TW202138840A - Wireless positioning method including a learning analytic step, a detecting positioning step, and an analytic positioning step - Google Patents

Wireless positioning method including a learning analytic step, a detecting positioning step, and an analytic positioning step Download PDF

Info

Publication number
TW202138840A
TW202138840A TW109111146A TW109111146A TW202138840A TW 202138840 A TW202138840 A TW 202138840A TW 109111146 A TW109111146 A TW 109111146A TW 109111146 A TW109111146 A TW 109111146A TW 202138840 A TW202138840 A TW 202138840A
Authority
TW
Taiwan
Prior art keywords
signal
positioning
tag unit
signal information
artificial intelligence
Prior art date
Application number
TW109111146A
Other languages
Chinese (zh)
Other versions
TWI726671B (en
Inventor
吳陳寬
Original Assignee
神達電腦股份有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 神達電腦股份有限公司 filed Critical 神達電腦股份有限公司
Priority to TW109111146A priority Critical patent/TWI726671B/en
Application granted granted Critical
Publication of TWI726671B publication Critical patent/TWI726671B/en
Publication of TW202138840A publication Critical patent/TW202138840A/en

Links

Images

Abstract

A wireless positioning method is applied to a wireless positioning system to cooperate with a working field. The working field includes a plurality of grid points. The wireless positioning system includes an automatic vehicle device and an artificial intelligence server. The automatic vehicle device includes an automatic vehicle and a tag unit disposed on the automatic vehicle. The wireless positioning method includes a learning analytic step, a detecting positioning step, and an analytic positioning step. In the learning analytic step, the artificial intelligence server simulates the signal information on the grid points which are not detected by the tag unit, and integrates the simulated signal information into a position signal information table for storage. In coordination with the detecting positioning step, the tag unit sends the positioning signal information to the artificial intelligence server. In the analytic positioning step, the artificial intelligence server finds the signal strength combination corresponding to the positioning signal information from the position signal information table to achieve the purpose of indoor positioning.

Description

無線定位方法Wireless positioning method

本發明是有關於一種定位方法，特別是指一種無線定位方法。The present invention relates to a positioning method, in particular to a wireless positioning method.

近年來，隨著室內定位技術的廣泛應用與發展，常見使用無線射頻技術(RFID)、無線網路(Wi-Fi)、ZigBee與超聲波等方式進行室內定位。一般來說，室內定位需要在室內的場域內部屬多個無線網路基地台(AP)，且場域內部透過定位標籤利用無線定位方式與所述的無線網路基地台進行訊號偵測，並為了提高室內定位的精準度，通常需要偵測室內場域的每個座標位置對應的訊號，達成室內定位的目的。然而，要偵測室內場域的每個座標位置對應的訊號會非常耗費時間與人力，極需從業人員探討與改善。In recent years, with the widespread application and development of indoor positioning technology, it is common to use radio frequency technology (RFID), wireless network (Wi-Fi), ZigBee, and ultrasound for indoor positioning. Generally speaking, indoor positioning needs to belong to multiple wireless network base stations (AP) in the indoor field, and the wireless positioning method is used for signal detection with the wireless network base station through the positioning tag in the field. And in order to improve the accuracy of indoor positioning, it is usually necessary to detect the signal corresponding to each coordinate position of the indoor field to achieve the purpose of indoor positioning. However, it takes a lot of time and manpower to detect the signal corresponding to each coordinate position in the indoor field, and it is extremely necessary for practitioners to discuss and improve.

因此，本發明之目的，即在提供一種省時且有效率的無線定位方法。Therefore, the purpose of the present invention is to provide a time-saving and efficient wireless positioning method.

於是，本發明無線定位方法，應用於一無線定位系統配合應用於一工作場域，該工作場域包含多個網格，及多個網格點，該無線定位系統包含一自動車裝置、多個無線網路訊號收發器、一資料伺服器，及一人工智慧伺服器，該自動車裝置包括一自動車，及一設置於該自動車上的標籤單元。該無線定位方法包含一初始移動步驟、一訊號收集步驟、一資料整合傳輸步驟、一學習分析步驟、一偵測定位步驟，及一分析定位步驟。Therefore, the wireless positioning method of the present invention is applied to a wireless positioning system in conjunction with a work field, the work field includes a plurality of grids, and a plurality of grid points, the wireless positioning system includes an automatic vehicle device, a plurality of A wireless network signal transceiver, a data server, and an artificial intelligence server. The automatic car device includes an automatic car and a label unit arranged on the automatic car. The wireless positioning method includes an initial movement step, a signal collection step, a data integration and transmission step, a learning analysis step, a detection positioning step, and an analysis positioning step.

於該初始移動步驟中，該自動車受控制地在該工作場域內依一預設路線移動且使該自動車經過該工作場域中所需經過的網格。In the initial moving step, the automatic vehicle is controlled to move in the work field according to a preset route and the automatic vehicle passes through the grid in the work field.

於該訊號收集步驟中，於該自動車經過所需經過的網格的過程中，該標籤單元會偵測所需偵測的網格點且該標籤單元會依一偵測工作表在經過所需偵測的每一網格點時，該標籤單元發送一偵測訊息，而當每一無線網路訊號收發器接收到該偵測訊息而傳送一訊號資訊至該標籤單元，並該標籤單元在所需偵測的每一網格點上取得所述無線網路訊號收發器傳送的所述訊號資訊，而該標籤單元依所需偵測的每一網格點的位置座標而將在所需偵測的每一網格點上接收到所述訊號資訊儲存成一訊號強度組合。In the signal collection step, the tag unit will detect the grid points that need to be detected and the tag unit will pass through the required grid points according to a detection worksheet while the automatic car passes through the required grid. When each grid point is detected, the tag unit sends a detection message, and when each wireless network signal transceiver receives the detection message, it sends a signal information to the tag unit, and the tag unit is Obtain the signal information transmitted by the wireless network signal transceiver at each grid point to be detected, and the tag unit will be in the required position according to the position coordinates of each grid point to be detected. The signal information received at each grid point detected is stored as a signal strength combination.

於該資料整合傳輸步驟中，該標籤單元將所需偵測的每一網格點對應的訊號強度組合傳送至該資料伺服器且該資料伺服器將所述訊號強度組合依其對應的網格點的位置座標整合成一位置訊號資訊表儲存，並該人工智慧伺服器經該資料伺服器取得該位置訊號資訊表。In the data integration and transmission step, the tag unit sends the signal strength combination corresponding to each grid point to be detected to the data server, and the data server sends the signal strength combination according to its corresponding grid The position coordinates of the points are integrated into a position signal information table for storage, and the artificial intelligence server obtains the position signal information table through the data server.

於該學習分析步驟中，該人工智慧伺服器分析該位置訊號資訊表且依該位置訊號資訊表中的每一訊號強度組合的資訊分析模擬出該標籤單元未偵測的網格點上的訊號資訊，並該人工智慧伺服器將模擬出的每一組訊號資訊依對應的網格點的位置座標儲存成對應的訊號強度組合而整合入該位置訊號資訊表儲存。In the learning analysis step, the artificial intelligence server analyzes the position signal information table and analyzes and simulates the signals on the grid points that are not detected by the tag unit based on the information analysis of each signal intensity combination in the position signal information table The artificial intelligence server stores each set of simulated signal information into a corresponding signal strength combination based on the position coordinates of the corresponding grid point and integrates it into the position signal information table for storage.

於該偵測定位步驟中，當該自動車在該工作場域內且該標籤單元發送一定位訊息，而當每一無線網路訊號收發器接收到該定位訊息並傳送一定位訊號至該標籤單元，而該標籤單元將所述定位訊號儲存成一定位訊號資訊傳送至該人工智慧伺服器。In the detecting and positioning step, when the automatic vehicle is in the working field and the tag unit sends a positioning message, and when each wireless network signal transceiver receives the positioning message and sends a positioning signal to the tag unit , And the tag unit stores the positioning signal as a positioning signal information and sends it to the artificial intelligence server.

於該分析定位步驟中，而該人工智慧伺服器分析該定位訊號資訊的所述定位訊號的訊號強度且從該位置訊號資訊表中找出對應訊號強度組合。In the analyzing and positioning step, the artificial intelligence server analyzes the signal strength of the positioning signal of the positioning signal information and finds the corresponding signal strength combination from the position signal information table.

本發明之功效在於：藉由於該學習分析步驟中，該人工智慧伺服器依該位置訊號資訊表中的每一訊號強度組合的資訊分析模擬出該標籤單元未偵測的網格點上的訊號資訊，並將模擬出的每一訊號資訊整合入該位置訊號資訊表儲存，且配合於該偵測定位步驟中，當該標籤單元接收到所述定位訊號，而該標籤單元將所述定位訊號儲存成對應的定位訊號資訊且傳送至該人工智慧伺服器，以及於該分析定位步驟中，該人工智慧伺服器分析該定位訊號資訊的所述定位訊號的訊號強度且從該位置訊號資訊表中找出對應訊號強度組合，達成室內定位的目的。而且透過該人工智慧伺服器可分析模擬出該標籤單元未偵測的網格點上的訊號資訊的設計，改善以往該標籤單元需要偵測該工作場域的大數量的網格點的問題，有效節省時間與人力並兼具確保室內精準定位的目的。The effect of the present invention is that, in the learning analysis step, the artificial intelligence server analyzes and simulates the signal on the undetected grid point of the tag unit according to the information of each signal intensity combination in the position signal information table Information, and integrates each simulated signal information into the position signal information table for storage, and cooperates in the detection and positioning step, when the tag unit receives the positioning signal, the tag unit transfers the positioning signal The corresponding positioning signal information is stored and sent to the artificial intelligence server, and in the analyzing and positioning step, the artificial intelligence server analyzes the signal strength of the positioning signal of the positioning signal information and obtains it from the position signal information table Find out the corresponding signal strength combination to achieve the purpose of indoor positioning. Moreover, the artificial intelligence server can analyze and simulate the design of the signal information on the grid points not detected by the tag unit, which improves the problem that the tag unit needs to detect a large number of grid points in the working field in the past. It effectively saves time and manpower and has the purpose of ensuring accurate indoor positioning.

在本發明被詳細描述之前，應當注意在以下的說明內容中，類似的元件是以相同的編號來表示。Before the present invention is described in detail, it should be noted that in the following description, similar elements are denoted by the same numbers.

參閱圖1與圖2，本發明無線定位方法的實施例，應用於一無線定位系統1配合應用於一工作場域60。該工作場域60包含多個網格61，及多個網格點62。該無線定位系統1包含一自動車裝置2、五個無線網路訊號收發器3(AP)、一資料伺服器4，及一人工智慧伺服器5。該自動車裝置2包括一自動車21，及一設置於該自動車21上的標籤單元22。該無線定位方法包含一初始移動步驟A、一訊號收集步驟B、一資料整合傳輸步驟C、一學習分析步驟D、一偵測定位步驟E，及一分析定位步驟F。於本實施例中，所述無線網路訊號收發器3、該資料伺服器4與該人工智慧伺服器5彼此是以無線網路訊號傳輸連結，但不以此為限。Referring to FIGS. 1 and 2, the embodiment of the wireless positioning method of the present invention is applied to a wireless positioning system 1 in cooperation with a working field 60. The working field 60 includes a plurality of grids 61 and a plurality of grid points 62. The wireless positioning system 1 includes an automatic vehicle device 2, five wireless network signal transceivers 3 (AP), a data server 4, and an artificial intelligence server 5. The automatic vehicle device 2 includes an automatic vehicle 21 and a label unit 22 provided on the automatic vehicle 21. The wireless positioning method includes an initial movement step A, a signal collection step B, a data integration transmission step C, a learning analysis step D, a detection positioning step E, and an analysis positioning step F. In this embodiment, the wireless network signal transceiver 3, the data server 4, and the artificial intelligence server 5 are connected to each other through wireless network signal transmission, but it is not limited to this.

首先，於該初始移動步驟A中，該自動車21受控制地在該工作場域60內依一預設路線移動且使該自動車21經過該工作場域60中所需經過的網格61。First, in the initial movement step A, the automatic vehicle 21 is controlled to move in the work field 60 according to a preset route and the automatic vehicle 21 passes through the grid 61 that the work field 60 needs to pass.

接著，於該訊號收集步驟B中，於該自動車21經過所需經過的網格61的過程中，該標籤單元22會偵測所需偵測的網格點62且該標籤單元22會依一偵測工作表在經過所需偵測的每一網格點62時，該標籤單元22發送一偵測訊息，而當每一無線網路訊號收發器3接收到該偵測訊息而傳送一訊號資訊至該標籤單元22，並該標籤單元22在所需偵測的每一網格點62上取得所述無線網路訊號收發器3傳送的所述訊號資訊時，而該標籤單元22依所需偵測的每一網格點62的位置座標而將在所需偵測的每一網格點62上接收到所述訊號資訊儲存成一訊號強度組合。詳細來說，就是該自動車21在首次進入該工作場域60時，該自動車21會受控制地依該預設路線在該工作場域60內移動且在經過所需經過的網格點62時，該標籤單元22會偵測所需偵測的網格點62而發送該偵測訊息去偵測所述無線網路訊號收發器3的訊號，而在所需偵測的每一網格點62上取得所述無線網路訊號收發器3傳送的所述訊號資訊時，並該標籤單元22依所需偵測的每一網格點62的位置座標而將所需偵測的每一網格點62上所接收到所述訊號資訊儲存成該訊號強度組合。簡單而言，就是該自動車21經過所需偵測的網格點62時，該標籤單元22會發送該偵測訊息至所述無線網路訊號收發器3，而當每一無線網路訊號收發器3接收到該偵測訊息而傳送對應的訊號資訊至該標籤單元22，並該標籤單元22在每一所需偵測的網格點62接收到的所述訊號資訊依其對應的位置座標而儲存成該訊號強度組合。於本實施例中，每一訊號強度組合具有對應每一網格點62的一位置座標，及多個訊號強度。每一訊號強度組合的所述訊號強度為在每一網格點62上，該標籤單元22所接收到所述無線網路訊號收發器3傳送的訊號強度。也就是說，每一訊號強度組合為該標籤單元22在每一所需偵測的網格點62接收到的所述訊號資訊，於本實施例中，該標籤單元22會將在每一所需偵測的網格點62接收到的所述訊號資訊依其對應的位置座標整合成對應的訊號強度組合，舉例來說，該標籤單元22在 (a111)座標位置的網格點62上收到所述無線網路訊號收發器3的所述訊號強度分別為(-37,-37,-38,-38,-36)，其對應的訊號強度組合為﹛a101：﹛-37,-37,-38,-38,-36﹜﹜。Then, in the signal collection step B, when the automatic vehicle 21 passes through the grid 61 to be passed, the tag unit 22 will detect the grid point 62 to be detected and the tag unit 22 will follow When the detection worksheet passes through each grid point 62 that needs to be detected, the tag unit 22 sends a detection message, and when each wireless network signal transceiver 3 receives the detection message, it sends a signal Information to the tag unit 22, and when the tag unit 22 obtains the signal information transmitted by the wireless network signal transceiver 3 at each grid point 62 that needs to be detected, the tag unit 22 The position coordinates of each grid point 62 to be detected and the signal information received on each grid point 62 to be detected are stored as a signal strength combination. In detail, when the automatic vehicle 21 first enters the working field 60, the automatic vehicle 21 will be controlled to move within the working field 60 according to the preset route and pass the grid point 62 that needs to be passed. , The tag unit 22 will detect the grid point 62 to be detected and send the detection message to detect the signal of the wireless network signal transceiver 3, and at each grid point to be detected When the signal information transmitted by the wireless network signal transceiver 3 is obtained at 62, the tag unit 22 will detect the position coordinates of each grid point 62 that needs to be detected. The signal information received on the grid 62 is stored as the signal strength combination. Simply put, when the automatic vehicle 21 passes the grid point 62 that needs to be detected, the tag unit 22 will send the detection message to the wireless network signal transceiver 3, and when each wireless network signal is sent and received The device 3 receives the detection message and transmits corresponding signal information to the tag unit 22, and the signal information received by the tag unit 22 at each grid point 62 to be detected is based on its corresponding position coordinates And save it as the signal strength combination. In this embodiment, each signal intensity combination has a position coordinate corresponding to each grid point 62 and multiple signal intensities. The signal strength of each signal strength combination is the signal strength transmitted by the wireless network signal transceiver 3 received by the tag unit 22 at each grid point 62. That is, each signal strength combination is the signal information received by the tag unit 22 at each grid point 62 to be detected. In this embodiment, the tag unit 22 will The signal information received by the grid point 62 to be detected is integrated into a corresponding signal strength combination according to its corresponding position coordinates. For example, the tag unit 22 receives the grid point 62 at the (a111) coordinate position. The signal strengths to the wireless network signal transceiver 3 are (-37, -37, -38, -38, -36), and the corresponding signal strength combination is {a101:} -37, -37 ,-38,-38,-36﹜﹜.

另外要說明的是，本實施例中，該標籤單元22會依該偵測工作表在經過所需偵測的每一網格點62時，該標籤單元22會發送一預定次數的該偵測訊息，該預定次數為5次，但不以此為限。也就是說，該標籤單元22在經過每一所需偵測的網格點62時會發送該偵測訊息5次，而當每一無線網路訊號收發器3每次接收到該偵測訊息而傳送該訊號資訊至該標籤單元22，並該標籤單元22在所需偵測的每一網格點62上取得所述無線網路訊號收發器3傳送的所述訊號資訊後，該標籤單元22會依所需偵測的每一網格點62的位置座標而將所需偵測的每一網格點62上接收到每一無線網路訊號收發器3所傳送的該預定次數的所述訊號資訊的訊號強度取平均值而儲存成該訊號強度組合。In addition, it should be noted that, in this embodiment, the tag unit 22 will send a predetermined number of detections when passing through each grid point 62 that needs to be detected according to the detection worksheet. Message, the predetermined number of times is 5, but not limited to this. In other words, the tag unit 22 will send the detection message 5 times when passing through each grid point 62 that needs to be detected, and every time when each wireless network signal transceiver 3 receives the detection message After transmitting the signal information to the tag unit 22, and the tag unit 22 obtains the signal information transmitted by the wireless network signal transceiver 3 at each grid point 62 that needs to be detected, the tag unit 22 will receive the predetermined number of times transmitted by each wireless network signal transceiver 3 on each grid point 62 that needs to be detected according to the position coordinates of each grid point 62 that needs to be detected. The signal intensity of the signal information is averaged and stored as the signal intensity combination.

再來，於該資料整合傳輸步驟C中，該標籤單元22將所需偵測的每一網格點62對應的訊號強度組合傳送至該資料伺服器4且該資料伺服器4將所述訊號強度組合依其對應的網格點62的位置座標整合成一位置訊號資訊表儲存，並該人工智慧伺服器5經該資料伺服器4取得該位置訊號資訊表。簡單來說，就是該資料伺服器4將所接收到的所述訊號強度組合依每一訊號強度組合對應的網格點62的位置座標整合成該位置訊號資訊表儲存，所以該位置訊號資訊表目前記載的每一訊號強組合為對應該標籤單元22在所需偵測的每一網格點62接收到的所述訊號資訊，而該人工智慧伺服器5可直接從該資料伺服器4取得該位置訊號資訊表。Then, in the data integration transmission step C, the tag unit 22 transmits the signal strength combination corresponding to each grid point 62 to be detected to the data server 4, and the data server 4 transmits the signal The intensity combination is integrated into a position signal information table and stored according to the position coordinates of the corresponding grid points 62, and the artificial intelligence server 5 obtains the position signal information table through the data server 4. To put it simply, the data server 4 integrates the received signal strength combination into the position signal information table and stores it according to the position coordinates of the grid points 62 corresponding to each signal strength combination, so the position signal information table Each strong combination of signals currently recorded corresponds to the signal information received by the tag unit 22 at each grid point 62 that needs to be detected, and the artificial intelligence server 5 can directly obtain it from the data server 4 The position signal information table.

接著，於該學習分析步驟D中，該人工智慧伺服器5分析該位置訊號資訊表且依該位置訊號資訊表中的每一訊號強度組合的資訊分析模擬出該標籤單元22未偵測的網格點62上的訊號資訊，並該人工智慧伺服器5將模擬出的每一組訊號資訊依對應的網格點62的位置座標儲存成對應的訊號強度組合而整合入該位置訊號資訊表儲存。也就是說，該人工智慧伺服器5取得該位置訊號資訊表後，且依該位置訊號資訊表中的每一訊號強度組合的資訊分析模擬出該標籤單元22未偵測的網格點62上的訊號資訊。於本實施例中，該人工智慧伺服器5分析該位置訊號資訊表且依該位置訊號資訊表中的每一訊號強度組合的位置座標與所述訊號強度分析模擬出該標籤單元22未偵測的網格點62上的訊號資訊，並該人工智慧伺服器5將模擬出的每一組訊號資訊依對應的網格點62的位置座標儲存成對應的訊號強度組合且整合入該位置訊號資訊表儲存。進一步來說，就是本實施例中，該人工智慧伺服器5分析該位置訊號資訊表中的每一訊號強度組合對應的位置座標與對應的所述訊號強度且依所述訊號強度組合的位置座標彼此的距離與所述訊號強度組合對應的所述訊號強度進行分析運算，而模擬出該標籤單元22未偵測的網格點62上的訊號資訊，舉例來說，於本實施例中，該標籤單元22已偵測網格點62(a100)與網格點62(a111)上的訊號資訊，而該位置訊號資訊表目前記載﹛a100：﹛-32,-32,  -32,-31,-32﹜﹜與﹛a111：﹛-37,-37,-38,-38,-36﹜﹜的訊號強組合，該人工智慧伺服器5分析在﹛a100：﹛-32,-32,-32,-31,  -32﹜﹜與﹛a111：﹛-37,-37,-38,-38,-36﹜﹜兩者間的距離與訊號強度(RSSI)差值且模擬運算出對應的斜率曲線圖，並該人工智慧伺服器5藉此推算出該標籤單元22未偵測的網格點62(如：a122、a133等網格點62)上的訊號資訊，其他依此類推，在此不另贅述。而該人工智慧伺服器5將模擬出的每一組訊號資訊依對應的網格點62的位置座標儲存成對應的訊號強度組合且整合入該位置訊號資訊表儲存，所以此時該位置訊號資訊表已記載所有網格點62對應的訊號強度組合。Then, in the learning analysis step D, the artificial intelligence server 5 analyzes the position signal information table and simulates the undetected net of the tag unit 22 according to the information analysis of each signal intensity combination in the position signal information table. The signal information on the grid point 62, and the artificial intelligence server 5 stores each set of simulated signal information according to the position coordinates of the corresponding grid point 62 into the corresponding signal strength combination and integrates it into the position signal information table for storage . In other words, after the artificial intelligence server 5 obtains the position signal information table, it simulates the undetected grid points 62 of the tag unit 22 based on the information analysis of each signal intensity combination in the position signal information table. Signal information. In this embodiment, the artificial intelligence server 5 analyzes the position signal information table and simulates that the tag unit 22 is not detected based on the position coordinates of each signal strength combination in the position signal information table and the signal strength analysis. The artificial intelligence server 5 stores the signal information of each set of simulated signal information according to the position coordinates of the corresponding grid point 62 into the corresponding signal strength combination and integrates the position signal information Table storage. Furthermore, in this embodiment, the artificial intelligence server 5 analyzes the position coordinates corresponding to each signal strength combination in the position signal information table and the position coordinates corresponding to the signal strength and according to the signal strength combination. The signal intensity corresponding to the distance between each other and the signal intensity combination is analyzed and calculated, and the signal information on the grid point 62 not detected by the tag unit 22 is simulated. For example, in this embodiment, the The tag unit 22 has detected the signal information on the grid point 62 (a100) and the grid point 62 (a111), and the position signal information table currently records {a100:} -32, -32, -32, -31, -32﹜﹜and﹛a111:﹛-37,-37,-38,-38,-36﹜﹜The strong combination of signals, the artificial intelligence server 5 analyzes in﹛a100:﹛-32,-32,-32 ,-31, -32﹜﹜ and ﹛a111: ﹛-37,-37,-38,-38,-36﹜﹜The distance between the two and the signal strength (RSSI) difference, and the corresponding slope curve is calculated by simulation Figure, and the artificial intelligence server 5 uses this to calculate the signal information on the grid points 62 (such as grid points 62 such as a122, a133, etc.) that are not detected by the tag unit 22, and so on. Another repeat. The artificial intelligence server 5 stores each set of simulated signal information into a corresponding signal strength combination according to the position coordinates of the corresponding grid point 62 and integrates it into the position signal information table for storage. Therefore, the position signal information at this time The table has recorded the signal intensity combinations corresponding to all grid points 62.

接著，於該偵測定位步驟E中，當該自動車21在該工作場域60內且該標籤單元22發送一定位訊息，而當每一無線網路訊號收發器3接收到該定位訊息並傳送一定位訊號至該標籤單元22，而該標籤單元22將所述定位訊號儲存成一定位訊號資訊傳送至該人工智慧伺服器5。詳細來說，在上述步驟中，該人工智慧伺服器5已完成將該位置訊號資訊表記載所有網格點62對應的訊號強度組合，所以該位置訊號資訊表已記載所有網格點62對應的訊號強度組合，接著。當該自動車21進入在該工作場域60內移動而進行送貨作業或停止在該工作場域60某處時，該標籤單元22會持續發送該定位訊息至所述無線網路訊號收發器3且取得所述無線網路訊號收發器3分別回傳的對應的定位訊號，而所述定位訊號為對應該自動車21在當下的位置時，該標籤單元22所接收到所述無線網路訊號收發器3傳送的訊號資訊，並該標籤單元22將所述定位訊號儲存成該定位訊號資訊傳送至該人工智慧伺服器5。Then, in the detecting and positioning step E, when the automatic vehicle 21 is in the working field 60 and the tag unit 22 sends a positioning message, and when each wireless network signal transceiver 3 receives the positioning message and transmits A positioning signal is sent to the tag unit 22, and the tag unit 22 stores the positioning signal as a positioning signal information and sends it to the artificial intelligence server 5. In detail, in the above steps, the artificial intelligence server 5 has already recorded the signal strength combinations corresponding to all the grid points 62 in the position signal information table, so the position signal information table has recorded the signal strength combinations corresponding to all the grid points 62 Signal strength combination, then. When the automatic vehicle 21 enters and moves within the work field 60 to perform delivery operations or stops somewhere in the work field 60, the tag unit 22 will continue to send the positioning information to the wireless network signal transceiver 3 And obtain the corresponding positioning signal returned by the wireless network signal transceiver 3, and when the positioning signal corresponds to the current position of the automatic vehicle 21, the tag unit 22 receives the wireless network signal transceiver The tag unit 22 stores the positioning signal as the positioning signal information and sends it to the artificial intelligence server 5.

最後，於該分析定位步驟F中，而該人工智慧伺服器5分析該定位訊號資訊的所述定位訊號的訊號強度且從該位置訊號資訊表中找出對應訊號強度組合。由於該定位訊號資訊為對應該自動車21在當下的位置時，該標籤單元22所接收到所述無線網路訊號收發器3傳送的訊號資訊，所以該定位資訊沒有記載該自動車21當下的位置，而該人工智慧伺服器5接收到該定位訊號資訊後，該人工智慧伺服器5會依該定位訊號資訊中所述定位訊號的訊號強度從該位置訊號資訊表中找出對應訊號強度組合，舉例來說，該定位資訊的所述定位訊號的訊號強度為(-37,-37,-37,-38,-36))，該人工智慧伺服器5從該位置訊號資訊表中找出﹛a111：﹛-37,-37,-38,-38,-36﹜﹜的組合較符合該定位資訊的所述訊號強度，所以藉此可判斷該自動車21當下的位置在a111網格點62附近，而使用者可透過該人工智慧伺服器5得知該自動車21所在位置，便於即時掌握該自動車21工作進度與動向。Finally, in the analyzing and positioning step F, the artificial intelligence server 5 analyzes the signal strength of the positioning signal of the positioning signal information and finds the corresponding signal strength combination from the position signal information table. Since the positioning signal information corresponds to the current position of the automatic vehicle 21, the tag unit 22 receives the signal information transmitted by the wireless network signal transceiver 3, so the positioning information does not record the current position of the automatic vehicle 21. After the artificial intelligence server 5 receives the positioning signal information, the artificial intelligence server 5 will find the corresponding signal strength combination from the position signal information table according to the signal strength of the positioning signal described in the positioning signal information, for example In other words, the signal strength of the positioning signal of the positioning information is (-37, -37, -37, -38, -36)), and the artificial intelligence server 5 finds out from the position signal information table {a111 : The combination of ﹛-37, -37, -38, -38, -36﹜﹜ is more consistent with the signal strength of the positioning information, so it can be judged that the current position of the automatic vehicle 21 is near the a111 grid point 62, The user can learn the location of the automatic vehicle 21 through the artificial intelligence server 5, so as to grasp the work progress and movement of the automatic vehicle 21 in real time.

藉由於該學習分析步驟D中，該人工智慧伺服器5依該位置訊號資訊表中的每一訊號強度組合的資訊分析模擬出該標籤單元22未偵測的網格點62上的訊號資訊，並將模擬出的每一訊號資訊整合入該位置訊號資訊表儲存，且配合於該偵測定位步驟E中，當該標籤單元22接收到所述定位訊號，而該標籤單元22將所述定位訊號儲存成對應的定位訊號資訊且傳送至該人工智慧伺服器5，以及於該分析定位步驟F中，該人工智慧伺服器5分析該定位訊號資訊的所述定位訊號的訊號強度且從該位置訊號資訊表中找出對應訊號強度組合，達成室內定位的目的。而且透過該人工智慧伺服器5可分析模擬出該標籤單元22未偵測的網格點62上的訊號資訊的設計，改善以往該標籤單元22需要偵測該工作場域60的大數量的網格點62的問題，有效節省時間與人力並兼具確保室內精準定位的目的。In the learning analysis step D, the artificial intelligence server 5 simulates the signal information on the grid points 62 not detected by the tag unit 22 according to the information analysis of each signal intensity combination in the position signal information table, And integrate each simulated signal information into the location signal information table for storage, and cooperate in the detection and positioning step E. When the tag unit 22 receives the location signal, the tag unit 22 will locate the location signal. The signal is stored as the corresponding positioning signal information and sent to the artificial intelligence server 5, and in the analyzing and positioning step F, the artificial intelligence server 5 analyzes the signal strength of the positioning signal of the positioning signal information and obtains data from the position Find the corresponding signal strength combination in the signal information table to achieve the purpose of indoor positioning. In addition, the artificial intelligence server 5 can analyze and simulate the design of the signal information on the grid points 62 that the tag unit 22 does not detect, which improves the design of the large number of grids that the tag unit 22 needs to detect the work field 60 in the past. The problem of grid 62 effectively saves time and manpower and has the purpose of ensuring accurate indoor positioning.

綜上所述，本發明無線定位方法，藉由該學習分析步驟D中，該人工智慧伺服器5模擬出該標籤單元22未偵測的網格點62上的訊號資訊且整合入該位置訊號資訊表儲存，且配合於該偵測定位步驟E中，而該標籤單元22將該定位訊號資訊傳送至該人工智慧伺服器5，以及於該分析定位步驟F中，該人工智慧伺服器5從該位置訊號資訊表中找出對應該定位訊號資訊的訊號強度組合，達成室內定位的目的。In summary, in the wireless positioning method of the present invention, through the learning and analyzing step D, the artificial intelligence server 5 simulates the signal information on the grid points 62 not detected by the tag unit 22 and integrates the position signal The information table is stored and coordinated in the detection and positioning step E, and the tag unit 22 transmits the positioning signal information to the artificial intelligence server 5, and in the analysis and positioning step F, the artificial intelligence server 5 receives The signal strength combination corresponding to the positioning signal information is found in the position signal information table to achieve the purpose of indoor positioning.

惟以上所述者，僅為本發明之實施例而已，當不能以此限定本發明實施之範圍，凡是依本發明申請專利範圍及專利說明書內容所作之簡單的等效變化與修飾，皆仍屬本發明專利涵蓋之範圍內。However, the above are only examples of the present invention. When the scope of implementation of the present invention cannot be limited by this, all simple equivalent changes and modifications made in accordance with the scope of the patent application of the present invention and the content of the patent specification still belong to This invention patent covers the scope.

1:無線定位系統 61:網格 2:自動車裝置 62:網格點 21:自動車 A:初始移動步驟 22:標籤單元 B:訊號收集步驟 3:無線網路訊號收發器 C:資料整合傳輸步驟 4:資料伺服器 D:學習分析步驟 5:人工智慧伺服器 E:偵測定位步驟 60:工作場域 F:分析定位步驟1: wireless positioning system 61: Grid 2: Automatic vehicle device 62: grid points 21: Automatic car A: Initial moving steps 22: label unit B: Signal collection steps 3: Wireless network signal transceiver C: Data integration and transmission steps 4: Data server D: Learn the analysis steps 5: Artificial Intelligence Server E: Detection and positioning steps 60: Workplace F: Analysis and positioning steps

本發明之其他的特徵及功效，將於參照圖式的實施方式中清楚地呈現，其中： 圖1是一方塊示意圖，說明本發明無線定位方法之實施例中，一無線定位系統配合應用於一工作場域；及 圖2是一流程圖，說明本發明無線定位方法之實施例的步驟。Other features and effects of the present invention will be clearly presented in the embodiments with reference to the drawings, in which: Figure 1 is a schematic block diagram illustrating an embodiment of the wireless positioning method of the present invention, a wireless positioning system is used in conjunction with a working field; and Figure 2 is a flowchart illustrating the steps of an embodiment of the wireless positioning method of the present invention.

A:初始移動步驟A: Initial moving steps

B:訊號收集步驟B: Signal collection steps

C:資料整合傳輸步驟C: Data integration and transmission steps

D:學習分析步驟D: Learn the analysis steps

E:偵測定位步驟E: Detection and positioning steps

F:分析定位步驟F: Analysis and positioning steps

Claims (5)

一種無線定位方法，應用於一無線定位系統配合應用於一工作場域，該工作場域包含多個網格，及多個網格點，該無線定位系統包含一自動車裝置、多個無線網路訊號收發器、一資料伺服器，及一人工智慧伺服器，該自動車裝置包括一自動車，及一設置於該自動車上的標籤單元，該無線定位方法包含： 一初始移動步驟，該自動車受控制地在該工作場域內依一預設路線移動且使該自動車經過該工作場域中所需經過的網格； 一訊號收集步驟，於該自動車經過所需經過的網格的過程中，該標籤單元會偵測所需偵測的網格點且該標籤單元會依一偵測工作表在經過所需偵測的每一網格點時，該標籤單元發送一偵測訊息，而當每一無線網路訊號收發器接收到該偵測訊息而傳送一訊號資訊至該標籤單元，並該標籤單元在所需偵測的每一網格點上取得所述無線網路訊號收發器傳送的所述訊號資訊，而該標籤單元依所需偵測的每一網格點的位置座標而將在所需偵測的每一網格點上接收到所述訊號資訊儲存成一訊號強度組合； 一資料整合傳輸步驟，該標籤單元將所需偵測的每一網格點對應的訊號強度組合傳送至該資料伺服器且該資料伺服器將所述訊號強度組合依其對應的網格點的位置座標整合成一位置訊號資訊表儲存，並該人工智慧伺服器經該資料伺服器取得該位置訊號資訊表； 一學習分析步驟，該人工智慧伺服器分析該位置訊號資訊表且依該位置訊號資訊表中的每一訊號強度組合的資訊分析模擬出該標籤單元未偵測的網格點上的訊號資訊，並該人工智慧伺服器將模擬出的每一組訊號資訊依對應的網格點的位置座標儲存成對應的訊號強度組合而整合入該位置訊號資訊表儲存； 一偵測定位步驟，當該自動車在該工作場域內且該標籤單元發送一定位訊息，而當每一無線網路訊號收發器接收到該定位訊息並傳送一定位訊號至該標籤單元，而該標籤單元將所述定位訊號儲存成一定位訊號資訊傳送至該人工智慧伺服器；及 一分析定位步驟，而該人工智慧伺服器分析該定位訊號資訊的所述定位訊號的訊號強度且從該位置訊號資訊表中找出對應訊號強度組合。A wireless positioning method applied to a wireless positioning system in cooperation with a working field, the working field including multiple grids and multiple grid points, the wireless positioning system including an automatic vehicle device and multiple wireless networks A signal transceiver, a data server, and an artificial intelligence server. The automatic vehicle device includes an automatic vehicle and a tag unit provided on the automatic vehicle. The wireless positioning method includes: An initial movement step, where the automatic vehicle is controlled to move in the work field according to a preset route and the automatic vehicle passes through a grid that needs to be passed in the work field; A signal collection step. During the process of the automatic vehicle passing through the grid, the tag unit will detect the grid points to be detected and the tag unit will pass the required detection according to a detection worksheet. At each grid point, the tag unit sends a detection message, and when each wireless network signal transceiver receives the detection message, it sends a signal information to the tag unit, and the tag unit is The signal information transmitted by the wireless network signal transceiver is obtained at each grid point detected, and the tag unit will be detected at the desired location according to the position coordinates of each grid point to be detected The signal information received at each grid point of is stored as a signal strength combination; In a data integration transmission step, the tag unit sends the signal strength combination corresponding to each grid point to be detected to the data server, and the data server transmits the signal strength combination according to the corresponding grid point The position coordinates are integrated into a position signal information table for storage, and the artificial intelligence server obtains the position signal information table through the data server; In a learning and analysis step, the artificial intelligence server analyzes the position signal information table and analyzes and simulates the signal information on the grid points not detected by the tag unit according to the information analysis of each signal intensity combination in the position signal information table, And the artificial intelligence server stores each set of simulated signal information according to the position coordinates of the corresponding grid points into a corresponding signal strength combination and integrates it into the position signal information table for storage; A detection and positioning step, when the automatic vehicle is in the working field and the tag unit sends a positioning message, and when each wireless network signal transceiver receives the positioning message and sends a positioning signal to the tag unit, and The tag unit stores the positioning signal as a positioning signal information and sends it to the artificial intelligence server; and An analysis and positioning step, and the artificial intelligence server analyzes the signal strength of the positioning signal of the positioning signal information and finds the corresponding signal strength combination from the position signal information table. 如請求項1所述的無線定位方法，其中，於該訊號收集步驟中，每一訊號強度組合具有對應每一網格點的一位置座標，及多個訊號強度，每一訊號強度組合的所述訊號強度為每一訊號強度組合對應的每一網格點上，該標籤單元接收所述無線網路訊號收發器傳送的訊號強度。The wireless positioning method according to claim 1, wherein, in the signal collection step, each signal strength combination has a position coordinate corresponding to each grid point, and a plurality of signal strengths, and all the signal strength combinations are The signal strength is at each grid point corresponding to each signal strength combination, and the tag unit receives the signal strength transmitted by the wireless network signal transceiver. 如請求項2所述的無線定位方法，其中，於該學習分析步驟中，該人工智慧伺服器分析該位置訊號資訊表且依該位置訊號資訊表中的每一訊號強度組合的位置座標與所述訊號強度分析模擬出該標籤單元未偵測的網格點上的訊號資訊，並該人工智慧伺服器將模擬出的每一組訊號資訊依對應的網格點的位置座標儲存成對應的訊號強度組合且整合入該位置訊號資訊表儲存。The wireless positioning method according to claim 2, wherein, in the learning analysis step, the artificial intelligence server analyzes the position signal information table and according to the position coordinates of each signal intensity combination in the position signal information table and the position The signal strength analysis simulates the signal information on the grid points not detected by the tag unit, and the artificial intelligence server stores each set of simulated signal information into corresponding signals according to the position coordinates of the corresponding grid points The intensity combination is integrated into the position signal information table for storage. 如請求項3所述的無線定位方法，其中，於該學習分析步驟中，該人工智慧伺服器分析該位置訊號資訊表中的每一訊號強度組合對應的位置座標與對應的所述訊號強度且依所述訊號強度組合的位置座標彼此的距離與所述訊號強度組合對應的所述訊號強度進行分析運算，而模擬出該標籤單元未偵測的網格點上的訊號資訊。The wireless positioning method according to claim 3, wherein, in the learning analysis step, the artificial intelligence server analyzes the position coordinates corresponding to each signal strength combination in the position signal information table and the corresponding signal strength, and According to the distance between the position coordinates of the signal intensity combination and the signal intensity corresponding to the signal intensity combination, an analysis operation is performed to simulate the signal information on the grid points not detected by the tag unit. 如請求項2所述的無線定位方法，其中，於該訊號收集步驟中，該標籤單元會依該偵測工作表在經過所需偵測的每一網格點時，該標籤單元會發送一預定次數的該偵測訊息，而當所述無線網路訊號收發器每次接收到該偵測訊息而傳送對應的訊號資訊至該標籤單元，並該標籤單元在所需偵測的每一網格點上取得所述無線網路訊號收發器傳送的所述訊號資訊後，該標籤單元會依所需偵測的每一網格點的位置座標而將所需偵測的每一網格點上接收到每一無線網路訊號收發器所傳送的該預定次數的所述訊號資訊的訊號強度取平均值而儲存成該訊號強度組合。The wireless positioning method according to claim 2, wherein, in the signal collection step, the tag unit will send a signal when passing through each grid point to be detected according to the detection worksheet. A predetermined number of the detection messages, and when the wireless network signal transceiver receives the detection message every time, it sends the corresponding signal information to the tag unit, and the tag unit is in each network that needs to be detected. After obtaining the signal information transmitted by the wireless network signal transceiver on the grid point, the tag unit will detect each grid point according to the position coordinates of each grid point to be detected The signal strength of the predetermined number of signal information received from each wireless network signal transceiver is averaged and stored as the signal strength combination.
TW109111146A 2020-04-01 2020-04-01 Wireless positioning method TWI726671B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
TW109111146A TWI726671B (en) 2020-04-01 2020-04-01 Wireless positioning method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
TW109111146A TWI726671B (en) 2020-04-01 2020-04-01 Wireless positioning method

Publications (2)

Publication Number Publication Date
TWI726671B TWI726671B (en) 2021-05-01
TW202138840A true TW202138840A (en) 2021-10-16

Family

ID=77036620

Family Applications (1)

Application Number Title Priority Date Filing Date
TW109111146A TWI726671B (en) 2020-04-01 2020-04-01 Wireless positioning method

Country Status (1)

Country Link
TW (1) TWI726671B (en)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
TWI774555B (en) * 2021-09-10 2022-08-11 神達電腦股份有限公司 Indoor wireless positioning method

Family Cites Families (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR101369135B1 (en) * 2006-06-21 2014-03-05 엘지전자 주식회사 Mehtod for supproting quality of multimeida broadcast multicast service(mbms) in mobile communications system and terminal thereof
US9078232B2 (en) * 2013-05-01 2015-07-07 Qualcomm, Incorporated Methods and apparatuses for characterizing and affecting mobile device location accuracy and/or uncertainty
CA2978714C (en) * 2015-03-06 2019-04-02 Gatekeeper Systems, Inc. Low-energy consumption location of movable objects
WO2016164031A1 (en) * 2015-04-09 2016-10-13 Corvus Technologies Corp Beacon and associated components for a ranging system
CN107484118A (en) * 2016-06-07 2017-12-15 滴滴（中国）科技有限公司 A kind of indoor scene localization method and system based on building WiFi fingerprints
US10477342B2 (en) * 2016-12-15 2019-11-12 David H. Williams Systems and methods of using wireless location, context, and/or one or more communication networks for monitoring for, preempting, and/or mitigating pre-identified behavior
US10498477B2 (en) * 2017-02-15 2019-12-03 Qualcomm Incorporated Tracking reference signal configuration design

Also Published As

Publication number Publication date
TWI726671B (en) 2021-05-01

Similar Documents

Publication Publication Date Title
CN106294179B (en) Analog detection method and server in process of application development
JP3651850B2 (en) Mobile communication terminal test equipment
Xu et al. Pedestrain monitoring system using Wi-Fi technology and RSSI based localization
CN102198003B (en) Limb movement detection and evaluation network system and method
CN106874187A (en) code coverage collection method and device
RU2013125478A (en) LIMITED RESOURCE IN A WIRELESS NETWORK
CN109413678A (en) A kind of Wi-Fi module automatic test approach and test macro
CN103906107A (en) Data collecting type WLAN testing system and method based on WIFI terminal
TWI726671B (en) Wireless positioning method
CN108235355B (en) Environment simulation method and device
CN107360552A (en) A kind of indoor orientation method of multi-categorizer overall situation dynamic fusion
CN111625939A (en) Scale evaluation system and method for vehicle-road cooperative application
CN107770786A (en) A kind of method of testing of network performance, equipment and system
CN105024873A (en) Protocol conformance testing system and method based on equipment and scene simulation
CN110443562A (en) A kind of positioning attendance management method and system
CN101521928A (en) Positioned mobile alarm method and system based on mobile network
CN113993064B (en) Wireless positioning method
CN110536334B (en) WLAN-based room division signal quality assessment method and device
CN109714733A (en) The detection restoration methods and Internet of things system of Internet of Things communication failure
TWI774555B (en) Indoor wireless positioning method
CN105024772A (en) Channel model simulation method and system for micro-power wireless communication
Huang et al. Comparison of automatic tracking and clustering algorithms for time-variant multipath components
EP2785078A1 (en) Method for attendance registration
CN104937987B (en) A kind of measurement feedback method, terminal and base station
CN105591827A (en) Method of generating WIA-PA (Wireless networks for Industrial Automation-Process Automation) protocol test set based on equipment life cycle Petri net