioning, which will further cause problems for users and reduce their experience.

Summary of the invention

In view of the shortcomings of the prior art, the present invention provides a low-power multi-mode positioning platform and positioning method, which solves the problem that external factors can cause positioning delays and signal instability, and positioning during high concurrency periods causes inaccurate positioning, further reducing the user experience.

To achieve the above objectives, the present invention is implemented through the following technical solutions: a low-power multi-mode positioning platform, comprising:

Multi-mode data acquisition module, used to transmit data from different satellite systems to the data fusion analysis module;

The data fusion analysis module is used to process the acquired satellite system data to obtain fused data, and transmit the fused data to the information positioning analysis module;

The information positioning analysis module is used to classify the fused data in combination with the historical data to obtain high-concurrency data and non-high-concurrency data, and transmit the two to the positioning enhancement analysis module;

The positioning enhancement analysis module is used to analyze the acquired high-concurrency data and non-high-concurrency data. For high-concurrency data, the signal strength and processing frequency band are analyzed and screened, and the frequency band is determined in combination with the stable duration of the standard signal strength. For non-high-concurrency data, the current environment and processing frequency band are analyzed, and the frequency band value is calculated. At the same time, the determined frequency band is screened according to the frequency band value, and then the determined frequency band is transmitted to the positioning information output module.

As a further solution of the present invention: the specific manner in which the data fusion analysis module processes the satellite system data is as follows:

The data format corresponding to the satellite data is obtained, and the data format that the current system can recognize is obtained and recorded as the recognition conversion format, and the conversion loss corresponding to the recognition conversion format is obtained. At the same time, the recognition conversion format corresponding to the minimum conversion loss is selected as the standard format, and then the satellite data is converted to the standard format to obtain the fused data.

As a further solution of the present invention: the specific manner in which the information location analysis module analyzes the fusion data is as follows:

The historical data within the time period t is obtained, and the positioning time of the fused data is classified into high-concurrency time periods and non-high-concurrency time periods. The fused data is further classified according to the positioning time classification to obtain high-concurrency data and non-high-concurrency data.

As a further solution of the present invention: the analysis of non-high-concurrency data by the positioning enhancement analysis module is as follows:

The positioning information classified as non-high-concurrency data is obtained and recorded as the analysis module. Then, the signal strength and processing frequency band corresponding to the analysis target are obtained, and the processing frequency band is recorded as i, where i=1, 2, ..., 5. At the same time, the signal strength corresponding to the processing frequency band Li is obtained and recorded as QLi;

Then, the processing frequency band and signal strength corresponding to the positioning information are obtained and recorded as the current frequency band and current strength respectively. The processing frequency band corresponding to the processing frequency band signal strength QLi greater than the current strength is recorded as the frequency band to be analyzed a, and a=1, 2, ..., c.

The standard signal strength is obtained, and the stable duration of the standard signal strength corresponding to the frequency band a to be analyzed is obtained as Ta, and the stable duration Ta is compared with the threshold Ty, and the value of the threshold Ty is set by the operator. Then, the frequency band a to be analyzed whose stable duration Ta is greater than the threshold Ty is screened out and recorded as the pre-selected frequency band, and the pre-selected frequency band corresponding to the least concurrent users in the pre-selected frequency band is selected as the determined frequency band.

As a further solution of the present invention: the positioning enhanced analysis module analyzes high-concurrency data in the following manner:

The real-time data transmitted is obtained, and the real-time data includes the current environment corresponding to the positioning information. The current frequency band of the positioning information is obtained and recorded as the target frequency band, and all the processing frequency bands Li are obtained. Then, the user proportion values in the processing frequency bands Li under different current environments are calculated and recorded as the environment proportion values. At the same time, the signal strength average values corresponding to different current environments are calculated to obtain the environment signal average value;

The current environment corresponding to the positioning information is recorded as the comparison environment, and the processing frequency band corresponding to the non-existent comparison environment is screened out and recorded as the pre-processing frequency band b, where b=1, 2, ..., o. At the same time, the number of concurrent users corresponding to the pre-processing frequency band b is obtained and recorded as Kb and the signal strength mean Qbp.

The frequency band delay time corresponding to the pre-processing frequency band b is obtained and recorded as YTb, and the obtained data is substituted into the formula The frequency band value Rb corresponding to the preprocessing frequency band b is calculated, and then the frequency band values Rb are sorted from large to small, and the preprocessing frequency band corresponding to the smallest frequency band value Rb is selected and recorded as the determined frequency band, and the determined frequency band information is generated.

As a further solution of the present invention: the positioning information output module is used to correct and adjust the positioning information according to the determined frequency band.

As a further solution of the present invention: a low-power multi-mode positioning method, the method specifically comprises the following steps:

Step 1: uniformly convert the data formats of multiple satellite data to obtain fused data;

Step 2: Analyze the generation time of the fused data in combination with historical data, and classify the fused data into high-concurrency data and non-high-concurrency data;

Step 3: Analyze the signal strength and processing frequency band of high-concurrency data, and determine the frequency band based on the stable duration of the standard signal strength;

Step 4: Analyze the current environment of high-concurrency data and select the determined frequency band based on the signal strength corresponding to the current environment.

The present invention provides a low-power multi-mode positioning platform and positioning method. Compared with the prior art, it has the following beneficial effects:

The present invention processes the acquired data, specifically performs data fusion processing, thereby improving the accuracy of the data in the utilization process, and further improving the accuracy of multi-mode positioning. Secondly, the data is classified into different usage time periods, and high concurrency time periods and non-high concurrency time periods are analyzed separately. The data is comprehensively analyzed and processed in combination with the signal strength and environmental information corresponding to the positioning. At the same time, different transmission frequency bands are selected in combination with the parameter information of the positioning information itself, thereby reducing the impact of external factors and improving the accuracy of positioning.

BRIEF DESCRIPTION OF THE DRAWINGS

Fig. 1 is a schematic diagram of the system of the present invention;

FIG. 2 is a flow chart of the method of the present invention.

DETAILED DESCRIPTION

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

Please refer to Figure 1. The present application provides a low-power multi-mode positioning platform, and the multi-mode positioning platform includes: a multi-mode data acquisition module, a data fusion analysis module, an information positioning analysis module, a positioning enhancement module and a positioning information output module, wherein the positioning enhancement analysis module will obtain the corresponding real-time data for comprehensive analysis.

As embodiment 1 of the present invention

The multi-mode data acquisition module is used to acquire data from different satellite systems, including data generated by multiple global satellite navigation systems such as GPS, Beidou, and GLONASS, and transmit the acquired satellite data to the data fusion analysis module.

The data fusion analysis module is used to perform fusion analysis on satellite data, and the specific fusion analysis method is as follows:

The data format corresponding to the satellite data is obtained, and the data format that the current system can recognize is obtained and recorded as the recognition conversion format. The current system here is represented as a multi-mode positioning platform. The data format is processed through this platform, and the conversion loss corresponding to the recognition conversion format is obtained. The conversion loss represents the information loss or signal quality degradation that may be encountered in the process of converting data from one format to another, such as matching impedance. In wired signal transmission, the conversion between differential mode and common mode will reduce the quality of the transmission signal. At the same time, the recognition conversion format corresponding to the smallest conversion loss is selected as the standard format, and then the satellite data is converted to the standard format to obtain fused data, and the fused data is transmitted to the information positioning analysis module.

Combined with actual analysis, data fusion can include multiple aspects, including feature fusion, data integration, data matching, data merging, data aggregation, building data lakes and using data warehouses. Feature fusion is a method of integrating features from different data sources; data integration merges data from multiple data sources into a unified data set through cleaning, conversion and integration techniques. In this process, data in different formats need to be processed uniformly to ensure consistency and accuracy; data matching finds the same or similar data records between different data sources; data merging means merging data from different data sources into a larger data set; data aggregation means summarizing data from different data sources to create a higher-level data set.

The information location analysis module is used to analyze the acquired fusion data, obtain the generation time corresponding to the fusion data, and perform judgment analysis based on the generation time. The specific judgment analysis method is as follows:

The historical data within the time period t is obtained, and the specific value of the time period t is set by the operator. The user positioning time is classified according to the historical data. Specifically, the user positioning time is classified into a high-concurrency time period and a non-high-concurrency time period. The specific high-concurrency time period indicates that the amount of data processed simultaneously by the system in this time period is large. Then, the generation time of the fused data is obtained, and the fused data also represents the positioning information. The generation time of the fused data is judged to obtain high-concurrency data or non-high-concurrency data. At the same time, the generated high-concurrency data or non-high-concurrency data is transmitted to the positioning enhancement analysis module.

Combined with actual analysis, the basic information corresponding to high concurrency data includes response time, QPS (number of response requests per second), throughput, number of concurrent users and total page views. Regarding QPS, generally speaking, when QPS is less than 50, it is a small website, the basic server can bear it, and no optimization is needed. When QPS reaches 100, it is a data query bottleneck, and optimization measures such as database cache layer and database load balancing may be needed.

The positioning enhanced analysis module is used to analyze the acquired non-high-concurrency data, and the specific analysis method is as follows:

The positioning information classified as non-high-concurrency data is obtained, and the positioning information here has the same meaning as the fusion data. The positioning information is obtained and recorded as the analysis target at the same time. Then, the signal strength and processing frequency band corresponding to the analysis target are obtained, and the processing frequency bands include: very low frequency (VLF), low frequency (LF), medium frequency (MF), high frequency (HF) and very high frequency (VHF). The processing frequency band is recorded as i, and i=1, 2, ..., 5. The specific labels from 1 to 5 correspond to very low frequency to very high frequency respectively. At the same time, the signal strength corresponding to the processing frequency band Li is obtained and recorded as QLi. Then, the processing frequency band and signal strength corresponding to the positioning information are obtained and recorded as the current frequency band and the current strength respectively. The processing frequency band corresponding to the signal strength QLi of the screening processing frequency band is greater than the current strength and is recorded as the frequency band to be analyzed a, and a=1, 2, ..., c. Next, c represents the number of frequency bands to be analyzed, where the value of c is determined according to the number of frequency bands to be analyzed. For example, if the number of frequency bands to be analyzed is 4, the value of c is 4;

Then, the signal stability of the frequency band a to be analyzed is analyzed, and the signal stability is analyzed in the following way: the standard signal strength is obtained, and the specific value of the standard signal strength here is set by the operator, and then the stable duration of the standard signal strength corresponding to all the frequency bands a to be analyzed is obtained and recorded as Ta, and at the same time, the frequency bands a to be analyzed whose stable duration Ta is greater than the threshold Ty are screened out and recorded as pre-selected frequency bands, and the pre-selected frequency band corresponding to the least concurrent users in the pre-selected frequency bands is selected as the determined frequency band, and the determined frequency band information is generated and transmitted to the positioning information output module.

Combined with actual analysis, very low frequency (VLF): the frequency range is between 3kHz and 30kHz, and the corresponding electromagnetic wave wavelength is very long wave, ranging from 100km to 10km, low frequency (LF): the frequency range is between 30kHz and 300kHz, and the corresponding electromagnetic wave wavelength is long wave, ranging from 10km to 1km, medium frequency (MF): the frequency range is between 300kHz and 3000kHz, and the corresponding electromagnetic wave wavelength is medium wave, ranging from 1000m to 100m, high frequency (HF): the frequency range is between 3MHz and 30MHz, and the corresponding electromagnetic wave wavelength is short wave, ranging from 100m to 10m, very high frequency (VHF): the frequency range is between 30MHz and 300MHz, and the corresponding electromagnetic wave wavelength is meter wave, ranging from 10m to 1m.

The positioning information output module is used to adjust the positioning information according to the determined frequency band information.

As the second embodiment of the present invention, it is implemented on the basis of the first embodiment, and is different from the first embodiment in that the positioning enhancement analysis module analyzes the high-concurrency data.

The positioning enhanced analysis module is used to analyze high-concurrency data and obtain the transmitted real-time data at the same time. The specific real-time data includes the current environment corresponding to the positioning information, and the current environment represents a simple environment, a complex environment and other environments. The positioning information is analyzed in combination with the current environment, and the specific analysis method is as follows:

The current frequency band of the positioning information is obtained and recorded as the target frequency band, and all the processing frequency bands Li are obtained. Then, the user proportion values in the processing frequency band Li under different current environments are calculated and recorded as the environment proportion values. At the same time, the signal strength means corresponding to different current environments are calculated to obtain the environment signal mean value. Specifically, for all processing frequency bands, the positioning environment conditions used by their users are classified and processed, and the environment proportion values in the same frequency band are calculated according to different environment classifications, and then the signal strength mean value in the same environment is calculated.

Then, the current environment corresponding to the positioning information is obtained and recorded as the comparison environment, and the processing frequency band i is screened according to the comparison environment. The specific screening method is: the processing frequency band corresponding to the non-existent comparison environment is screened and recorded as the pre-processing frequency band b. Here, if the environment corresponding to the current positioning information is a simple environment, the corresponding environment in the pre-processing frequency band obtained by screening does not include the simple environment. In other words, the pre-processing frequency bands remaining after the simple environment is excluded, and b=1, 2, ..., o, where o represents the number of pre-processing frequency bands. At the same time, the number of concurrent users corresponding to the pre-processing frequency band b is obtained, recorded as Kb and the signal strength mean Qbp. Then, the frequency band delay time corresponding to the pre-processing frequency band b is obtained, recorded as YTb, and the obtained data is substituted into the formula Calculate and obtain the frequency band value Rb corresponding to the preprocessing frequency band b;

Then, the frequency band values Rb are sorted from large to small, and the preprocessed frequency band corresponding to the smallest frequency band value Rb is selected and recorded as the determined frequency band, and the determined frequency band information is generated and transmitted to the positioning information output module.

The positioning information output module is used to perform positioning analysis based on the determined frequency band information.

As the third embodiment of the present invention, the focus is on combining the implementation processes of the first embodiment and the second embodiment.

Please refer to FIG2 , a low-power multi-mode positioning method, the method specifically includes the following steps:

Step 1: uniformly convert the data formats of multiple satellite data to obtain fused data;

Step 2: Analyze the generation time of the fused data in combination with historical data, and classify the fused data into high-concurrency data and non-high-concurrency data;

Step 3: Analyze the signal strength and processing frequency band of high-concurrency data, and determine the frequency band based on the stable duration of the standard signal strength;

Step 4: Analyze the current environment of high-concurrency data and select the determined frequency band based on the signal strength corresponding to the current environment.

Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims (5)

1. A low-power multi-mode positioning platform, characterized in that the platform specifically includes the following modules: Multi-mode data acquisition module, used to transmit data from different satellite systems to the data fusion analysis module; The data fusion analysis module is used to process the acquired satellite system data to obtain fused data, and transmit the fused data to the information positioning analysis module; The information positioning analysis module is used to classify the fused data in combination with the historical data to obtain high-concurrency data and non-high-concurrency data, and transmit the two to the positioning enhancement analysis module; The positioning enhancement analysis module is used to analyze the acquired high-concurrency data and non-high-concurrency data. For high-concurrency data, the signal strength and processing frequency band are analyzed and screened, and the frequency band is determined in combination with the stable duration of the standard signal strength. For non-high-concurrency data, the current environment and processing frequency band are analyzed, and the frequency band value is calculated. At the same time, the determined frequency band is screened according to the frequency band value, and then the determined frequency band is transmitted to the positioning information output module. The analysis of non-high-concurrency data is as follows: The positioning information classified as non-high-concurrency data is obtained and recorded as the analysis module. Then, the signal strength and processing frequency band corresponding to the analysis target are obtained, and the processing frequency band is recorded as i, where i=1, 2, ..., 5. At the same time, the signal strength corresponding to the processing frequency band Li is obtained and recorded as QLi; Then, the processing frequency band and signal strength corresponding to the positioning information are obtained and recorded as the current frequency band and current strength respectively, and the processing frequency band corresponding to the processing frequency band signal strength QLi greater than the current strength is recorded as the frequency band to be analyzed a, and a=1, 2, ..., c, where c represents the number of frequency bands to be analyzed; The processing method for the frequency band to be analyzed is: The standard signal strength is obtained, and the stable duration of the standard signal strength corresponding to the frequency band a to be analyzed is obtained, recorded as Ta, and the stable duration Ta is compared with the threshold Ty, and the value of the threshold Ty is set by the operator, and then the frequency band a to be analyzed whose stable duration Ta is greater than the threshold Ty is screened out and recorded as the pre-selected frequency band, and the pre-selected frequency band corresponding to the least concurrent users in the pre-selected frequency band is selected as the determined frequency band; The analysis method for high concurrent data is as follows: The real-time data transmitted is obtained, and the real-time data includes the current environment corresponding to the positioning information. The current frequency band of the positioning information is obtained and recorded as the target frequency band, and all the processing frequency bands Li are obtained. Then, the user proportion values in the processing frequency bands Li under different current environments are calculated and recorded as the environment proportion values. At the same time, the signal strength average values corresponding to different current environments are calculated to obtain the environment signal average value; The current environment corresponding to the positioning information is recorded as the comparison environment, and the processing frequency band corresponding to the non-existent comparison environment is screened and recorded as the pre-processing frequency band b, where b=1, 2, ..., o, where o represents the number of pre-processing frequency bands. At the same time, the number of concurrent users corresponding to the pre-processing frequency band b is obtained and recorded as Kb and the signal strength mean value Qbp; The frequency band value calculation and analysis method for the preprocessing frequency band is as follows: The frequency band delay time corresponding to the pre-processing frequency band b is obtained and recorded as YTb, and the obtained data is substituted into the formula The frequency band value Rb corresponding to the preprocessing frequency band b is calculated, and then the frequency band values Rb are sorted from large to small, and the preprocessing frequency band corresponding to the smallest frequency band value Rb is selected and recorded as the determined frequency band, and the determined frequency band information is generated. 2. A low-power multi-mode positioning platform according to claim 1, characterized in that the specific manner in which the data fusion analysis module processes the satellite system data is as follows: The data format corresponding to the satellite data is obtained, and the data format that the current system can recognize is obtained and recorded as the recognition conversion format, and the conversion loss corresponding to the recognition conversion format is obtained. At the same time, the recognition conversion format corresponding to the minimum conversion loss is selected as the standard format, and then the satellite data is converted to the standard format to obtain the fused data. 3. A low-power multi-mode positioning platform according to claim 1, characterized in that the information positioning analysis module analyzes the fusion data in the following specific manner: The historical data within the time period t is obtained, and the positioning time of the fused data is classified into high-concurrency time periods and non-high-concurrency time periods. The fused data is further classified according to the positioning time classification to obtain high-concurrency data and non-high-concurrency data. 4. A low-power multi-mode positioning platform according to claim 1, characterized in that the positioning information output module is used to correct and adjust the positioning information according to the determined frequency band. 5. A low-power multi-mode positioning method, the method is applied to the low-power multi-mode positioning platform according to any one of claims 1 to 4, characterized in that the method specifically comprises the following steps: Step 1: uniformly convert the data formats of multiple satellite data to obtain fused data; Step 2: Analyze the generation time of the fused data in combination with historical data, and classify the fused data into high-concurrency data and non-high-concurrency data; Step 3: Analyze the signal strength and processing frequency band of high-concurrency data, and determine the frequency band based on the stable duration of the standard signal strength; Step 4: Analyze the current environment of high-concurrency data and select the determined frequency band based on the signal strength corresponding to the current environment.