Precise positioning method and system for mobile communication terminal based on artificial intelligence

Abstract

The present invention relates to the field of data processing technology, and in particular to a precise positioning method and system for a mobile communication terminal based on artificial intelligence, comprising a positioning tag, a reading and writing subsystem, an environment collection subsystem and an integration subsystem, wherein the reading and writing subsystem comprises an X-axis reader / writer, a Y-axis reader / writer and a Z-axis reader / writer. Firstly, the positioning tag locates the position of the mobile communication terminal in real time to obtain real-time positioning; then, the X-axis reader / writer reads and writes the X-axis position based on real-time positioning; the Y-axis reader / writer reads and writes the Y-axis position based on real-time positioning; the Z-axis reader / writer reads and writes the Z-axis position based on real-time positioning; meanwhile, the environment collection subsystem is used to collect current environment data of the positioning tag; finally, the integration subsystem integrates the X-axis position, the Y-axis position, the Z-axis position and the current environment to obtain positioning data, thereby solving the problem of excessive positioning error of the existing positioning method.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a precise positioning method and system for a mobile communication terminal based on artificial intelligence. Background Art

[0002] The current location of the mobile communication terminal can be tracked through the positioning function, so as to find the terminal. However, the existing positioning method, which combines the GPS system and the GSM base station positioning, also has the problem of inaccurate positioning, resulting in excessive positioning errors. Summary of the invention

[0003] The purpose of the present invention is to provide a precise positioning method and system for a mobile communication terminal based on artificial intelligence, aiming to solve the problem of excessive positioning error in existing positioning methods.

[0004] To achieve the above-mentioned purpose, in a first aspect, the present invention provides a precise positioning system for a mobile communication terminal based on artificial intelligence, comprising a positioning tag, a reading and writing subsystem, an environment collection subsystem and an integration subsystem, wherein the positioning tag, the reading and writing subsystem and the integration subsystem are connected in sequence, and the environment collection subsystem is connected to the integration subsystem; the reading and writing subsystem comprises an X-axis reader / writer, a Y-axis reader / writer and a Z-axis reader / writer, and the X-axis reader / writer, the Y-axis reader / writer and the Z-axis reader / writer are all connected to the positioning tag;

[0005] The positioning tag is used to locate the position of the mobile communication terminal in real time to obtain real-time positioning;

[0006] The X-axis reader / writer reads and writes the X-axis position based on the real-time positioning;

[0007] The Y-axis reader / writer reads and writes the Y-axis position based on the real-time positioning;

[0008] The Z-axis reader / writer reads and writes the Z-axis position based on the real-time positioning;

[0009] The environment collection subsystem is used to collect the current environment data of the positioning tag;

[0010] The integration subsystem is used to integrate the X-axis position, the Y-axis position, the Z-axis position and the current environment data to obtain positioning data.

[0011] Wherein, the environment acquisition subsystem includes a shooting module and a key extraction module, and the shooting module is connected to the key extraction module;

[0012] The shooting module is used to shoot the current environment picture where the positioning tag is located;

[0013] The key extraction module is used to perform key extraction on the current environment picture to obtain current environment data.

[0014] Wherein, the environment acquisition subsystem further includes a light measuring module and a fill light module, and the light measuring module is connected to the fill light module;

[0015] The light measuring module is used to measure the current light conditions;

[0016] The fill light module performs fill light when the shooting module shoots based on the current lighting condition.

[0017] Wherein, the key extraction module includes a modeling submodule, a training submodule and an extraction submodule, and the modeling submodule, the training submodule and the extraction submodule are connected;

[0018] The modeling submodule is used to construct a neural network model;

[0019] The training submodule is used to train the neural network model using a public data set to obtain a key extraction model;

[0020] The extraction submodule is used to input the current environment picture into the key extraction model for training to obtain current environment data.

[0021] Wherein, the training submodule comprises an acquisition unit, a preprocessing unit, a training unit and a verification unit, and the acquisition unit, the preprocessing unit, the training unit and the verification unit are connected in sequence;

[0022] The acquisition unit is used to acquire a public data set;

[0023] The preprocessing unit is used to filter, mark and divide the public data set in sequence to obtain a training set and a validation set;

[0024] The training unit is used to train the neural network model using the training set to obtain a pre-trained model;

[0025] The verification unit is used to use the verification set to verify and optimize the pre-trained model to obtain a key extraction model.

[0026] Wherein, the extraction submodule includes a format adjustment unit and an input unit, and the format adjustment unit is connected to the input unit;

[0027] The format adjustment unit adjusts the format of the current environment picture based on the input format of the key extraction model to obtain input data;

[0028] The input unit is used to input the input data into the key extraction model for training to obtain current environment data.

[0029] Wherein, the precise positioning system for mobile communication terminals based on artificial intelligence further comprises a visualization subsystem, and the visualization subsystem is connected to the integration subsystem;

[0030] The visualization subsystem generates a positioning display model based on the positioning data.

[0031] In a second aspect, the present invention provides a method for accurately positioning a mobile communication terminal based on artificial intelligence, comprising the following steps:

[0032] The positioning tag locates the position of the mobile communication terminal in real time to obtain real-time positioning;

[0033] The X-axis reader / writer reads and writes the X-axis position based on the real-time positioning;

[0034] The Y-axis reader / writer reads and writes the Y-axis position based on the real-time positioning;

[0035] The Z-axis reader / writer reads and writes the Z-axis position based on the real-time positioning;

[0036] The environment collection subsystem collects the current environment data of the positioning tag;

[0037] The integration subsystem integrates the X-axis position, the Y-axis position, the Z-axis position and the current environment data to obtain positioning data.

[0038] The environment collection subsystem collects the current environment data of the positioning tag, including:

[0039] The shooting module shoots the current environment picture where the positioning tag is located;

[0040] The key extraction module performs key extraction on the current environment picture to obtain current environment data.

[0041] Wherein, after the step integrating subsystem integrates the X-axis position, the Y-axis position, the Z-axis position and the current environment data to obtain positioning data, the method further includes:

[0042] The visualization subsystem generates a positioning display model based on the positioning data.

[0043] The invention discloses a precise positioning system for a mobile communication terminal based on artificial intelligence. First, the positioning tag locates the position of the mobile communication terminal in real time to obtain the real-time positioning; then, the X-axis reader / writer reads and writes the X-axis position based on the real-time positioning; the Y-axis reader / writer reads and writes the Y-axis position based on the real-time positioning; the Z-axis reader / writer reads and writes the Z-axis position based on the real-time positioning; at the same time, the environment acquisition subsystem is used to collect the current environment data of the positioning tag; finally, the integration subsystem integrates the X-axis position, the Y-axis position, the Z-axis position and the current environment data to obtain positioning data, reads the real-time positioning in three directions, and uses the current environment data as a search reference, thereby solving the problem of excessive positioning error of the existing positioning method. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0045] Figure 1 It is a structural schematic diagram of a precise positioning system for a mobile communication terminal based on artificial intelligence provided by the present invention.

[0046] Figure 2 It is a structural diagram of the positioning tag and the reading and writing subsystem.

[0047] Figure 3 It is a structural diagram of the environment acquisition subsystem.

[0048] Figure 4 It is a structural diagram of the key extraction module.

[0049] Figure 5 It is a structural diagram of the training submodule.

[0050] Figure 6 It is a structural diagram of the extraction submodule.

[0051] Figure 7 It is a structural diagram showing the subsystem.

[0052] Figure 8 It is a flow chart of a precise positioning method of a mobile communication terminal based on artificial intelligence provided by the present invention.

[0053] 1- positioning tag, 2- reading and writing subsystem, 3- environment acquisition subsystem, 4- integration subsystem, 5- visualization subsystem, 6- display subsystem, 7- X-axis reader / writer, 8- Y-axis reader / writer, 9- Z-axis reader / writer, 10- shooting module, 11- key extraction module, 12- light measurement module, 13- fill light module, 14- modeling submodule, 15- training submodule, 16- extraction submodule, 17- acquisition unit, 18- preprocessing unit, 19- training unit, 20- verification unit, 21- format adjustment unit, 22- input unit, 23- display module, 24- scene acquisition module, 25- feature extraction module, 26- angle conversion module. DETAILED DESCRIPTION

[0054] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0055] See also Figures 1 to 7 In the first aspect, the present invention provides a precise positioning system for a mobile communication terminal based on artificial intelligence, comprising a positioning tag 1, a reading and writing subsystem 2, an environment acquisition subsystem 3 and an integration subsystem 4, wherein the positioning tag 1, the reading and writing subsystem 2 and the integration subsystem 4 are connected in sequence, and the environment acquisition subsystem 3 is connected to the integration subsystem 4; the reading and writing subsystem 2 comprises an X-axis reader 7, a Y-axis reader 8 and a Z-axis reader 9, and the X-axis reader 7, the Y-axis reader 8 and the Z-axis reader 9 are all connected to the positioning tag 1;

[0056] The positioning tag 1 is used to locate the position of the mobile communication terminal in real time to obtain real-time positioning;

[0057] The X-axis reader / writer 7 reads and writes the X-axis position based on the real-time positioning;

[0058] The Y-axis reader / writer 8 reads and writes the Y-axis position based on the real-time positioning;

[0059] The Z-axis reader / writer 9 reads and writes the Z-axis position based on the real-time positioning;

[0060] The environment collection subsystem 3 is used to collect the current environment data of the positioning tag 1;

[0061] The integration subsystem 4 is used to integrate the X-axis position, the Y-axis position, the Z-axis position and the current environment data to obtain positioning data.

[0062] Specifically, first, the positioning tag 1 locates the position of the mobile communication terminal in real time to obtain the real-time positioning; then, the X-axis reader / writer 7 reads and writes the X-axis position based on the real-time positioning; the Y-axis reader / writer 8 reads and writes the Y-axis position based on the real-time positioning; the Z-axis reader / writer 9 reads and writes the Z-axis position based on the real-time positioning; at the same time, the environment acquisition subsystem 3 is used to collect the current environment data of the positioning tag 1; finally, the integration subsystem 4 integrates the X-axis position, the Y-axis position, the Z-axis position and the current environment data to obtain positioning data, reads the real-time positioning in three directions, and uses the current environment data as a search reference, thereby solving the problem of excessive positioning error in existing positioning methods.

[0063] Furthermore, the environment acquisition subsystem 3 includes a shooting module 10 and a key extraction module 11, and the shooting module 10 and the key extraction module 11 are connected;

[0064] The shooting module 10 is used to shoot the current environment picture where the positioning tag 1 is located;

[0065] The key extraction module 11 is used to perform key extraction on the current environment picture to obtain current environment data.

[0066] The environment acquisition subsystem 3 further includes a light measuring module 12 and a fill light module 13, and the light measuring module 12 and the fill light module 13 are connected;

[0067] The light measuring module 12 is used to measure the current light conditions;

[0068] The fill light module 13 performs fill light when the shooting module 10 shoots based on the current lighting conditions.

[0069] The key extraction module 11 includes a modeling submodule 14, a training submodule 15 and an extraction submodule 16, and the modeling submodule 14, the training submodule 15 and the extraction submodule 16 are connected;

[0070] The modeling submodule 14 is used to construct a neural network model;

[0071] The training submodule 15 is used to train the neural network model using a public data set to obtain a key extraction model;

[0072] The extraction submodule 16 is used to input the current environment picture into the key extraction model for training to obtain current environment data.

[0073] The training submodule 15 includes an acquisition unit 17, a preprocessing unit 18, a training unit 19 and a verification unit 20, and the acquisition unit 17, the preprocessing unit 18, the training unit 19 and the verification unit 20 are connected in sequence;

[0074] The acquisition unit 17 is used to acquire a public data set;

[0075] The preprocessing unit 18 is used to filter, mark and divide the public data set in sequence to obtain a training set and a validation set;

[0076] The training unit 19 is used to train the neural network model using the training set to obtain a pre-trained model;

[0077] The verification unit 20 is used to use the verification set to verify and optimize the pre-trained model to obtain a key extraction model.

[0078] The extraction submodule 16 includes a format adjustment unit 21 and an input unit 22, and the format adjustment unit 21 is connected to the input unit 22;

[0079] The format adjustment unit 21 adjusts the format of the current environment picture based on the input format of the key extraction model to obtain input data;

[0080] The input unit 22 is used to input the input data into the key extraction model for training to obtain current environment data.

[0081] Specifically, the shooting module 10 shoots the current environment picture where the positioning tag 1 is located; the modeling submodule 14 constructs a neural network model; the acquisition unit 17 acquires a public data set; the preprocessing unit 18 sequentially filters, marks and divides the public data set to obtain a training set and a verification set; the training unit 19 uses the training set to train the neural network model to obtain a pre-trained model; the verification unit 20 uses the verification set to verify and optimize the pre-trained model to obtain a key extraction model; the format adjustment unit 21 adjusts the format of the current environment picture based on the input format of the key extraction model to obtain input data; the input unit 22 inputs the input data into the key extraction model for training to obtain the current environment data. In addition, the light metering module 12 measures the current lighting conditions; the fill light module 13 performs fill light based on the current lighting conditions when the shooting module 10 shoots, thereby improving the shooting clarity of the shooting module 10.

[0082] Furthermore, the precise positioning system for mobile communication terminals based on artificial intelligence further includes a visualization subsystem 5, and the visualization subsystem 5 is connected to the integration subsystem 4;

[0083] The visualization subsystem 5 generates a positioning display model based on the positioning data.

[0084] Specifically, the visualization subsystem 5 generates a positioning display model based on the positioning data for easy viewing.

[0085] Furthermore, the precise positioning system for mobile communication terminals based on artificial intelligence further includes a display subsystem 6, and the display subsystem 6 is connected to the visualization subsystem 5;

[0086] The display subsystem 6 is used to display the positioning display model.

[0087] Specifically, the positioning display model is displayed by the display subsystem 6 to improve the real-time performance of positioning viewing.

[0088] Further, the display subsystem 6 includes a display module 23, a scene acquisition module 24, a feature extraction module 25 and an angle conversion module 26, and the display module 23, the scene acquisition module 24, the feature extraction module 25 and the angle conversion module 26 are connected;

[0089] The display module 23 is used to display the positioning display model;

[0090] The scene acquisition module 24 is used to acquire scene images within the display range of the display module 23;

[0091] The feature extraction module 25 is used to extract features from the scene image to obtain crowd density;

[0092] The angle conversion unit adjusts the angle of the display module 23 based on the crowd density.

[0093] Specifically, the display module 23 displays the positioning display model; the scene acquisition module 24 acquires the scene picture within the display range of the display module 23; the feature extraction module 25 performs feature extraction on the scene picture to obtain crowd density; the angle conversion unit adjusts the angle of the display module 23 based on the crowd density to improve the display effect of the display module 23 on the positioning display model.

[0094] See also Figure 8 In a second aspect, the present invention provides a precise positioning method for a mobile communication terminal based on artificial intelligence, comprising the following steps:

[0095] S1, positioning tag 1 locates the position of the mobile communication terminal in real time to obtain real-time positioning;

[0096] S2, the X-axis reader / writer 7 reads and writes the X-axis position based on the real-time positioning;

[0097] S3, the Y-axis reader / writer 8 reads and writes the Y-axis position based on the real-time positioning;

[0098] S4, the Z-axis reader / writer 9 reads and writes the Z-axis position based on the real-time positioning;

[0099] S5, the environment collection subsystem 3 collects the current environment data of the positioning tag 1;

[0100] Specifically, the shooting module 10 shoots the current environment picture where the positioning tag 1 is located; the key extraction module 11 performs key extraction on the current environment picture to obtain current environment data.

[0101] S6. The integration subsystem 4 integrates the X-axis position, the Y-axis position, the Z-axis position and the current environment data to obtain positioning data.

[0102] Specifically, after the step integration subsystem 4 integrates the X-axis position, the Y-axis position, the Z-axis position and the current environment data to obtain positioning data, the method further includes: the visualization subsystem 5 generates a positioning display model based on the positioning data.

[0103] The above disclosure is only a preferred embodiment of a precise positioning method and system for a mobile communication terminal based on artificial intelligence of the present invention. Of course, this cannot be used to limit the scope of rights of the present invention. Ordinary technicians in this field can understand that all or part of the processes of the above embodiments and equivalent changes made according to the claims of the present invention still fall within the scope of the invention.

Claims

1. A precise positioning system for mobile communication terminals based on artificial intelligence, It is characterized in that It includes a positioning tag, a reading and writing subsystem, an environment collection subsystem and an integration subsystem, wherein the positioning tag, the reading and writing subsystem and the integration subsystem are connected in sequence, and the environment collection subsystem is connected to the integration subsystem; the reading and writing subsystem includes an X-axis reader, a Y-axis reader and a Z-axis reader, and the X-axis reader, the Y-axis reader and the Z-axis reader are all connected to the positioning tag; The positioning tag is used to locate the position of the mobile communication terminal in real time to obtain real-time positioning; The X-axis reader / writer reads and writes the X-axis position based on the real-time positioning; The Y-axis reader / writer reads and writes the Y-axis position based on the real-time positioning; The Z-axis reader / writer reads and writes the Z-axis position based on the real-time positioning; The environment collection subsystem is used to collect the current environment data of the positioning tag; The integration subsystem is used to integrate the X-axis position, the Y-axis position, the Z-axis position and the current environment data to obtain positioning data; The environment acquisition subsystem includes a shooting module and a key extraction module, and the shooting module is connected to the key extraction module; The shooting module is used to shoot the current environment picture where the positioning tag is located; The key extraction module is used to perform key extraction on the current environment picture to obtain current environment data; The key extraction module includes a modeling submodule, a training submodule and an extraction submodule, and the modeling submodule, the training submodule and the extraction submodule are connected; The modeling submodule is used to construct a neural network model; The training submodule is used to train the neural network model using a public data set to obtain a key extraction model; The extraction submodule is used to input the current environment picture into the key extraction model for training to obtain current environment data; The precise positioning system for mobile communication terminals based on artificial intelligence further includes a visualization subsystem, and the visualization subsystem is connected to the integration subsystem; The visualization subsystem generates a positioning display model based on the positioning data; The precise positioning system for mobile communication terminals based on artificial intelligence further includes a display subsystem, and the display subsystem is connected to the visualization subsystem; The display subsystem is used to display the positioning display model; The display subsystem comprises a display module, a scene acquisition module, a feature extraction module and an angle conversion module, and the display module, the scene acquisition module, the feature extraction module and the angle conversion module are connected; The display module is used to display the positioning display model; The scene acquisition module is used to acquire scene images within the display range of the display module; The feature extraction module is used to extract features from the scene image to obtain crowd density; The angle conversion unit adjusts the angle of the display module based on the crowd density.

2. The precise positioning system for mobile communication terminals based on artificial intelligence as claimed in claim 1, It is characterized in that The environment acquisition subsystem further includes a light measuring module and a fill light module, and the light measuring module is connected to the fill light module; The light measuring module is used to measure the current light conditions; The fill light module performs fill light when the shooting module shoots based on the current lighting condition.

3. The precise positioning system for mobile communication terminals based on artificial intelligence as claimed in claim 2, It is characterized in that The training submodule comprises an acquisition unit, a preprocessing unit, a training unit and a verification unit, wherein the acquisition unit, the preprocessing unit, the training unit and the verification unit are connected in sequence; The acquisition unit is used to acquire a public data set; The preprocessing unit is used to filter, mark and divide the public data set in sequence to obtain a training set and a validation set; The training unit is used to train the neural network model using the training set to obtain a pre-trained model; The verification unit is used to use the verification set to verify and optimize the pre-trained model to obtain a key extraction model.

4. The precise positioning system for mobile communication terminals based on artificial intelligence as claimed in claim 3, It is characterized in that The extraction submodule includes a format adjustment unit and an input unit, and the format adjustment unit is connected to the input unit; The format adjustment unit adjusts the format of the current environment picture based on the input format of the key extraction model to obtain input data; The input unit is used to input the input data into the key extraction model for training to obtain current environment data.

5. A precise positioning method for a mobile communication terminal based on artificial intelligence, applied to the precise positioning system for a mobile communication terminal based on artificial intelligence as claimed in claim 4, It is characterized in that The following steps are involved: The positioning tag locates the position of the mobile communication terminal in real time to obtain real-time positioning; The X-axis reader / writer reads and writes the X-axis position based on the real-time positioning; The Y-axis reader / writer reads and writes the Y-axis position based on the real-time positioning; The Z-axis reader / writer reads and writes the Z-axis position based on the real-time positioning; The environment collection subsystem collects the current environment data of the positioning tag; The integration subsystem integrates the X-axis position, the Y-axis position, the Z-axis position and the current environment data to obtain positioning data.

6. The precise positioning method of a mobile communication terminal based on artificial intelligence as claimed in claim 5, It is characterized in that The environment collection subsystem collects the current environment data of the positioning tag, including: The shooting module shoots the current environment picture where the positioning tag is located; The key extraction module performs key extraction on the current environment picture to obtain current environment data.

7. The precise positioning method of a mobile communication terminal based on artificial intelligence as claimed in claim 6, It is characterized in that After the step integration subsystem integrates the X-axis position, the Y-axis position, the Z-axis position and the current environment data to obtain positioning data, the method further includes: The visualization subsystem generates a positioning display model based on the positioning data.

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