Working personnel monitoring system based on UWB positioning

Abstract

The invention discloses a worker monitoring system based on UWB positioning, relates to the technical field of worker safety monitoring, and solves the technical problems of low positioning precision and low safety monitoring precision in the prior art. The method comprises the following steps: acquiring environment data of a target area and monitoring data of workers; wherein the monitoring data comprises positioning data and physical sign data; calculating the distance between the worker and the UWB base station based on the positioning data, and marking the distance as distance data; constructing a correction model based on the environment data to correct the distance data to obtain target distance data; based on the target distance data, calculating to obtain position coordinates of the worker, and marking in a preset three-dimensional model in real time to obtain position point information of the worker in the preset three-dimensional model; calculating a health sign evaluation coefficient of the worker based on the sign data; and judging whether to carry out early warning or not based on the position point information and the health sign evaluation coefficient. The technical problem is solved.

Description

Technical Field

[0001] The invention belongs to the field of staff safety monitoring, and in particular is a staff monitoring system based on UWB positioning. Background Art

[0002] With the development of modern industry, ensuring the safety of workers has become particularly important, especially in complex environments such as large factories, mines, and construction sites. Traditional personnel monitoring methods, such as RFID and Wi-Fi positioning, have problems such as low positioning accuracy and susceptibility to interference, and cannot meet the requirements of high precision and real-time performance. UWB technology, with its high precision, low power consumption, and strong anti-multipath capability, has become an ideal choice for the new generation of personnel positioning technology.

[0003] The existing UWB (ultra-wideband) positioning system mainly calculates the distance between the staff and each base station by measuring the time difference or flight time between the wireless signal emitted by the UWB tag from the transmitter to the receiving end of the UWB base station at multiple known locations, and then determines the precise position of the staff in space to achieve the purpose of personnel monitoring. However, the existing technology does not take into account that the propagation speed and path of the UWB signal are affected by factors such as ambient temperature, humidity, and air density. For example, in a high temperature or high humidity environment, the water vapor content in the air increases, the dielectric constant increases, and the signal propagation speed slows down, thereby affecting the positioning accuracy; at the same time, relying only on positioning data to monitor the safety of staff is obviously not accurate and comprehensive enough.

[0004] Therefore, the present invention proposes a staff monitoring system based on UWB positioning to solve the above problems. Summary of the invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes a staff monitoring system based on UWB positioning, which is used to solve the problem that the prior art does not take into account that the propagation speed and path of UWB signals are affected by factors such as ambient temperature, humidity, and air density. For example, in a high temperature or high humidity environment, the water vapor content in the air increases, the dielectric constant increases, and the signal propagation speed slows down, thereby affecting the positioning accuracy; at the same time, relying only on positioning data to monitor the safety of staff, it is obvious that there are technical problems such as low monitoring accuracy and insufficient comprehensiveness.

[0006] To achieve the above-mentioned object, the first aspect of the present invention provides a staff monitoring system based on UWB positioning, comprising: a data acquisition module, a data analysis module, a positioning warning module and a vital sign warning module;

[0007] Data acquisition module: used to obtain the environmental data of the target area and the monitoring data of the staff in real time; the monitoring data includes: positioning data and vital sign data;

[0008] Positioning analysis module: calculate the distance between the staff and the UWB base station based on the positioning data and mark it as distance data; construct a correction model based on the environmental data to correct the distance data and obtain the target distance data; and,

[0009] The position coordinates of the staff are calculated based on the target distance data, and are marked in real time in the preset three-dimensional model to obtain the position point information of the staff in the preset three-dimensional model;

[0010] Physical sign analysis module: calculates the health sign assessment coefficient of the staff based on the physical sign data;

[0011] Early warning module: Determine whether to issue an early warning based on location information and health sign assessment coefficients.

[0012] Preferably, the real-time acquisition of environmental data of the target area and monitoring data of staff includes:

[0013] The data acquisition equipment is used to collect the environmental data of the target area and the vital signs data of the staff in real time; the target area refers to the working area that needs to be monitored; the environmental data include: ambient temperature and ambient humidity; the positioning data refers to the time difference between the wireless signal emitted by the UWB tag installed on the staff from the transmitter to the time it is received by several UWB base station receivers; the vital signs data include: heart rate and body temperature.

[0014] Preferably, the calculating the distance between the staff and the UWB base station based on the positioning data includes:

[0015] Extract the vital sign data of the staff and mark it as Ti; where Ti represents the time difference from when the wireless signal emitted by the UWB tag is sent from the transmitter to when it is received by the i-th UWB base station receiver, i = {1, 2, 3, ..., n}, n refers to the number of UWB base stations, and n ≥ 3;

[0016] The distance between the staff and the UWB base station is calculated by the formula di=[c / γ^(1 / 2)]×Ti; where di represents the distance between the staff and the i-th UWB base station; c is the speed of light, and γ is the relative dielectric constant of air under standard atmospheric conditions.

[0017] Preferably, the correction model is constructed based on the environmental data to correct the distance data, including:

[0018] The environmental data is used as the independent variable, and the environmental temperature is marked as W, and the environmental humidity is marked as H; the corrected distance between the staff and the UWB base station is used as the dependent variable, and marked as the target distance data Mdi;

[0019] The independent variables and dependent variables are fitted by polynomial fitting to establish a correction model; the correction model is specifically: Mdi=di×[α×e^[(W-ZW) / ZW]+β×e^[-H / (H+1)]]; wherein α and β are proportional coefficients, and when W-ZW>0, 0<α<1, when W-ZW<0, -1<α<0, when W-ZW=0, α=0; -1<β<0; ZW is the standard temperature.

[0020] It should be noted that, relative to standard atmospheric conditions, as the temperature rises, the air density decreases, and the dielectric constant decreases, so the actual distance between the staff and the UWB base station will be larger; as the temperature decreases, the air density increases, and the dielectric constant increases, so the actual distance between the staff and the UWB base station will be smaller;

[0021] Compared with standard atmospheric conditions, as humidity increases, the water vapor content in the air increases and the dielectric constant increases, so the actual distance between the staff and the UWB base station will be smaller; as humidity decreases, the water vapor content in the air decreases and the dielectric constant decreases, so the actual distance between the staff and the UWB base station will be larger.

[0022] The present invention takes ambient temperature and humidity as independent variables and uses polynomial fitting to establish a correction model that can reflect the influence of temperature and humidity changes on signal propagation speed. By constructing a correction model based on environmental data, the UWB signal propagation distance is dynamically adjusted, thereby solving the problem that the propagation speed and path of the UWB signal are affected by factors such as ambient temperature, humidity, and air density, resulting in low positioning accuracy.

[0023] Preferably, the step of calculating the position coordinates of the staff member based on the target distance data and marking them in real time in a preset three-dimensional model includes:

[0024] Constructing a three-dimensional model of the target area to obtain a preset three-dimensional model;

[0025] Establish a spatial rectangular coordinate system based on the preset three-dimensional model;

[0026] The position coordinates of the staff are calculated based on the spatial rectangular coordinate system and the distance between the staff and the UWB base station.

[0027] It should be noted that the position coordinates of the staff member are the position point information of the staff member in the preset three-dimensional model.

[0028] Preferably, constructing a three-dimensional model of the target area includes:

[0029] The three-dimensional data of the target area is collected by a three-dimensional acquisition device; the three-dimensional acquisition device includes: a laser scanner or a 3D scanner; the three-dimensional data refers to the building structure of the target area, such as the UWB base station, walls, doors and windows, corridors, stairs, elevator shafts, room layout, operating equipment and other information set up in the target area;

[0030] A three-dimensional model of the target area is constructed based on the three-dimensional data through three-dimensional construction software; wherein the three-dimensional construction software includes: AutoCAD, SketchUp or Blender.

[0031] Preferably, the establishing of a spatial rectangular coordinate system based on a preset three-dimensional model includes:

[0032] Extracting a preset three-dimensional model of the target area;

[0033] A spatial rectangular coordinate system is established by taking the center point of the preset three-dimensional model as the origin.

[0034] Preferably, the calculating of the position coordinates of the staff member based on the spatial rectangular coordinate system and the distance between the staff member and the UWB base station includes:

[0035] Extracting the established spatial rectangular coordinate system, and obtaining the coordinates of several UWB base stations in the target area in the spatial rectangular coordinate system;

[0036] Based on the coordinates of several UWB base stations in the spatial rectangular coordinate system and the distance between the staff and the UWB base station, the coordinates of the staff's position in the spatial rectangular coordinate system are calculated.

[0037] The present invention significantly improves the visualization and management efficiency of the personnel positioning system by constructing a high-precision three-dimensional model of the target area and marking the position coordinates of the staff in real time; using three-dimensional acquisition equipment to obtain detailed building structure data; using professional three-dimensional construction software to convert these data into an intuitive three-dimensional model, and based on this, a unified spatial rectangular coordinate system is established, combining the known coordinates of the UWB base station and the distance data between the staff and each base station, and using geometric algorithms to accurately calculate the position of the staff in three-dimensional space. This real-time and accurate positioning method not only enables managers to intuitively monitor the specific location of each employee and their surrounding environment, but also greatly improves work efficiency, especially in complex environments such as large factories, mines, and construction sites, helping to quickly respond to emergencies, optimize path planning, and avoid potential safety risks.

[0038] Preferably, the health sign assessment coefficient of the worker is calculated based on the physical sign data, including:

[0039] Label the heart rate as L and the body temperature as G;

[0040] The health sign assessment coefficient is calculated through the formula: P = A×e^[ln(L) / (ln(L)+1)]+B×e^[ln(G) / (ln(G)+1)]; wherein P is the health sign assessment coefficient, A and B are proportional coefficients, and 0<A<1, 0<B<1.

[0041] It should be noted that the proportional coefficients A and B are set by experts in this field based on experience according to historical vital sign data; wherein the historical vital sign data refers to the historical data of the collected vital sign data.

[0042] The present invention calculates the health sign assessment coefficient based on the physical sign data, which can evaluate the physical condition of the staff more comprehensively and accurately. It not only significantly improves the safety and reliability of the personnel monitoring system, but also can discover the potential health problems of employees at an early stage, such as abnormal heart rate or high body temperature, and issue early warnings and take corresponding measures to avoid decreased work efficiency or safety accidents caused by health problems. In addition, the health sign assessment coefficient is used in combination with location information to further enhance the system's intelligent early warning capability, and can make more reasonable decisions by comprehensively considering the physical condition of employees when they enter dangerous areas, thereby ensuring the safety and work efficiency of employees.

[0043] Preferably, the determining whether to issue an early warning based on the location point information and the health sign assessment coefficient includes:

[0044] Extracting a preset three-dimensional model of the target area, and marking and dividing the preset dangerous area in the preset three-dimensional model; wherein the preset dangerous area refers to a prohibited entry area or a high-risk area in the target area;

[0045] Determine whether the position coordinates of the staff are within the preset danger zone marked in the 3D model; if yes, generate warning information and send it to the client; if not, continue monitoring and judgment;

[0046] Determine whether the health sign assessment coefficient is greater than the preset health sign assessment threshold; if yes, generate an early warning message and send it to the client; if not, continue monitoring and judging.

[0047] It should be noted that the preset health sign assessment threshold is set by experts in this field based on their experience according to the normal physical sign data of the staff.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] 1. The prior art does not take into account that the propagation speed and path of UWB signals are affected by factors such as ambient temperature, humidity, and air density. For example, in a high temperature or high humidity environment, the water vapor content in the air increases, the dielectric constant increases, and the signal propagation speed slows down, thereby affecting the positioning accuracy; at the same time, relying only on positioning data to monitor the safety of staff is obviously not accurate and comprehensive enough; the present invention calculates the position of the staff by measuring the time difference between the wireless signal emitted by the UWB tag and multiple base stations, and also collects the environmental data of the target area and the vital sign data of the staff in real time; based on the environmental data, a correction model is constructed to correct the distance data to ensure that high-precision positioning can be maintained under different environmental conditions; based on the vital sign data, the health sign assessment coefficient is calculated, and the location information and health status are combined for dual warning, which can promptly issue an alarm when the staff enters a dangerous area or the health sign is abnormal, thereby providing a more comprehensive and intelligent safety monitoring, solving the problem of low positioning accuracy and low safety monitoring accuracy in the prior art.

[0050] 2. The present invention significantly improves the visualization and management efficiency of the personnel positioning system by constructing a three-dimensional model of the target area and marking the position coordinates of the staff in real time; uses a three-dimensional acquisition device to obtain detailed building structure data of the target area, including information such as UWB base stations, walls, doors and windows, corridors, stairs, elevator shafts, room layouts, and operating equipment, to ensure the integrity and accuracy of the model; uses professional three-dimensional construction software to convert these data into a high-precision three-dimensional model to provide an intuitive visual reference for subsequent spatial positioning; establishes a spatial rectangular coordinate system based on a preset three-dimensional model, and uses the center point of the model as the origin to ensure the unity and consistency of the coordinate system; by extracting the coordinates of the known UWB base station in the spatial rectangular coordinate system, and combining the target distance data between the staff and each base station, the position coordinates of the staff in the three-dimensional space are accurately calculated using a geometric algorithm, which not only realizes the real-time and accurate positioning of the staff position, but also directly maps its position information to the three-dimensional model, so that managers can intuitively see the specific location of each employee and the surrounding environment, greatly improving work efficiency, especially in complex environments such as large factories, mines, and construction sites, managers can quickly respond to emergencies, optimize path planning, and avoid potential safety risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0052] Figure 1 A schematic diagram of a system module according to an embodiment of the present invention;

[0053] Figure 2 A schematic diagram of a specific process of an embodiment of the present invention;

[0054] Figure 3 Schematic diagram of determining health sign assessment coefficients according to an embodiment of the present invention. DETAILED DESCRIPTION

[0055] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than 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.

[0056] See also Figure 1-Figure 2 , the first aspect of the present invention provides a staff monitoring system based on UWB positioning, including: a data acquisition module, a data analysis module, a positioning warning module and a vital sign warning module;

[0057] Data acquisition module: used to obtain the environmental data of the target area and the monitoring data of the staff in real time; the monitoring data includes: positioning data and vital sign data;

[0058] Positioning analysis module: calculate the distance between the staff and the UWB base station based on the positioning data and mark it as distance data; construct a correction model based on the environmental data to correct the distance data and obtain the target distance data; and,

[0059] The position coordinates of the staff are calculated based on the target distance data, and are marked in real time in the preset three-dimensional model to obtain the position point information of the staff in the preset three-dimensional model;

[0060] Physical sign analysis module: calculates the health sign assessment coefficient of the staff based on the physical sign data;

[0061] Early warning module: Determine whether to issue an early warning based on location information and health sign assessment coefficients.

[0062] Obtain real-time environmental data of the target area and monitoring data of staff, including:

[0063] The data acquisition equipment is used to collect the environmental data of the target area and the vital signs data of the staff in real time; the target area refers to the working area that needs to be monitored; the environmental data include: ambient temperature and ambient humidity; the positioning data refers to the time difference between the wireless signal emitted by the UWB tag installed on the staff from the transmitter to the time it is received by several UWB base station receivers; the vital signs data include: heart rate and body temperature.

[0064] The distance between the staff and the UWB base station is calculated based on the positioning data, including:

[0065] Extract the vital sign data of the staff and mark it as Ti; where Ti represents the time difference from when the wireless signal emitted by the UWB tag is sent from the transmitter to when it is received by the i-th UWB base station receiver, i = {1, 2, 3, ..., n}, n refers to the number of UWB base stations, and n ≥ 3;

[0066] The distance between the staff and the UWB base station is calculated by the formula di=[c / γ^(1 / 2)]×Ti; where di represents the distance between the staff and the i-th UWB base station; c is the speed of light, and γ is the relative dielectric constant of air under standard atmospheric conditions.

[0067] A correction model is constructed based on environmental data to correct the distance data, including:

[0068] The environmental data is used as the independent variable, and the environmental temperature is marked as W, and the environmental humidity is marked as H; the corrected distance between the staff and the UWB base station is used as the dependent variable, and marked as the target distance data Mdi;

[0069] The independent variables and dependent variables are fitted by polynomial fitting to establish a correction model; the correction model is specifically: Mdi=di×[α×e^[(W-ZW) / ZW]+β×e^[-H / (H+1)]]; wherein α and β are proportional coefficients, and when W-ZW>0, 0<α<1, when W-ZW<0, -1<α<0, when W-ZW=0, α=0; -1<β<0; ZW is the standard temperature.

[0070] The position coordinates of the staff are calculated based on the target distance data and marked in real time in the preset 3D model, including:

[0071] Constructing a three-dimensional model of the target area to obtain a preset three-dimensional model;

[0072] Establish a spatial rectangular coordinate system based on the preset three-dimensional model;

[0073] The position coordinates of the staff are calculated based on the spatial rectangular coordinate system and the distance between the staff and the UWB base station.

[0074] Construct a 3D model of the target area, including:

[0075] The three-dimensional data of the target area is collected by a three-dimensional acquisition device; the three-dimensional acquisition device includes: a laser scanner or a 3D scanner; the three-dimensional data refers to the building structure of the target area, such as the UWB base station, walls, doors and windows, corridors, stairs, elevator shafts, room layout, operating equipment and other information set up in the target area;

[0076] A three-dimensional model of the target area is constructed based on the three-dimensional data through three-dimensional construction software; wherein the three-dimensional construction software includes: AutoCAD, SketchUp or Blender.

[0077] Establish a spatial rectangular coordinate system based on the preset 3D model, including:

[0078] Extracting a preset three-dimensional model of the target area;

[0079] A spatial rectangular coordinate system is established by taking the center point of the preset three-dimensional model as the origin.

[0080] The position coordinates of the staff are calculated based on the spatial rectangular coordinate system and the distance between the staff and the UWB base station, including:

[0081] Extracting the established spatial rectangular coordinate system, and obtaining the coordinates of several UWB base stations in the target area in the spatial rectangular coordinate system;

[0082] Based on the coordinates of several UWB base stations in the spatial rectangular coordinate system and the distance between the staff and the UWB base station, the coordinates of the staff's position in the spatial rectangular coordinate system are calculated.

[0083] The health sign assessment coefficient of the staff is calculated based on the physical sign data, including:

[0084] Label the heart rate as L and the body temperature as G;

[0085] The health sign assessment coefficient is calculated through the formula: P = A×e^[ln(L) / (ln(L)+1)]+B×e^[ln(G) / (ln(G)+1)]; wherein P is the health sign assessment coefficient, A and B are proportional coefficients, and 0<A<1, 0<B<1.

[0086] See also Figure 3 , based on the location information and health sign assessment coefficient, determine whether to issue an early warning, including:

[0087] Extracting a preset three-dimensional model of the target area, and marking and dividing the preset dangerous area in the preset three-dimensional model; wherein the preset dangerous area refers to a prohibited entry area or a high-risk area in the target area;

[0088] Determine whether the position coordinates of the staff are within the preset danger zone marked in the 3D model; if yes, generate warning information and send it to the client; if not, continue monitoring and judgment;

[0089] Determine whether the health sign assessment coefficient is greater than the preset health sign assessment threshold; if yes, generate an early warning message and send it to the client; if not, continue monitoring and judging.

[0090] For example, suppose you are monitoring the workers of a large factory, which is located in an environment with large temperature and humidity changes. You need to conduct safety monitoring and early warning for the workers in the factory. The specific process is as follows:

[0091] 1. Collection of environmental data and vital signs data;

[0092] Target Area:

[0093] The factory workshop includes four UWB base stations (n=4), which are marked as B1, B2, B3, and B4 respectively.

[0094] Ambient temperature (W): 35°C;

[0095] Ambient humidity (H): 60%;

[0096] Staff Information:

[0097] A worker wears a UWB tag that sends a wireless signal to four base stations every 1 second.

[0098] Staff heart rate (L): 140 beats / min;

[0099] Body temperature of staff (G): 36.8℃;

[0100] 2. Calculate distance based on positioning data;

[0101] Step 1: Extract time difference data;

[0102] Assume that the time difference between the wireless signal transmitted by the UWB tag from the transmitter to the four base stations is:

[0103] Time difference of B1 receiving: T1 = 1.2 × 10^(-7) seconds

[0104] Time difference of B1 receiving: T2 = 1.5 × 10^(-7) seconds;

[0105] Time difference of B1 receiving: T3 = 1.8 × 10^(-7) seconds;

[0106] Time difference of B1 receiving: T4 = 2.1 × 10^(-7) seconds;

[0107] Step 2: Calculate the initial distance data;

[0108] The distance between the staff and the UWB base station is calculated according to the formula di = [c / γ^(1 / 2)] × Ti, where c is the speed of light (299792458m / s) and γ is the relative dielectric constant of air under standard atmospheric conditions (1.000536);

[0109] Substituting the data into the equation, we get: d1≈35.89 meters;

[0110] d2≈44.86 m;

[0111] d3≈53.83 m;

[0112] d4≈62.80 m;

[0113] 3. Build a correction model based on environmental data;

[0114] Step 1: Define environment parameters:

[0115] Standard temperature ZW = 15°C;

[0116] Standard humidity ZH = 0%;

[0117] Step 2: Calculate the correction factor;

[0118] According to the correction model Mdi=di×[α×e^[(W-ZW) / ZW]+β×e^[-H / (H+1)]], the distance di between the staff and the UWB base station is corrected;

[0119] Assume proportionality coefficients: α = 0.5, β = -0.3;

[0120] Substituting the data into this we get:

[0121] Md1 = 35.89 × [0.5 × e^[(35-15) / 15] - 0.3 × e^[-60 / (60+1)]] ≈ 64.04 meters;

[0122] Md2 = 44.86 × [0.5 × e^[(35-15) / 15] - 0.3 × e^[-60 / (60+1)]] ≈ 79.55 meters;

[0123] Md3=53.83×[0.5×e^[(35-15) / 15]-0.3×e^[-60 / (60+1)]]≈95.06 meters;

[0124] Md4 = 62.80 × [0.5 × e^[(35-15) / 15] - 0.3 × e^[-60 / (60+1)]] ≈ 111.57 meters;

[0125] 4. Calculate the position coordinates based on the target distance data;

[0126] Step 1: Establish a spatial rectangular coordinate system;

[0127] Assume that a 3D model of the factory has been constructed, and a spatial rectangular coordinate system has been established with its center point as the origin. The coordinates of the four base stations are known to be:

[0128] B1: (0,0,0);

[0129] B2: (80,0,0);

[0130] B3: (80,80,10);

[0131] B4: (0,80,10);

[0132] Step 2: Calculate the location coordinates of the staff;

[0133] Using geometric algorithms, such as Euclidean distance, combined with the corrected distance data Mdi, the position coordinates of the staff in three-dimensional space are calculated. Assume that the result calculated by the geometric algorithm is:

[0134] X = 40 meters;

[0135] Y=40 meters;

[0136] Z = 5 meters;

[0137] Therefore, the worker's position coordinates are (40,40,5).

[0138] 5. Calculate the health sign assessment coefficient based on the physical sign data;

[0139] Step 1: Extract vital sign data;

[0140] Heart rate L = 140 beats / min;

[0141] Body temperature G=36.8℃;

[0142] Step 2: Calculate the health sign assessment coefficient;

[0143] The health sign assessment coefficient is calculated according to the formula P = A × e^[ln(L) / (ln(L)+1)]+B × e^[ln(G) / (ln(G)+1)];

[0144] Assume the scale factor is:

[0145] A=0.6;

[0146] B = 0.4;

[0147] Substitute into the formula to calculate:

[0148] P=0.6×e^[ln(140) / (ln(140)+1)]+0.4×e^[ln(36.8) / (ln(36.8)+1)]≈2.26;

[0149] Therefore, the staff's health sign assessment coefficient is 2.26.

[0150] 6. Determine whether to issue an early warning based on location information and health sign assessment coefficients;

[0151] Step 1: Check whether the location is within the danger zone;

[0152] Assume that there is a preset danger zone in the factory. Since the worker's location coordinates are (40,40,5), which is within the danger zone, the system will generate an early warning message and send it to the client.

[0153] Step 2: Check whether the health sign assessment coefficient exceeds the threshold;

[0154] Assume that the preset health sign assessment threshold is 1.5. Since the health sign assessment coefficient of the staff member is 2.26, which is greater than the preset health sign assessment threshold, the system generates an early warning message and sends it to the client.

[0155] Final result:

[0156] The system detected that the staff member entered the preset danger zone and had health problems, so the system triggered an early warning, reminding managers to pay attention to the employee's safety and take corresponding measures.

[0157] Through this example, it can be seen that the present invention can not only accurately locate the position of the staff in real time, but also correct the positioning results according to the environmental data to ensure high accuracy under different environmental conditions. In addition, the system can also evaluate the health status of employees through vital sign data, and combine location information for dual warning, providing more comprehensive and intelligent safety monitoring. This comprehensive monitoring method significantly improves the safety and management efficiency of the workplace, especially in complex environments, managers can quickly respond to emergencies, optimize path planning, and avoid potential safety risks.

[0158] Part of the data in the above formula is calculated by removing the dimension and taking its numerical value. The formula is a formula closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained through simulation of a large amount of data.

[0159] Working principle of the present invention:

[0160] The present invention obtains environmental data of a target area and monitoring data of a staff member; wherein the monitoring data includes: positioning data and vital sign data; based on the positioning data, the distance between the staff member and the UWB base station is calculated and marked as distance data; based on the environmental data, a correction model is constructed to correct the distance data to obtain target distance data; based on the target distance data, the position coordinates of the staff member are calculated and marked in real time in a preset three-dimensional model to obtain the position point information of the staff member in the preset three-dimensional model; based on the vital sign data, the health vital sign evaluation coefficient of the staff member is calculated; based on the position point information and the health vital sign evaluation coefficient, whether to issue an early warning is determined, thereby solving the technical problems of low positioning accuracy and low safety monitoring accuracy in the prior art.

[0161] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A staff monitoring system based on UWB positioning, characterized in that: include: Data collection module, data analysis module, positioning warning module and vital sign warning module; Data acquisition module: used to obtain the environmental data of the target area and the monitoring data of the staff in real time; the monitoring data includes: positioning data and vital sign data; Positioning analysis module: calculate the distance between the staff and the UWB base station based on the positioning data and mark it as distance data; construct a correction model based on the environmental data to correct the distance data and obtain the target distance data; and, The position coordinates of the staff are calculated based on the target distance data, and are marked in real time in the preset three-dimensional model to obtain the position point information of the staff in the preset three-dimensional model; Physical sign analysis module: calculates the health sign assessment coefficient of the staff based on the physical sign data; Early warning module: Determine whether to issue an early warning based on location information and health sign assessment coefficients.

2. A staff monitoring system based on UWB positioning according to claim 1, characterized in that: The real-time acquisition of environmental data of the target area and monitoring data of staff members includes: The data acquisition equipment is used to collect the environmental data of the target area and the vital signs data of the staff in real time; the target area refers to the working area that needs to be monitored; the environmental data include: ambient temperature and ambient humidity; the positioning data refers to the time difference between the wireless signal emitted by the UWB tag installed on the staff from the transmitter to the time it is received by several UWB base station receivers; the vital signs data include: heart rate and body temperature.

3. A staff monitoring system based on UWB positioning according to claim 1, characterized in that: The calculating the distance between the staff and the UWB base station based on the positioning data includes: Extract the vital sign data of the staff and mark it as Ti; where Ti represents the time difference from when the wireless signal emitted by the UWB tag is sent from the transmitter to when it is received by the i-th UWB base station receiver, i = {1, 2, 3, ..., n}, n refers to the number of UWB base stations, and n ≥ 3; The distance between the staff and the UWB base station is calculated by the formula di=[c / γ^(1 / 2)]×Ti; where di represents the distance between the staff and the i-th UWB base station; c is the speed of light, and γ is the relative dielectric constant of air under standard atmospheric conditions.

4. The staff monitoring system based on UWB positioning according to claim 1, characterized in that: The step of constructing a correction model based on the environmental data to correct the distance data includes: The environmental data is used as the independent variable, and the environmental temperature is marked as W, and the environmental humidity is marked as H; the corrected distance between the staff and the UWB base station is used as the dependent variable, and marked as the target distance data Mdi; The independent variables and dependent variables are fitted by polynomial fitting to establish a correction model; the correction model is specifically: Mdi=di×[α×e^[(W-ZW) / ZW]+β×e^[-H / (H+1)]]; wherein α and β are proportional coefficients, and when W-ZW>0, 0<α<1, when W-ZW<0, -1<α<0, when W-ZW=0, α=0; -1<β<0; ZW is the standard temperature.

5. The staff monitoring system based on UWB positioning according to claim 1, characterized in that: The method of calculating the position coordinates of the staff member based on the target distance data and marking them in real time in a preset three-dimensional model includes: Constructing a three-dimensional model of the target area to obtain a preset three-dimensional model; Establish a spatial rectangular coordinate system based on the preset three-dimensional model; The position coordinates of the staff are calculated based on the spatial rectangular coordinate system and the distance between the staff and the UWB base station.

6. A staff monitoring system based on UWB positioning according to claim 5, characterized in that: The constructing of the three-dimensional model of the target area includes: Collecting three-dimensional data of the target area through a three-dimensional acquisition device; wherein the three-dimensional acquisition device includes: a laser scanner or a 3D scanner; A three-dimensional model of the target area is constructed based on the three-dimensional data through three-dimensional construction software; wherein the three-dimensional construction software includes: AutoCAD, SketchUp or Blender.

7. The staff monitoring system based on UWB positioning according to claim 5, characterized in that: The establishing of a spatial rectangular coordinate system based on a preset three-dimensional model includes: Extracting a preset three-dimensional model of the target area; A spatial rectangular coordinate system is established by taking the center point of the preset three-dimensional model as the origin.

8. The staff monitoring system based on UWB positioning according to claim 5, characterized in that: The method of calculating the position coordinates of the staff member based on the spatial rectangular coordinate system and the distance between the staff member and the UWB base station includes: Extracting the established spatial rectangular coordinate system, and obtaining the coordinates of several UWB base stations in the target area in the spatial rectangular coordinate system; Based on the coordinates of several UWB base stations in the spatial rectangular coordinate system and the distance between the staff and the UWB base station, the coordinates of the staff's position in the spatial rectangular coordinate system are calculated.

9. The staff monitoring system based on UWB positioning according to claim 1, characterized in that: The health sign assessment coefficient of the staff member calculated based on the physical sign data includes: Label the heart rate as L and the body temperature as G; The health sign assessment coefficient is calculated through the formula: P = A×e^[ln(L) / (ln(L)+1)]+B×e^[ln(G) / (ln(G)+1)]; wherein P is the health sign assessment coefficient, A and B are proportional coefficients, and 0<A<1, 0<B<1.

10. The staff monitoring system based on UWB positioning according to claim 1, characterized in that: The determining whether to issue an early warning based on the location information and the health sign assessment coefficient includes: Extracting a preset three-dimensional model of the target area, and marking and dividing the preset dangerous area in the preset three-dimensional model; wherein the preset dangerous area refers to a prohibited entry area or a high-risk area in the target area; Determine whether the position coordinates of the staff are within the preset danger zone marked in the 3D model; if yes, generate warning information and send it to the client; if not, continue monitoring and judgment; Determine whether the health sign assessment coefficient is greater than the preset health sign assessment threshold; if yes, generate an early warning message and send it to the client; if not, continue monitoring and judging.

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