Personnel positioning system and positioning method based on cloud platform

Abstract

The personnel positioning system based on the cloud platform comprises a software system and intelligent hardware equipment, and the software system is provided with an Internet of Things access server, an Internet of Things platform and the cloud platform; the intelligent hardware equipment comprises a Bluetooth beacon, an intelligent safety helmet and an Internet of Things base station; indoor positioning of a building is carried out through a positioning device on the intelligent safety helmet and a Bluetooth beacon, and outdoor tracking is carried out through a GNSS; the indoor positioning data is subjected to preliminary screening and further standardization processing, and finally, a simple and effective positioning logic calculation method is adopted, so that the position information of all on-site personnel is quickly obtained, scheduling management is facilitated, the personnel management efficiency is improved, and the safety of personnel management is also improved due to an abnormal alarm of a positioning system.

Description

Technical Field

[0001] The present invention relates to the technical field of personnel positioning, and more specifically, to a personnel positioning system and method based on a cloud platform. Background Art

[0002] Currently, there are a large number of construction workers on construction sites, and their qualities vary. They are one of the main sources of project costs and safety risks. Controlling the on-site workers is crucial for the efficient and safe progress of the project. Taking inventory of on-site personnel and managing personnel attendance are difficult. Currently, face turnstiles are mainly used for automatic statistics of personnel attendance, but problems such as face swiping on behalf of others and difficulty in controlling leaving after face swiping often occur; it is a major difficulty in project management to keep track of the presence of personnel on the project in real time. In terms of safety management, there is no alarm reminder for staying at the construction site during abnormal time periods (such as at night) or entering restricted areas.

[0003] Patent document CN108174371A discloses a construction site personnel positioning system and method, including RFID tags, wireless base stations, main base stations, fiber optic switches, and servers; the RFID tags are set on safety helmets, and several of the wireless base stations are set within the construction site, which are used to detect the information stored in the RFID tags within the construction site; the base station is communicatively connected to the wireless base station through a wireless network, which is used to read the information data of the RFID tags detected by the wireless base station, realizing real-time supervision and refined management of construction site personnel, simplifying the installation process, improving the understanding of the number and location of personnel, and enhancing the accuracy of construction site management. However, this method requires a large number of wireless base stations to be set up, and it is impossible to accurately position the location of construction workers.

[0004] Patent document CN107071734A discloses an intelligent safety helmet multi-functional positioning system and method. It includes a GSM module, a GPS module, a Bluetooth module, and a microprocessor installed on the safety helmet, an iBeacon device installed on the indoor ceiling, and a server set in the cloud; the microprocessor is electrically connected to the GSM module, the GPS module, and the Bluetooth module respectively, the Bluetooth module is communicatively connected to the iBeacon device through a Bluetooth network, and the GSM module is communicatively connected to the server through a network. The present invention respectively uses the GSM module, the GPS module, and the Bluetooth module to collect the real-time positioning information of the safety helmet, accurately obtains the real-time geographical location information of the user by using the GSM+GPS+Bluetooth method, and uses the server to view the geographical location information of the user in real time, realizing accurate outdoor positioning of the user. However, this device cannot accurately judge whether a worker is in danger. Summary of the Invention

[0005] The present invention overcomes the deficiencies of the prior art and provides a simple and effective personnel positioning system for a cloud platform, including a software system and intelligent hardware devices. The software system includes an Internet of Things access server, an Internet of Things platform, and a cloud platform. The intelligent hardware devices include Bluetooth beacons, intelligent safety helmets, and Internet of Things base stations. It is characterized in that a Bluetooth module, a locator, an acceleration sensor, and a communication device are arranged inside the intelligent safety helmet. The communication device transmits the information data collected through the intelligent safety helmet to the Internet of Things base station. The Internet of Things base station communicates with the Bluetooth beacon and transmits the data to the Internet of Things access server. The Internet of Things platform analyzes the data of the Internet of Things access server and forwards the analyzed data to the cloud platform. The cloud platform receives the analyzed data transmitted by the Internet of Things platform and then performs positioning logic calculations to determine the specific positions of the safety helmet and the personnel.

[0006] Further, as a preference, the Bluetooth beacon is built-in with a BLE Bluetooth module and is used in cooperation with the locator on the intelligent safety helmet. The Bluetooth beacons are arranged at intervals of 10 to 15 meters.

[0007] Further, as a preference, the acceleration sensor is a three-axis acceleration sensor, which is used to determine whether a person wears a safety helmet and whether the person falls into a dangerous state, and gives a prompt alarm. At the same time, it also has a step counting function and an alarm function.

[0008] Further, as a preference, the locator uses a GNSS signal module to locate outdoor personnel.

[0009] Further, as a preference, the barometer collects the atmospheric pressure value and converts it into a floor height value.

[0010] Further, as a preference, the communication device transmits the information of the Bluetooth beacon, the information of the barometer, and the information on the accelerometer collected through a LoRaWAN communication module to the Internet of Things base station.

[0011] The present invention also provides a positioning method for the above-mentioned personnel system based on a cloud platform, which is characterized by including the following steps:

[0012] Step 1, configure the software and hardware of the personnel positioning system for the construction cloud platform, obtain an artificial calibration data set, and store it in the first data unit of the Internet of Things server.

[0013] Step 2: Search for signals through the Bluetooth module and locator of the intelligent safety helmet, connect to the Internet of Things base station through the communication device. The Internet of Things base station receives the safety helmet data and transmits the collected Bluetooth beacon information, barometer information, information on the accelerometer, and location information to the Internet of Things server; and stores the Bluetooth beacon information and barometer information in the second data unit of the Internet of Things server, and stores the longitude and latitude data in the location information in the third data unit of the Internet of Things server.

[0014] Step 3: Based on the altitude data collected by the barometer, preliminarily screen the data range of the second data unit to determine which floor the data is on, and then filter out the beacons received on other floors.

[0015] Step 4: For the data set of the second data unit after preliminary screening, use the Altman-Z model algorithm based on normal distribution to perform data screening and analysis to obtain a standard data set.

[0016] Step 5: Based on the standard data set, perform location calculation.

[0017] Step 6: The construction cloud platform displays the personnel location information.

[0018] Furthermore, the specific steps of Step 4 include:

[0019] Step S41: Calculate the sample variance of the data set of the second data unit after preliminary filtering:

[0020]

[0021] σ represents the standard deviation, R in = {r1, r2, r3,..., rn}, n represents the number of data points, r i represents the i-th data point, and μ represents the average value of the data.

[0022] Substitute the intensity value of each sample point and the total number of all sample points in the second data unit into the above standard deviation formula to calculate the corresponding standard deviation of each sample point in the data set.

[0023] Step S42: Use the calculated standard deviation of all sample points to calculate the standardized data set Z i , and the model algorithm is as follows:

[0024]

[0025] Substitute the RSSI of the intensity value of each sample point in the second data unit to obtain the standardized sample data set R i = {R1, R2,... R n};

[0026] Step S43: Perform data screening and analysis based on the Z i value to obtain a standard data set.

[0027] Further, step 5 specifically includes:

[0028] S51: In the standard data set, select the maximum RSSI value of the signal transmitters from three known positions for the point to be located;

[0029] S52: Use the signal attenuation model to convert the RSSI value into the distance from the point to be located to each signal transmitter;

[0030] S53: Draw three circles with the three signal transmitters as the centers and the distances between them and the point to be located as the radii. The intersection of these three circles is the coordinate of the point to be located.

[0031] Further, perform data screening and analysis based on the Z i value to obtain the standard data set. The specific method is that when Zi is greater than 0, it means the data is greater than the mean; when it is less than 0, it means it is greater than the mean; when it is equal to 0, it means it is equal to the mean; when it is equal to 1, it means the data is one standard deviation larger than the mean; when it is equal to -1, it means the data is one standard deviation smaller than the mean. Set the allowable range as [-3, 3]. Values exceeding this range are determined as outliers and discarded.

[0032] The present invention has the following advantages and beneficial effects:

[0033] (1) Integrate software systems such as the Internet of Things access server, Internet of Things platform, and cloud platform with intelligent hardware devices such as Bluetooth beacons, intelligent safety helmets, and Internet of Things base stations to build a complete personnel positioning architecture. The data collected by the intelligent safety helmet is processed and transmitted layer by layer through the base station, access server, Internet of Things platform to the cloud platform, realizing the efficient flow and accurate analysis of personnel positioning information from collection to presentation, and laying a solid technical foundation for personnel management in complex construction sites. The intelligent safety helmet integrates a Bluetooth module, a locator (GNSS signal module for outdoor positioning), an acceleration sensor (a three-axis acceleration sensor realizes functions such as wearing, falling, step counting, and alarm), a barometer (converting floor height to assist in positioning), and a communication device (LoRaWAN module for data transmission); multiple sensors cooperate to capture personnel movement, environment, and position data in all directions, breaking through the limitations of traditional positioning, achieving seamless indoor and outdoor switching for accurate positioning, improving the adaptability and reliability of the system, and meeting the needs of diverse scenarios.

[0034] (2) The positioning data is optimized through double screening. The barometer altitude data preliminarily filters out interference beacons outside the floor, and the indoor positioning data is deeply purified by the Altman-Z model algorithm based on normal distribution. According to the statistical characteristics of the data, outliers are removed and standardized processing is carried out, significantly reducing Bluetooth positioning noise and errors, and outputting a high-precision standard data set, providing a high-quality data source for precise positioning, and enhancing the stability and accuracy of positioning. Outdoor, the latitude and longitude positioning is directly read according to the GNSS signal; indoors, the RSSI three-point positioning method is combined with the signal attenuation model, and the maximum RSSI values of three points are selected from the processed data set to calculate the coordinates by distance conversion. The algorithm balances accuracy, speed and computational cost, and solves the position with simple and efficient geometric operations, highlighting its advantages in scenarios with changing construction environments and moderate accuracy requirements, realizing fast and accurate indoor positioning, and optimizing the timeliness and accuracy of personnel positioning.

[0035] (3) Real-time and accurately locate all personnel at the construction site, solve the problems of personnel attendance, inventory and on-site status monitoring, avoid loopholes such as proxy swiping of face turnstiles, and dynamically master the personnel distribution and trajectories. The cloud platform visual display is convenient for dispatching and command. According to the location information, tasks are reasonably allocated and resources are allocated, improving construction efficiency and collaboration, reducing costs and increasing efficiency, and empowering project management; the acceleration sensor continuously monitors the safety status of personnel, and automatically alarms and triggers rescue in case of anomalies (such as falls). The system sets alarm rules for abnormal time and area intrusion, strengthens the safety control line at the construction site, prevents accidents from occurring, promptly disposes of risks, comprehensively protects the lives of personnel, improves the intelligent and refined level of construction site safety management, and meets the requirements of safe production development. Brief Description of the Drawings

[0036] Figure 1 is the architecture diagram of the cloud platform personnel positioning system of the present invention;

[0037] Figure 2 is the positioning flow chart of the cloud platform personnel positioning system of the present invention;

[0038] Figure 3 is the physical diagram of the safety helmet of the cloud platform personnel positioning system of the present invention;

[0039] Figure 4 is the calculation principle diagram of the cloud platform personnel positioning system of the present invention. Detailed Embodiments

[0040] To make the technical solutions and advantages of the present invention clearer, the present invention and its beneficial effects will be further described in detail below in combination with the detailed embodiments and the drawings of the specification. However, the embodiments of the present invention are not limited thereto.

[0041] In the present invention, "at least one" means one or more, and "a plurality" means two or more. "At least one of the following" or a similar expression means any combination of these items, including any combination of single item(s) or plural item(s). For example, "at least one of a, b, or c", or "at least one of a, b, and c" can both represent: a, b, c, a - b (i.e., a and b), a - c, b - c, or a - b - c, where a, b, and c can be single or multiple respectively.

[0042] Please refer to Figure 1 As shown, a personnel positioning system for a cloud platform according to the present invention includes a software system and intelligent hardware devices. The software system includes an Internet of Things access server, an Internet of Things platform, and a cloud platform; the intelligent hardware devices include Bluetooth beacons, intelligent safety helmets, and Internet of Things base stations.

[0043] The Bluetooth beacon is built - in with a BLE Bluetooth module, which broadcasts a BLE (Bluetooth) signal outward once every 100 ms. The signal carries the unique identifier UUID of the beacon and is used in conjunction with the locator on the intelligent safety helmet to accurately identify the signal of the safety helmet indoors. The Bluetooth beacon adopts the low - power Bluetooth BLE broadcast protocol and can have a battery life of 2 - 3 years when powered by a battery. The Bluetooth beacons are arranged according to the actual on - site structure, with an interval of 10 - 15 meters. For example, in a certain floor of a building with rooms such as an office area, a meeting area, and a functional area, they can be arranged in representative rooms according to these functions.

[0044] The locator on the intelligent safety helmet is built - in with a 3 - axis acceleration sensor. By detecting the magnitude and change of the acceleration value, it can determine whether a person is wearing a safety helmet and whether they have fallen into a dangerous state, and give an alarm prompt. At the same time, it also has a step - counting function. It has a search and rescue mode with an alarm function, including emergency alarm, area detection alarm, and stay alarm. Hardware - wise, it manages information such as DevEUI and ID through an NFC chip, charges magnetically, and performs over - the - air upgrade via Bluetooth.

[0045] The locator on the intelligent safety helmet is also built - in with a BLE Bluetooth module, a GNSS signal module, a LoRaWAN communication module, and a barometer. The BLE Bluetooth module is used in conjunction with the Bluetooth beacon to wake up the device at regular intervals, search for nearby BLE broadcast signals, record the UUID (the unique identifier of each beacon) of the signal, and the signal strength (RSSI). The GNSS signal module is mainly for personnel positioning outdoors. The barometer collects the atmospheric pressure value. Different atmospheric pressure values correspond to different altitudes. By combining the altitude and the floor height of a single floor, the specific floor where the safety helmet is located can be determined. The LoRaWAN communication module transmits data such as the information of the Bluetooth beacon, the barometer, and the information on the accelerometer collected through this low - power transmission technology to the Internet of Things base station.

[0046] The IoT base station connects a large number of on-site installed Bluetooth beacons, manages them, realizes data reception and communication of all beacons, and then transmits the data of the Bluetooth beacons to the IoT access server.

[0047] The IoT access server receives the data from the IoT base station, processes the data according to the LoRaWAN standard, and forwards the data to the IoT platform.

[0048] The IoT platform parses the data from the IoT access server and forwards it to the construction cloud platform. Specifically, it realizes the reception, parsing, and transmission of locator data. The information carried by the data is parsed into the corresponding safety helmet, and the information of the collected Bluetooth beacon, including its number, signal strength, etc., is interpreted and then transmitted to the cloud platform.

[0049] The cloud platform uses the construction cloud platform, an enterprise-level intelligent construction site platform, which integrates a variety of intelligent devices to intelligently manage the people, machines, materials, methods, and environment at the construction site. In this embodiment, a data model of the building is established, the parsed data transmitted by the IoT platform is received, and then positioning logic calculation is performed to determine the specific positions of the safety helmet and the personnel. At the same time, as the final customer management platform, effective overall control and display are carried out.

[0050] See Figure 3 The intelligent safety helmet in this embodiment is a locator installed at the rear end of a common safety helmet, using Bluetooth protocol 5.0, Bluetooth sensitivity: -96dBm, physical size 80X80X35mm. The installation accessories include 2 screws, 1 silicone rubber pad, and 1 locator clip. The silicone rubber pad is very soft and plays a role in shock protection for the locator. The locator clip fixes the locator. During installation, first drill two screw holes in the safety helmet, then use two screws to fix the silicone rubber pad and the locator clip on the helmet, and finally push the locator into the locator clip.

[0051] See Figure 2 , this embodiment also provides a positioning method for the cloud platform personnel positioning system, including the following steps:

[0052] Step 1, configure the software and hardware of the construction cloud platform personnel positioning system, obtain the artificial calibration data group, the i-th positioning data (x i , y i , z i ), (x i , y i ) are plane coordinates, z i is the height coordinate, and it is stored in the first data unit.

[0053] Among them, it is worth noting that the configuration of the software and hardware of the cloud platform personnel positioning system in step 1 specifically includes:

[0054] Step S11: Determine the positioning area through the electronic map and configure the monomer positions: Set the coordinates in the monomer floor module of the construction cloud management platform based on the map information in the background of the construction cloud platform.

[0055] Step S12: Configure the construction plan drawing: Import the design drawings of each floor into the platform through the newly created building monomers, so as to achieve the consistency between the display end of the cloud platform and the actual construction site plane. Figure 1 Consistency.

[0056] Step S13: Set the safety helmet locator: Through the real-name personnel management method, when personnel enter the site or safety helmets are issued, bind the safety helmets with the personnel information. Search for the safety helmets to be bound through the device numbers, enter the ID card numbers of the personnel to be bound, and enter the personnel information into the roster module under the personnel management unit. After wearing the safety helmet, data will be reported to the system platform when the personnel move for positioning.

[0057] Step S14: Install the positioning beacon and bind it to the location: Fix the beacon to the corresponding position on the floor plan of the building monomer according to the previously set building monomer and the supporting plan drawing. Arrange one every 10 - 15 meters to ensure full coverage of each floor. After the beacon is installed, use a mobile terminal device with the construction cloud platform APP to scan the QR code on the beacon, enter the position information of the floor plan corresponding to this beacon, and then click on the floor plan with your finger to complete the dotting. The construction cloud platform APP records the position of this point. This position information is manually calibrated data and is stored in the first data unit.

[0058] Step 2: Obtain wireless positioning data and store them in the second and third data units respectively: The locator switches between the Bluetooth and GNSS position reporting intervals for outdoor tracking. Assume that it does not receive any signals from Bluetooth beacons within the Bluetooth position reporting interval, then it enters the GNSS position reporting interval, and turns on the GNSS module 3 minutes before the end of each GNSS position reporting interval, and then sends the coordinates to the server. If it cannot obtain satellites within 3 minutes, it will stop the GNSS module and retry in the next cycle. At the same time, assume that a Bluetooth beacon is received within the GNSS position reporting interval, the locator will also stop the GNSS module and enter the Bluetooth receiving mode within the Bluetooth position reporting interval. At the end of each Bluetooth positioning interval, the locator always turns on the Bluetooth to receive for 3 seconds. It sends the information of the nearby beacons to the server.

[0059] The BLE Bluetooth module and GNSS signal module of the locator on the intelligent safety helmet search for signals. Through the Internet of Things base station compliant with the LoRaWAN standard, the safety helmet data is received and forwarded to the Internet of Things access server to obtain positioning data. The LoRaWAN communication module stores the collected Bluetooth beacon information, barometer information, and information on the accelerometer in the second data unit, and the longitude and latitude data of GNSS are stored in the third data unit of the Internet of Things server.

[0060] Step 3: Use the altitude data collected by the barometer to preliminarily screen the data range of the second data unit: Specifically, determine which floor the data is on, and then filter out the beacons received on other floors. The relative altitude is measured according to the following steps: 1) Place a sensor downstairs to measure the reference atmospheric pressure Pr. 2) Use the formula to calculate the reference altitude value Hr, where P0 is the standard atmospheric pressure (101.325 kPa). 3) Place another sensor upstairs or elsewhere to measure the atmospheric pressure Pn. 4) Use the same formula to calculate the altitude value Hn based on Pn. 5) The relative altitude = Hr - Hn, which is the value of the coordinate z in the first data unit. The i-th coordinate data is z i , and according to the floor-to-floor distance Δh of the building monomer, the relative altitude / Δh is the floor where it is located. The z values on the same floor are equal, and other different z value singularities are removed to complete the preliminary filtering. The relative altitude error is less than 0.1 m. Note: The atmospheric pressure should be measured at the same location, and the relative altitude error < 1.

[0061] Step 4: For the data set of the second data unit after preliminary screening, use the Altman-Z model algorithm based on normal distribution for data screening and analysis, specifically as follows:

[0062] Step S41: Calculate the sample variance of the data set of the second data unit after preliminary filtering:

[0063]

[0064] σ represents the standard deviation, R in = {r1, r2, r3,..., rn}, n represents the number of data points, and r i represents the i-th data point, and μ represents the average value of the data.

[0065] Substitute the intensity value of each sample point and the total number of all sample points in the second data unit into the above standard deviation formula respectively, and calculate the corresponding standard deviation of each sample point in the data set.

[0066] Step S42: Use the calculated standard deviation of all sample points to calculate the standardized data set Z according to the Altman-Z model algorithm i , and the model algorithm is as follows:

[0067]

[0068] Substitute the RSSI value of each sample point of the second data unit to obtain the standardized sample data set R i ={R1, R2, … R n};

[0069] Step S43: Calculate the Zi value according to the above step S42 for data screening and analysis; the specific analysis is as follows, Z i Greater than 0 indicates that the data is greater than the mean, less than 0 indicates greater than the mean, equal to 0 indicates equal to the mean, equal to 1 indicates that the data is one standard deviation larger than the mean, and equal to -1 indicates that the data is one standard deviation smaller than the mean. In this embodiment, the allowable range is set to [-3, 3], and values exceeding this range are determined as outliers and discarded.

[0070] The corresponding sample standard data set is obtained through the above Altman-Z model algorithm and stored in the adjusted second data unit.

[0071] Step 5, perform positioning calculation based on the standard data set R i obtained by the previous optimization. Fingerprint recognition method, least squares method, Kalman filtering, etc. can be used. Considering the required accuracy for on-site implementation, signal transmission rate, and computational complexity brought by the computational amount, the three-point positioning method is adopted in this embodiment.

[0072] In this embodiment, for the outdoor positioning data stored in the third data unit, the GNSS data will directly display the longitude and latitude values fed back by the corresponding satellites on the map navigation, and after the position information is confirmed, it jumps to step 6.

[0073] For the second data unit corresponding to the Bluetooth beacon, the basic principle of the positioning calculation adopted: The RSSI three-point positioning algorithm measures the RSSI values received by the point to be located from three signal emission points at different positions, converts these RSSI values into distances using the signal attenuation model, and then calculates the coordinates of the point to be located through geometric methods based on this distance information. In this embodiment, the three different RSSI values are the three largest values from the normalized second data unit R i in. The values for other algorithms can be obtained from this data set.

[0074] The signal attenuation model adopted: The relationship between the RSSI value and the distance d between the signal emission point and the receiving point can usually be expressed as: d = 10(10×n∣RSSI∣ - A)

[0075] Where: d is the distance, with the unit of meter (m). RSSI is the received signal strength, usually a negative value. A is the absolute value of the RSSI value when the transmitter and the receiver are 1 meter apart, and the optimal range is between 45 - 49. n is the environmental attenuation factor, which needs to be tested and corrected, and the optimal range is between 3.25 - 4.5.

[0076] Specific positioning calculation steps:

[0077] S51 Select the maximum RSSI value measured: In the standardized second data unit, select the maximum RSSI value of the signal transmitter points from three known positions measured at the point to be located.

[0078] Among them, the positions of all installed Bluetooth beacons are stored in the first data unit.

[0079] S52 Calculate the distance: Using the above signal attenuation model, convert the RSSI value into the distance from the point to be located to each signal transmitter point.

[0080] S53 Geometric positioning: Taking three signal transmitter points (x1, y1), (x2, y2), (x3, y3) as the centers of circles, and taking the distances d1, d2, d3 between them and the point to be located as the radii, draw three circles respectively. The intersection point of these three circles is the coordinate of the point to be located.

[0081] If the three circles intersect at one point, then this point is the exact position of the point to be located (x0, y0).

[0082]

[0083] In actual situations, due to measurement errors and environmental factors, the three circles may not exactly intersect at one point. At this time, the following strategy can be used for approximate calculation: If any two circles are tangent or intersect, the intersection points of these two circles can be found first, and then verified with the third circle to see if the intersection points are within the error tolerance range of the third circle; if no two circles are tangent or intersect, two intersection points can be found with two circles first, and then the distances from these two points to the center of the third circle can be calculated respectively, and the point closer to the third circle is selected as the approximate position of the point to be located.

[0084] When judging whether two circles intersect or are tangent, a reasonable error tolerance value needs to be set. In this embodiment, the error value is that the distance to the boundary of the third circle is within the range of 1 meter. The size of this value depends on the positioning accuracy requirements of the system and the actual application scenario. The RSSI three - point positioning algorithm is a simple and effective positioning technology, suitable for scenarios where the positioning accuracy requirements are not particularly high.

[0085] Step 6, the construction cloud platform displays the personnel positioning information.

[0086] The working principle of this specific implementation is as follows: The positioning signal is obtained through the locator installed on the intelligent safety helmet. When outdoors, GNSS signals are collected, and Bluetooth beacon signals are collected indoors. The Bluetooth beacons are installed on the flat floors inside the building and cover the entire area. The intelligent safety helmet sends the received positioning information to the Internet of Things base station, and then transmits it to the Internet of Things access service for processing of network standard protocols. Then, it is parsed on the Internet of Things platform and forwarded to the cloud platform. The positioning logic operation is performed on the cloud platform to determine the location of the safety helmet, and the corresponding personnel location is also determined and displayed on the screen. Among them, for the data of the Bluetooth beacons, the collected data is initially filtered through the barometer data. Secondly, the data is standardized through the Altman-Z model algorithm to obtain more accurate data. Finally, the signal attenuation model is used, and the three-point positioning method is adopted to calculate the accurate indoor location information, which is finally displayed on the construction cloud platform.

[0087] Through the locator installed on the intelligent safety helmet, the present invention binds the on-site personnel information, thereby realizing the full-process supervision of on-site construction personnel, which is of great benefit to personnel tracking, safety management, and production efficiency management.

[0088] Finally, the method of this application is only a preferred implementation, and is not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A personnel positioning system based on a cloud platform, comprising a software system and intelligent hardware devices. The software system includes an Internet of Things access server, an Internet of Things platform, and a cloud platform; the intelligent hardware devices include Bluetooth beacons, intelligent safety helmets, and Internet of Things base stations; characterized in that, The intelligent safety helmet is internally provided with a Bluetooth module, a locator, an acceleration sensor, and a communication device. The communication device transmits the information data collected by the intelligent safety helmet to the Internet of Things base station. The Internet of Things base station is communicatively connected to the Bluetooth beacon and transmits the data to the Internet of Things access server. The Internet of Things platform analyzes the data of the Internet of Things access server and forwards the analyzed data to the cloud platform. The cloud platform receives the analyzed data transmitted by the Internet of Things platform and then performs positioning logic calculation to determine the specific location of the safety helmet and the personnel.

2. The personnel positioning system based on a cloud platform according to claim 1, wherein The Bluetooth beacon is internally provided with a BLE Bluetooth module and is used in cooperation with the locator on the intelligent safety helmet. The Bluetooth beacons are arranged at intervals of 10 to 15 meters.

3. The personnel positioning system based on the cloud platform according to claim 1, wherein, The acceleration sensor is a three-axis acceleration sensor, which is used to determine whether the personnel is wearing a safety helmet and whether they have fallen into a dangerous state, prompt an alarm. At the same time, it also has a pedometer function and an alarm function.

4. The personnel positioning system based on a cloud platform according to claim 1, wherein The locator uses a GNSS signal module to locate outdoor personnel.

5. The personnel positioning system based on a cloud platform according to claim 1, characterized in that, The barometer collects the atmospheric pressure value and converts it into a floor height value.

6. The personnel positioning system based on a cloud platform according to claim 1, wherein The communication device transmits the information of the Bluetooth beacon, the barometer, and the acceleration sensor collected through the LoRaWAN communication module to the Internet of Things base station.

7. A positioning method for a personnel system based on a cloud platform according to any one of claims 1-6, characterized in that, It includes the following steps: Step 1, configure the software and hardware of the personnel positioning system of the construction cloud platform, obtain the manually calibrated data set, and store it in the first data unit of the Internet of Things server. Step 2, search for signals through the Bluetooth module and the locator on the intelligent safety helmet, connect to the Internet of Things base station through the communication device. The Internet of Things base station receives the safety helmet data and transmits the information of the Bluetooth beacon, the barometer, the acceleration sensor, and the positioning information to the Internet of Things server; And store the information of the Bluetooth beacon, the barometer, and the acceleration sensor in the second data unit of the Internet of Things server, and store the longitude and latitude data in the positioning information in the third data unit of the Internet of Things server. Step 3, through the height data collected by the barometer, preliminarily screen the data range of the second data unit to determine which floor the data is on, and then filter out the beacons received on other floors. Step 4, perform data screening and analysis on the data set of the second data unit after preliminary screening by using the Altman-Z model algorithm based on normal distribution to obtain a standard data set. Step 5, perform positioning calculation based on the standard data set. Step 6, the construction cloud platform displays the personnel positioning information.

8. The positioning method according to claim 7, wherein The specific content of step 4 includes: Step S41: Calculate the sample variance of the data set of the second data unit after preliminary filtering: σ represents the standard deviation, R in = {r1, r2, r3, …, rn}, where n represents the number of data points, and r i represents the i-th data point, and μ represents the average value of the data. Substitute the intensity value of each sample point and the total number of sample points in the second data unit into the above standard deviation formula respectively to calculate the corresponding standard deviation of each sample point in the data set. Step S42: Using the calculated standard deviation of all sample points, calculate the standardized dataset Z according to the Altman-Z model algorithm i , and the model algorithm is as follows: Substitute each sample point intensity value RSSI of the second data unit to obtain the standardized sample data set R i ={R1, R2, … R n}; Step S43: Based on the Z i value, perform data screening and analysis to obtain a standard data set.

9. According to the positioning method described in claim 7, characterized in that, The specific content of step 5 includes: S51: In the standard data set, select the maximum RSSI value of the signal emission points from three known positions for the point to be located; S52: Use the signal attenuation model to convert the RSSI value into the distance from the point to be located to each signal emission point. S53: Taking the three signal emission points as the centers and the distances between them and the point to be located as the radii, draw three circles respectively. The intersection point of these three circles is the coordinate of the point to be located.

10. According to the positioning method described in claim 8, wherein According to Z i Perform data screening and analysis based on the value to obtain the standard data set. The specific method is as follows: when Zi is greater than 0, it means the data is greater than the mean; when it is less than 0, it means the data is less than the mean; when it is equal to 0, it means the data is equal to the mean; when it is equal to 1, it means the data is one standard deviation larger than the mean; when it is equal to -1, it means the data is one standard deviation smaller than the mean. Set the allowable range to [-3, 3], and values exceeding this range are determined as outliers and discarded.

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