Fire rescue site positioning system and method based on Internet of Things
By deploying passive RFID cards and equipment integrating RFID card readers and inertial navigation sensors at fire rescue sites, combined with IoT technology, the reliability and safety of personnel positioning at fire rescue sites are solved, real-time and reliable positioning coverage is achieved.
Patent Information
- Application Number
- CN202510214403.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-02-26
- Publication Date
- 2025-05-23
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
The prior art is difficult to realize real-time positioning of personnel at fire rescue sites, especially in abnormal environments. Traditional indoor positioning technology relies on base stations and beacons, which has problems such as unreliable power supply and explosion risk.
The Internet of Things-based fire rescue site positioning system is adopted. The system deploys passive RFID cards in the to-local area and integrates ultra-high frequency RFID card reader and inertial navigation sensor on the personnel wearing equipment, combines RFID positioning and inertial navigation sensor data, and performs data analysis and optimization through the command and dispatch backend to lock the position of the to-local person in real time.
Real-time positioning at the fire rescue site is achieved, the reliability and safety of positioning is ensured, the risk of battery explosion is avoided, and the fire rescue area is covered without space restrictions.
Smart Images
Figure CN120034829A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of positioning technology, and in particular to a fire rescue scene positioning system and method based on the Internet of Things. Background Art
[0002] With the rapid development of mobile Internet of Things technology, outdoor GPS positioning and indoor positioning technology have also been greatly developed and popularized, and have penetrated into people's daily lives. Travel value-added services such as navigation, LBS (Location-Base-Service), and location sharing have become very mature; GPS positioning services can only be used in outdoor areas. Indoor positioning technologies such as Bluetooth, wifi, UWB, and sound waves that have become popular in recent years have also laid a good application foundation for indoor navigation and positioning, but all indoor positioning technologies rely on indoor base stations, beacons and other location anchors, otherwise they cannot be positioned. For abnormal environments such as fire rescue sites, power supply cannot be guaranteed, and battery-powered equipment will also have the risk of explosion, but the positioning of personnel at fire rescue sites, especially the real-time location of firefighters themselves, is very critical. Summary of the invention
[0003] The purpose of the present invention is to provide a fire rescue scene positioning system and method based on the Internet of Things to solve the problems raised in the prior art.
[0004] To achieve the above object, the present invention provides the following technical solution: a method for positioning a fire rescue scene based on the Internet of Things, the method comprising: Deploy passive RFID cards at key locations in the area to be located at preset intervals; The person to be located wears a device integrating an ultra-high frequency RFID card reader and an inertial navigation sensor and enters the area to be located; After the UHF RFID card reader is powered on, it automatically scans the passive RFID cards within a certain range around it, reads the ID information of the passive RFID cards, and obtains the RFID positioning information; The inertial navigation sensor measures the acceleration, angular velocity and magnetic field change of the person to be located, calculates the movement trajectory and direction of the person to be located, and obtains the inertial navigation sensor positioning information; The command and dispatch background receives the RFID positioning information and the inertial navigation sensor positioning information, and determines the real-time position of the person to be located through a data analysis algorithm.
[0005] According to the above scheme, passive RFID cards are deployed at key locations of the area to be located according to a preset spacing d; the preset spacing d is determined according to the environmental characteristics of the area to be located, the signal propagation characteristics and the effective scanning radius of the UHF RFID reader; the passive RFID card has unique ID information, and the ID information is bound to the deployment location point through the UHF RFID reader and uploaded to the positioning management background; The positioning management backend establishes a mapping relationship based on the ID information of the passive RFID card and the deployment location point to form a positioning reference database for storing, managing and querying the passive RFID card information.
[0006] According to the above scheme, after the person to be located enters the area to be located, the UHF RFID reader is powered on and started; The UHF RFID reader continuously and automatically scans passive RFID cards within a certain range around it, and when a passive RFID card is detected, reads the ID information of the passive RFID card; The UHF RFID card reader uploads the ID information of the passive RFID card to the command and dispatch background through wireless communication; In combination with the ID information of the passive RFID card and the positioning reference database, the command and dispatch background locks the position of the person to be located at the nearest passive RFID card position and obtains the RFID positioning information.
[0007] According to the above scheme, the inertial navigation sensor includes an accelerometer, a gyroscope and a magnetometer; the accelerometer measures the linear acceleration of the person to be located; the gyroscope measures the angular velocity of the person to be located; the magnetometer measures the orientation of the person to be located; The calculating of the movement trajectory and direction of the person to be located includes calculating the displacement of the person to be located through acceleration data, calculating the turning angle of the person to be located through angular velocity data, and determining the movement direction of the person to be located in combination with magnetometer data; The inertial navigation sensor positioning information is uploaded to the command and dispatch background via wireless communication.
[0008] According to the above scheme, the initial position of the person to be located is determined according to the RFID positioning information, and the movement trajectory and direction of the person to be located are measured according to the inertial navigation sensor positioning information; When the person to be located has not passed the passive RFID card, the inertial navigation sensor positioning information is optimized in real time by using the Kalman filter algorithm; When the person to be located passes by the next passive RFID card, the cumulative error of the inertial navigation sensor is corrected using the RFID positioning information, and the corrected data is used as the initial state of the Kalman filter to continue optimizing the positioning data.
[0009] According to the above solution, correcting the cumulative error of the inertial navigation sensor using the RFID positioning information includes: Taking the RFID positioning information as the accurate position at the current moment, resetting the displacement of the person to be located calculated by the inertial navigation sensor; calculating the speed of the person to be located again according to the difference between the RFID positioning information and the position at the previous moment; correcting the movement direction of the person to be located using the inertial navigation sensor positioning information and the RFID positioning information; After correction, the inertial navigation sensor continues to measure the acceleration, angular velocity, and magnetic field change of the person to be located, and calculates the movement trajectory and direction; When the person to be located passes by the next passive RFID card, error correction is performed again.
[0010] According to the above solution, determining the real-time position of the person to be located through the data parsing algorithm includes: Fusing the RFID positioning information and the inertial navigation positioning information through the Kalman filter algorithm; The Kalman filter algorithm optimizes the positioning data of the inertial navigation sensor through prediction and update, and performs correction using the RFID positioning information; The prediction includes predicting the position and speed at the current moment according to the position and speed at the previous moment in the inertial navigation positioning information, combining with the motion model, and calculating the covariance matrix of the prediction result; the motion model is used to describe the motion law of the person to be located; the covariance matrix of the prediction result is used to represent the uncertainty of the prediction result; The formula for predicting the position and speed at the current moment is as follows: ; Where represents the position and speed at the current moment; F k represents the state transition matrix, describing how the state changes over time; x k-1 represents the state at the previous moment; B k represents the control input matrix; u k represents the control input vector; The formula for the covariance matrix of the prediction result is as follows: ; Where represents the prediction covariance matrix at the current moment; P k-1 represents the covariance matrix at the previous moment; Expressed as the transpose of the state transfer matrix; Q k Expressed as the process noise covariance matrix; The updating includes taking the RFID positioning information as an observation value, calculating a Kalman gain, wherein the Kalman gain is expressed as a weight of a prediction result and the observation value; based on the Kalman gain, using the RFID positioning information, correcting the predicted current moment position and speed, obtaining an optimized current moment position and speed, and updating a covariance matrix; The Kalman gain is formulated as follows: ; Among them, K k Expressed as Kalman gain; H k Represented as an observation matrix; Represented as the transpose of the observation matrix; R k denoted as the observation noise covariance matrix and denoted as the uncertainty of the observations; The optimized current position and speed are as follows: ; Among them, x k Represents the optimized current position and speed; z k It is represented as the observation value at the current moment, i.e., RFID positioning information; The updated covariance matrix is formulated as follows: ; Among them, P k It is represented as the updated covariance matrix; I is represented as the identity matrix; The optimized current moment position and speed are used as input at the next moment, and the prediction and update steps are repeated to achieve continuous positioning optimization.
[0011] A fire rescue scene positioning system based on the Internet of Things, the system includes: a passive RFID card, an RFID reader, an inertial navigation sensor module, a wireless communication module, a positioning management background and a command and dispatch background; The passive RFID cards are used to be distributed at preset intervals and deployed at key locations in the area to be positioned as positioning reference points; The RFID card reader automatically scans the passive RFID card nearby, reads the ID information of the passive RFID card, and obtains the RFID positioning information; The inertial navigation sensor module calculates the movement trajectory and direction of the person to be located through linear acceleration, angular velocity and magnetic field changes, and generates inertial navigation sensor positioning information; The wireless communication module is used to receive the RFID positioning information and the inertial navigation sensor positioning information obtained by the RFID card reader and the inertial navigation sensor module, and send them to the command and dispatch background; The positioning management backend establishes a mapping relationship between the ID information of the passive RFID card and the deployment location point to form a positioning reference database; The command and dispatch background receives and processes the information of the RFID card reader and the inertial navigation sensor module, generates real-time location information of the person to be located, and provides multi-dimensional display and status monitoring.
[0012] According to the above scheme, the command and dispatch background includes multi-dimensional display and status monitoring; The multi-dimensional display includes map display, list display and historical trajectory playback; the map display marks the position and movement trajectory of the person to be located in real time on the plane map of the area to be located; the list display displays the UHF RFID reader ID, current position and movement status of the person to be located in a table form; the historical trajectory playback playback and analysis of the historical movement trajectory of the person to be located; The status monitoring includes power status, equipment status and abnormal alarm; the power status includes the power information of the ultra-high frequency RFID reader and the inertial navigation sensor; the equipment status includes the working status of the RFID reader and the inertial navigation sensor; the abnormal alarm is automatically triggered when the person to be located does not move for a long time or the equipment fails, and the command and dispatch background will display the alarm situation.
[0013] According to the above scheme, the abnormal alarm includes: When the power information is lower than the set threshold, the communication interruption exceeds the set time, or the device working status is abnormal, the device fault alarm is triggered; When there is no abnormality in the equipment, but the person to be located does not move within the set time threshold, the person abnormality alarm is triggered.
[0014] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention uses RFID equipment without power supply, which greatly ensures the reliability of positioning when a fire occurs, and does not cause abnormal dangers of battery explosion; 2. The RFID device used in the present invention is small in size and can be integrated with protective equipment; 3. The present invention combines RFID positioning with inertial navigation sensors, provides a fixed position reference through passive RFID cards, and the inertial navigation sensors make up for the positioning blind spots, seamlessly connect, cover the fire rescue area, and are not restricted by space. BRIEF DESCRIPTION OF THE DRAWINGS
[0015] Figure 1This is a flowchart of the steps of a method for positioning a fire rescue scene based on the Internet of Things of the present invention; Figure 2 The present invention is a structural schematic diagram of a fire rescue scene positioning system based on the Internet of Things. DETAILED DESCRIPTION
[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0017] Example: Figure 1-Figure 2 As shown, the present invention provides a technical solution, a method for positioning a fire rescue scene based on the Internet of Things, the method comprising the steps of: S1. Deploy passive RFID cards at key locations in the area to be located according to preset intervals; Specifically, passive RFID cards are deployed at key locations of the area to be located according to a preset spacing d; the preset spacing d is determined according to the environmental characteristics of the area to be located, the signal propagation characteristics and the effective scanning radius of the UHF RFID reader; the passive RFID card has unique ID information, and the ID information is bound to the deployment location point through the UHF RFID reader and uploaded to the positioning management backend; for example: the area to be located has a total area of about 2,000 square meters, and the preset spacing d is determined to be 10 meters. The key locations of the area to be located, including channel intersections, elevator entrances, stairwells, etc., are deployed with emphasis, and a total of 200 passive RFID cards are deployed, each of which has unique ID information, for example: ID-001, ID-002, ..., ID-200; Furthermore, the positioning management background establishes a mapping relationship based on the ID information of the passive RFID card and the deployment location point to form a positioning reference database for storing, managing and querying the passive RFID card information; for example: a plane rectangular coordinate system is established with the lower left corner of the plan view of the area to be positioned as the origin, the deployment position of the passive RFID card ID-001 is (0,10), and the unit length is meter. This is only an example and is not limited.
[0018] S2, the person to be located wears a device integrating an ultra-high frequency RFID card reader and an inertial navigation sensor and enters the area to be located; Specifically, after the person to be located enters the area to be located, the UHF RFID reader is powered on and started; for example: the person to be located A wears a protective belt integrated with an UHF RFID reader and an inertial navigation sensor, and after entering the area to be located, the UHF RFID reader is powered on and started.
[0019] S3, after the UHF RFID reader is powered on, it automatically scans the passive RFID cards within a certain range around it, reads the ID information of the passive RFID cards, and obtains the RFID positioning information; Specifically, the UHF RFID reader continuously and automatically scans passive RFID cards within a certain range around it, and when a card is detected, reads the ID information of the passive RFID card; for example: the UHF RFID reader continuously and automatically scans passive RFID cards within a range of 10 meters around it, detects the passive RFID card, and reads the ID information of the passive RFID card as ID-001; the UHF RFID reader uploads the ID information of the passive RFID card to the command and dispatch background through wireless communication; combining the ID information of the passive RFID card and the positioning reference database, the command and dispatch background locks the position of the person A to be located at the nearest passive RFID card position, and obtains the RFID positioning information; for example: the command and dispatch background knows that the deployment position of the passive RFID card ID-001 is (0,10) based on the mapping relationship between the ID information and the positioning reference database, and then determines that the initial position of the person A to be located is (0,10).
[0020] S4, the inertial navigation sensor measures the acceleration, angular velocity and magnetic field change of the person to be located, calculates the movement trajectory and direction of the person to be located, and obtains the inertial navigation sensor positioning information; Specifically, the inertial navigation sensor includes an accelerometer, a gyroscope and a magnetometer; the accelerometer measures the linear acceleration of the person to be located; the gyroscope measures the angular velocity of the person to be located; the magnetometer measures the orientation of the person to be located; the calculation of the movement trajectory and direction of the person to be located includes calculating the displacement of the person to be located through the acceleration data, calculating the turning angle of the person to be located through the angular velocity data, and determining the movement direction of the person to be located in combination with the magnetometer data; for example: the accelerometer in the inertial navigation sensor measures the linear acceleration a=(0.2,0.1)m / s of the person A to be located 2 , the angular velocity measured by the gyroscope is ω=0.05rad / s, and the orientation measured by the magnetometer is θ=30°. Through calculation, the displacement in the x-axis direction is 1 meter, the displacement in the y-axis direction is 0.5 meter, the steering angle is 0.25rad, and the orientation is θ=30°; Furthermore, the movement trajectory and direction of the person to be located are confirmed, and the inertial navigation sensor positioning information is uploaded to the command and dispatch background via wireless communication.
[0021] S5, the command and dispatch background receives the RFID positioning information and the inertial navigation sensor positioning information, and determines the real-time position of the person to be located through a data analysis algorithm; Specifically, the initial position of the person to be located is determined according to the RFID positioning information, and the movement trajectory and direction of the person to be located are measured according to the inertial navigation sensor positioning information; when the person to be located has not passed through the passive RFID card, the inertial navigation sensor positioning information is optimized in real time by the Kalman filter algorithm; when the person to be located passes through the next passive RFID card, the accumulated error of the inertial navigation sensor is corrected by using the RFID positioning information, and the corrected data is used as the initial state of the Kalman filter to continue to optimize the positioning data; Furthermore, for example: when the person A to be located passes by the next passive RFID card ID-002, the deployment position (10, 10) of the passive RFID card ID-002 is used as the precise position at the current moment, and the displacement of the person to be located calculated by the inertial navigation sensor is reset; the speed of the person to be located is recalculated according to the difference between the RFID positioning information (10, 10) and the position at the previous moment; the movement direction of the person to be located is corrected using the inertial navigation sensor positioning information and the RFID positioning information; after the correction, the inertial navigation sensor continues to measure the acceleration, angular velocity and magnetic field change of the person to be located, and calculates the movement trajectory and direction; when the person to be located passes by the next passive RFID card, error correction is performed again.
[0022] Furthermore, the RFID positioning information is integrated with the inertial navigation positioning information through a Kalman filter algorithm; the Kalman filter algorithm optimizes the positioning data of the inertial navigation sensor through prediction and updating, and uses the RFID positioning information for correction; the prediction includes predicting the position and speed at the current moment based on the position and speed at the previous moment in the inertial navigation positioning information, combined with a motion model, and calculating the covariance matrix of the prediction result; the motion model is used to describe the motion law of the person to be located; the covariance matrix of the prediction result is used to represent the uncertainty of the prediction result; The formula for predicting the current position and speed is as follows: ; in, Expressed as the current position and velocity; F k Represented as a state transition matrix, describing how the state changes over time; x k-1 Indicates the state at the last moment; B k Expressed as the control input matrix; uk Represented as a control input vector; The covariance matrix of the prediction results is as follows: ; in, Represented as the prediction covariance matrix at the current moment; P k-1 It is represented as the frontal covariance matrix of the previous moment; Expressed as the transpose of the state transfer matrix; Q k Expressed as the process noise covariance matrix; The updating includes taking the RFID positioning information as an observation value, calculating a Kalman gain, wherein the Kalman gain is expressed as a weight of a prediction result and the observation value; based on the Kalman gain, using the RFID positioning information, correcting the predicted current moment position and speed, obtaining an optimized current moment position and speed, and updating a covariance matrix; The Kalman gain is formulated as follows: ; Among them, K k Expressed as Kalman gain; H k Represented as an observation matrix; Represented as the transpose of the observation matrix; R k denoted as the observation noise covariance matrix and denoted as the uncertainty of the observations; The optimized current position and speed are as follows: ; Among them, x k Represents the optimized current position and speed; z k It is represented as the observation value at the current moment, i.e., RFID positioning information; The updated covariance matrix is formulated as follows: ; Among them, P k It is represented as the updated covariance matrix; I is represented as the identity matrix; The optimized current moment position and speed are used as input at the next moment, and the prediction and update steps are repeated to achieve continuous positioning optimization.
[0023] The present invention provides another embodiment, a fire rescue scene positioning system based on the Internet of Things, the system comprising: a passive RFID card, an RFID card reader, an inertial navigation sensor module, a wireless communication module, a positioning management background and a command and dispatch background; The passive RFID cards are used to be distributed at preset intervals and deployed at key locations in the area to be positioned as positioning reference points; The RFID card reader automatically scans the passive RFID card nearby, reads the ID information of the passive RFID card, and obtains the RFID positioning information; The inertial navigation sensor module calculates the movement trajectory and direction of the person to be located through linear acceleration, angular velocity and magnetic field changes, and generates inertial navigation sensor positioning information; The wireless communication module is used to receive the RFID positioning information and the inertial navigation sensor positioning information obtained by the RFID card reader and the inertial navigation sensor module, and send them to the command and dispatch background; The positioning management background establishes a mapping relationship between the ID information of the passive RFID card and the deployment location point to form a positioning reference database; The command and dispatch background receives and processes the information of the RFID reader and the inertial navigation sensor module, generates the real-time location information of the person to be located, and provides multi-dimensional display and status monitoring; the multi-dimensional display includes map display, list display and historical trajectory playback; the map display marks the position and movement trajectory of the person to be located on the plan of the area to be located in real time; the list display displays the ultra-high frequency RFID reader ID, current position and movement status of the person to be located in a tabular form; the historical trajectory playback playback and analysis of the historical movement trajectory of the person to be located; the status monitoring includes power status, equipment status and abnormal alarm; the power status includes the power information of the ultra-high frequency RFID reader and the inertial navigation sensor; the equipment status includes the working status of the RFID reader and the inertial navigation sensor; the abnormal alarm automatically triggers an alarm when the person to be located has not moved for a long time or the equipment fails, and the command and dispatch background will display the alarm situation.
[0024] The present invention provides another embodiment, a fire rescue scene positioning system based on the Internet of Things, which completes the real-time positioning of personnel: According to the layout and environmental characteristics of the area to be located, the preset spacing is determined to be 10 meters, and a total of 300 passive RFID cards are deployed. Each passive RFID card has unique ID information, ranging from ID-001 to ID-300; Through the RFID card reader, the ID information of each passive RFID card is bound to the deployment location point, a mapping relationship is established, and a positioning reference database is formed; When the person A to be located walks to 8 meters away from the ID-015 card, the UHF RFID reader detects the passive RFID card, reads the ID information of the passive RFID card as ID-015, and sends the ID information of the passive RFID card to the command and dispatch background through the wireless communication module; In a certain period of time, the linear acceleration measured by the accelerometer in the inertial navigation sensor is (0.3, 0.2) m / s² (x and y axis components), the angular velocity measured by the gyroscope is 0.06 rad / s, and the orientation measured by the magnetometer is 45°. By calculating these data, the motion trajectory and direction information of the person A to be located are obtained and uploaded to the command and dispatch background through the wireless communication module; The command and dispatch background receives and processes the information of the RFID reader and the inertial navigation sensor module, generates the real-time location information of the person to be located, and provides multi-dimensional display and status monitoring. Specifically, for example, on the plan of the area to be located, the position of the person A to be located is highlighted, and the equipment number of the person A to be located is marked; the table shows: the ultra-high frequency RFID reader ID of the person A to be located, the current position (30, 40), the movement status is: moving; the ultra-high frequency RFID reader power is 60%, and the inertial navigation sensor power is 50%; the working status of the RFID reader and the inertial navigation sensor are both in normal operation; there is no abnormal warning at present; this is only an example for illustration and is not limited.
[0025] The present invention provides another embodiment, a fire rescue scene positioning system based on the Internet of Things for real-time monitoring and early warning; The command and dispatch background receives and processes the information of the RFID card reader and the inertial navigation sensor module, and generates the real-time location information of the person to be located; Commanders can view the location, movement status, equipment status and power information of all personnel to be located at the same time through the command and dispatch background; it also provides multi-dimensional display and status monitoring; Specifically, the multi-dimensional display includes map display, list display and historical trajectory playback; the map display marks the position and movement trajectory of the person to be located in real time on the plan view of the area to be located; the list display displays the UHF RFID reader ID, current position and movement status of the person to be located in a table form; the historical trajectory playback is the playback and analysis of the historical movement trajectory of the person to be located; for example: on the plan view of the area to be located, the position of person B to be located is highlighted and the UHF RFID reader ID of person B to be located is marked; the table shows: the UHF RFID reader ID of person B to be located, the current position (50,60), and the movement status: still; when the historical trajectory playback of person B to be located is selected, it jumps to the entire movement trajectory of person B to be located in the area to be located from the time the UHF RFID reader is powered on or enters the area to be located to the time the UHF RFID reader ID is powered off or comes out of the area to be located; The status monitoring includes power status, equipment status and abnormal alarm; the power status includes the power information of the UHF RFID reader and the inertial navigation sensor; the equipment status includes the working status of the RFID reader and the inertial navigation sensor; the abnormal alarm automatically triggers the alarm when the person to be located has not moved for a long time or the equipment fails, and the command and dispatch background will display the alarm situation; for example: the power information of the UHF RFID reader and the inertial navigation sensor of the current person to be located B is 60%, the set threshold is 20%, and the power is normal; the command and dispatch background continues to receive data from the wireless communication module, the set time is 1 minute, and the communication is normal; the command and dispatch background continues to receive the positioning information of the UHF RFID reader and the inertial navigation sensor of the person to be located B, and the working status of the equipment is normal; the person to be located B has been in a stationary state for 16 minutes, which exceeds the set time threshold of 15 minutes, and the equipment has no abnormality, and the personnel abnormal alarm is immediately triggered to remind the command personnel that the movement state of the person to be located B is abnormal at this time, and the position of the person to be located B at this time is highlighted on the plan of the area to be located.
[0026] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.
Claims
1. A fire rescue scene positioning method based on the Internet of Things, characterized by: The method includes: Deploy passive RFID cards at key locations in the area to be located at preset intervals; The person to be located wears a device integrating an ultra-high frequency RFID card reader and an inertial navigation sensor and enters the area to be located; After the UHF RFID card reader is powered on, it automatically scans the passive RFID cards within a certain range around it, reads the ID information of the passive RFID cards, and obtains the RFID positioning information; The inertial navigation sensor measures the acceleration, angular velocity and magnetic field change of the person to be located, calculates the movement trajectory and direction of the person to be located, and obtains the inertial navigation sensor positioning information; The command and dispatch background receives the RFID positioning information and the inertial navigation sensor positioning information, and determines the real-time position of the person to be located through a data analysis algorithm.
2. According to the fire rescue scene positioning system and method based on the Internet of Things according to claim 1, it is characterized by: The passive RFID card has unique ID information, which is bound to the deployment location through the UHF RFID reader and uploaded to the positioning management background; The positioning management backend establishes a mapping relationship based on the ID information of the passive RFID card and the deployment location point to form a positioning reference database for storing, managing and querying the passive RFID card information.
3. The method for positioning a fire rescue scene based on the Internet of Things according to claim 2, characterized in that: After the person to be located enters the area to be located, the UHF RFID reader is powered on and started; The UHF RFID reader continuously and automatically scans passive RFID cards within a certain range around it, and when a passive RFID card is detected, reads the ID information of the passive RFID card; The UHF RFID card reader uploads the ID information of the passive RFID card to the command and dispatch background through wireless communication; In combination with the ID information of the passive RFID card and the positioning reference database, the command and dispatch background locks the position of the person to be located at the nearest passive RFID card position and obtains the RFID positioning information.
4. The method for positioning a fire rescue scene based on the Internet of Things according to claim 1, characterized in that: The inertial navigation sensor includes an accelerometer, a gyroscope and a magnetometer; the accelerometer measures the linear acceleration of the person to be located; the gyroscope measures the angular velocity of the person to be located; The magnetometer measures the orientation of the person to be located; The calculating of the movement trajectory and direction of the person to be located includes calculating the displacement of the person to be located through acceleration data, calculating the turning angle of the person to be located through angular velocity data, and determining the movement direction of the person to be located in combination with magnetometer data; The inertial navigation sensor positioning information is uploaded to the command and dispatch background via wireless communication.
5. The method for positioning a fire rescue scene based on the Internet of Things according to claim 1, characterized in that: Determine the initial position of the person to be located according to the RFID positioning information, and measure the movement trajectory and direction of the person to be located according to the inertial navigation sensor positioning information; When the person to be located has not passed the passive RFID card, the inertial navigation sensor positioning information is optimized in real time by using the Kalman filter algorithm; When the person to be located passes the next passive RFID card, the RFID positioning information is used to correct the accumulated error of the inertial navigation sensor, and the corrected data is used as the initial state of the Kalman filter to continue to optimize the positioning data.
6. A method for positioning a fire rescue scene based on the Internet of Things according to claim 5, characterized in that: Correcting the accumulated error of the inertial navigation sensor using the RFID positioning information includes: Using the RFID positioning information as the precise position at the current moment, resetting the displacement of the person to be located calculated by the inertial navigation sensor; recalculating the speed of the person to be located based on the difference between the RFID positioning information and the position at the previous moment; and correcting the movement direction of the person to be located using the inertial navigation sensor positioning information and the RFID positioning information; After calibration, the inertial navigation sensor continues to measure the acceleration, angular velocity and magnetic field change of the person to be located, and calculates the motion trajectory and direction; When the person to be located passes the next passive RFID card, error correction is performed again.
7. The method for positioning a fire rescue scene based on the Internet of Things according to claim 5, characterized in that: Determining the real-time position of the person to be located by using a data analysis algorithm includes: Through the Kalman filter algorithm, the RFID positioning information is integrated with the inertial navigation positioning information; The Kalman filter algorithm optimizes the positioning data of the inertial navigation sensor through prediction and updating, and uses the RFID positioning information for correction; The prediction includes predicting the position and speed at the current moment based on the position and speed at the previous moment in the inertial navigation positioning information in combination with the motion model, and calculating the covariance matrix of the prediction result; the motion model is used to describe the motion law of the person to be located; the covariance matrix of the prediction result is used to represent the uncertainty of the prediction result; The updating includes taking the RFID positioning information as an observation value, calculating a Kalman gain, wherein the Kalman gain is expressed as a weight of a prediction result and the observation value; based on the Kalman gain, using the RFID positioning information, correcting the predicted current moment position and speed, obtaining an optimized current moment position and speed, and updating a covariance matrix; The optimized current moment position and speed are used as input at the next moment, and the prediction and update steps are repeated to achieve continuous positioning optimization.
8. A fire rescue scene positioning system based on the Internet of Things, applied to a fire rescue scene positioning method based on the Internet of Things as claimed in any one of claims 1 to 7, characterized in that: The system includes: passive RFID card, RFID reader, inertial navigation sensor module, wireless communication module, positioning management background and command and dispatch background; The passive RFID cards are used to be distributed at preset intervals and deployed at key locations in the area to be positioned as positioning reference points; The RFID card reader automatically scans the passive RFID card nearby, reads the ID information of the passive RFID card, and obtains the RFID positioning information; The inertial navigation sensor module calculates the movement trajectory and direction of the person to be located through linear acceleration, angular velocity and magnetic field changes, and generates inertial navigation sensor positioning information; The wireless communication module is used to receive the RFID positioning information and the inertial navigation sensor positioning information obtained by the RFID card reader and the inertial navigation sensor module, and send them to the command and dispatch background; The positioning management backend establishes a mapping relationship between the ID information of the passive RFID card and the deployment location point to form a positioning reference database; The command and dispatch background receives and processes the information of the RFID card reader and the inertial navigation sensor module, generates real-time location information of the person to be located, and provides multi-dimensional display and status monitoring.
9. The fire rescue scene positioning system based on the Internet of Things according to claim 8 is characterized in that: The command and dispatch background includes multi-dimensional display and status monitoring; The multi-dimensional display includes map display, list display and historical trajectory playback; the map display marks the position and movement trajectory of the person to be located in real time on the plane map of the area to be located; the list display displays the UHF RFID reader ID, current position and movement status of the person to be located in a table form; the historical trajectory playback playback and analysis of the historical movement trajectory of the person to be located; The status monitoring includes power status, equipment status and abnormal alarm; the power status includes the power information of the ultra-high frequency RFID reader and the inertial navigation sensor; the equipment status includes the working status of the RFID reader and the inertial navigation sensor; the abnormal alarm is automatically triggered when the person to be located does not move for a long time or the equipment fails, and the command and dispatch background will display the alarm situation.
10. A fire rescue scene positioning system and method based on the Internet of Things according to claim 9, characterized in that: The abnormal alarm includes: When the power information is lower than the set threshold, the communication interruption exceeds the set time, or the device working status is abnormal, the device fault alarm is triggered; When there is no abnormality in the equipment, but the person to be located does not move within the set time threshold, the person abnormality alarm is triggered.