CN108362830A - The SCM Based infusion atmospheric monitoring system of one kind and method - Google Patents

Abstract

The invention belongs to the field of medical auxiliary equipment, and discloses a single-chip microcomputer-based transfusion air monitoring system and method, including an air detection module, a signal amplification module, an A/D conversion module, a feedback module, an identification module, a single-chip microcomputer module, a filter circuit module, State evaluation module, display module, input module, air monitoring and analysis module, alarm module. The present invention monitors the air in the infusion set of the infusion patient to prevent air bubbles or a large amount of air from entering the infusion pipeline, eliminate potential safety hazards, and ensure the safety of the patient especially during pressurized infusion; at the same time, it can analyze and display the patient's personal information and treatment information in real time , comparative analysis of the treatment process, simulated recovery date, etc.; it is conducive to improving the information management of the hospital, enhancing the safety of the patient's infusion process, and improving the work efficiency of medical staff.

Description

A single-chip microcomputer-based transfusion air monitoring system and method

technical field

The invention belongs to the field of medical auxiliary equipment, in particular to a single-chip microcomputer-based transfusion air monitoring system and method.

Background technique

At present, the development of medical equipment is getting faster and faster, and the requirements for medical auxiliary equipment are also getting higher and higher. As a conventional medical method, infusion plays a pivotal role in the treatment of patients. When the air enters the infusion set, it will embolize the blood vessel along with the liquid medicine entering the blood vessel, causing the heartbeat to stop, causing extremely serious consequences. The existing infusion set has huge potential safety hazards.

To sum up, the problem in the prior art is that the existing infusion set lacks an air monitoring device, which cannot meet the needs of users.

Contents of the invention

Aiming at the problems existing in the prior art, the present invention provides a single-chip microcomputer-based transfusion air monitoring system and method.

The present invention is achieved in that a kind of transfusion air monitoring system and method based on single-chip microcomputer comprises:

Air detection module for detecting air composition;

Connected with the air detection module, a signal amplification module for amplifying the monitored air component signal;

Connected with the signal amplification module, it is used to convert the analog signal into an A/D conversion module of a digital signal;

A feedback module connected with the A/D conversion module for feedback control of the signal;

An identification module connected with the feedback module for signal identification;

A single-chip microcomputer module connected with the identification module for signal processing and storage;

Connected with the single-chip microcomputer module, a filter circuit module for signal filtering and a state evaluation module for information state evaluation;

The wireless positioning method of the single-chip microcomputer module specifically includes the following steps:

The coordinates of the anchor node within the communication range of the node O to be located are A _i ( _xi , y _i ), where i=0,1,...,n(n≥4);

Step 1: The node to be positioned samples the received signal r(t) to obtain the sampled signal r(n), where n=0,1,...,N-1, N represents the number of subcarriers contained in the OFDM symbol, and records at the same time The sending node of the received signal is A _i ( _xi , y _i );

Step 2: Calculate the cross-correlation value E according to the sampling signal r(n):

Step 3: According to the logarithmic distance path loss model, the following formula is used to calculate the distance between the node to be located and the anchor node A _i :

Pr(d _i )=Pr(d ₀ )-10·γlg(d _i )+X _σ ;

Among them, Pr(d′ _i ) represents the cross-correlation value obtained when the distance from the sender is d _i ′, Pr(d ₀ ) represents the cross-correlation value obtained at a distance of d ₀ = 1 meter from the sender, and γ represents the path loss factor , lg( ) means the logarithmic operation with base 10, and X _σ follows a Gaussian distribution with mean 0 and standard deviation σ;

Use the above formula to calculate the distance between each anchor node and the node O to be positioned as d′ _i , and the coordinates of the corresponding anchor nodes are A _i (xi _, y _i ), where i=0,1,2, ...,n;

Step 4: Estimate the coordinates O(x,y) of the node to be positioned according to the adaptive distance correction algorithm;

The specific methods of step two include:

The first step is to construct a correlation window consisting of a continuous sampling sequence of length l at the same sampling position in consecutive m OFDM symbols, then the logarithmic likelihood function Λ(τ) corresponding to the correlation window is expressed as:

Among them, the independent variable τ represents the starting point of the correlation window, and m represents the number of consecutive OFDM symbols;

The second step is to slide the correlation window for N+L sampling point lengths to obtain the maximum value of the logarithmic likelihood function Λ(τ), and the sampling time corresponding to this value is the starting position of the OFDM symbol

in, Indicates the value of the independent variable τ when the function reaches the maximum value, Λ(τ) represents the logarithmic likelihood function, m represents the number of continuous OFDM symbols, l represents the length of the continuous sampling sequence at the same sampling position, r(n) represents Sampling signal, N represents the number of subcarriers contained in the OFDM symbol, L represents the number of sampling points in the cyclic prefix part of the OFDM symbol, |·| is a modulo operator;

The third step, according to the starting position of the OFDM symbol Calculate the cross-correlation value E:

Step four specifically includes:

The first step is to select the differential correction point, determine the coordinates of the positioning intersection point and the complex number of positioning intersection points, and calculate the distance between the positioning intersection points;

Select the anchor node A ₀ with the smallest distance value from d′ _i (i=0,1,2,…,n) as the difference correction point, and then take the 3 smallest distance values from the remaining distance values, assuming these 3 The distance values are d′ ₁ , d′ ₂ and d′ ₃ respectively, and the corresponding anchor node coordinates are A ₁ (x ₁ ,y ₁ ), A ₂ (x ₂ ,y ₂ ) and A ₃ (x ₃ , y ₃ ), taking the anchor node A _i (xi _, y _i ) as the center and d′ _i as the radius to make three positioning circles i, where i=1, 2, 3, there are 6 intersections of the three positioning circles Type, there are two intersection points between two circles, these two intersection points are two equal real number intersection points, or two unequal real number intersection points, or two complex number intersection points; from the two intersection points of two positioning circles, Select the intersection point with the smaller distance from the center coordinates of the third positioning circle as the positioning intersection point to participate in the positioning of the node to be positioned; determine the number m' of three positioning intersection points and complex positioning intersection points by 3 positioning circles, The coordinates of the positioning intersection determined by 2 and positioning circle 3 are A'(x ₁ ,y ₁ ), the coordinates of the positioning intersection determined by positioning circle 1 and positioning circle 3 are B'(x ₂ ,y ₂ ), and the coordinates of the positioning intersection determined by positioning circle 1 and 3 are B'(x 2 ,y 2 ). The coordinates of the positioning intersection determined with the positioning circle 2 are C′(x ₃ , y ₃ ), and the distances between the positioning intersections A’ and B’, B’ and C’, A’ and C’ are d ₁₂ , d ₂₃ , d ₁₃ :

The second step is to set the threshold T, the individual difference coefficient correction coefficient ω, and the parameter λ (λ>0);

The third step is to judge whether d′ ₁ , d′ 2 , and d _{′ 3} need to be corrected according to the distances d ₁₂ , d ₂₃ , and d ₁₃ between _the three positioning intersection points. If d ₁₂ <T, d ₂₃ <T, d ₁₃ <T, then there is no need to correct d′ ₁ , d′ ₂ , d′ ₃ , go to the fifth step, otherwise, it is necessary to make corrections to d′ ₁ , d′ ₂ , d′ ₃ four steps;

The fourth step is to adjust the direction correction factors λ ₁ , λ ₂ and λ ₃ of the three measurement distances, and correct d′ ₁ , d′ ₂ , and d′ ₃ according to the following adaptive distance correction formula, and obtain the corrected distances as d ₁ , d ₂ , d ₃ :

Among them, d _i represents the correction distance between the node to be located and the anchor node A _i , d _0i represents the actual distance between the difference correction point A ₀ and the anchor node A _i , d′ _0i represents the difference between the correction point A ₀ and the anchor node The measurement distance between A _i , ω represents the individual difference coefficient correction coefficient, λ _i represents the direction correction factor, and exp(·) represents the exponential function;

According to the corrected distances d ₁ , d ₂ , d ₃ , recalculate the corrected distances d ₁₂ , d ₂₃ , and d ₁₃ between the three positioning intersection points, and return to the third step;

The fifth step is to calculate the positioning coordinate O(x ₀ ,y ₀ ) of the node to be positioned according to the following formula:

Among them, α ₁ , α ₂ , and α ₃ represent the weights of x′ ₁ , x′ ₂ , and x′ ₃ respectively, and β ₁ , β ₂ , and β ₃ represent the weights of y′ ₁ , y′ ₂ , and y′ ₃ respectively;

A display module connected with the status evaluation module for displaying information;

The method for calibrating the color information of the display module specifically includes the following steps:

Step 1. Select the calibration color card and the calibration light source. The calibration color card has no less than 24 color samples. According to the spectral reflectance ρ _i (λ) of the calibration color card N color samples and the spectral intensity distribution of the calibration light source Color matching function combined with CIE1931 standard chromaticity system Calculate the CIEXYZ tristimulus value (X _i , Y _i , Z _i ) of the N color samples of the calibration color card under the CIE1931 standard chromaticity system through the following two formulas;

Calculate the CIEXYZ tristimulus value (X _W , Y _W , Z _W ) of the calibration light source under the CIE1931 standard chromaticity system by the following formula;

Among them, Δλ is the spectral sampling interval used in the calculation, taking 5nm, i is the serial number of the N color samples of the calibration color card, i=1,2,3,...,N;

Step 2. Substitute (X _i , Y _i , Zi ) and (X _W , Y _W , Z _{W )} obtained in step 1 into the following two formulas to calculate the coordinates of each color sample in the uniform color space CIELAB

Step 3: Use the reference imaging system and the imaging system to be calibrated respectively to image the N color samples under the calibration light source, record and obtain the color information of the digital image, and read the corresponding digital driver of each color sample in the two imaging systems Values (R _Si , G _Si , B _Si ) and (R _Ti , G _Ti , B _Ti );

Step 4. For the imaging system to be calibrated, according to the CIELAB coordinates of the N color samples obtained in step 2 and the N color sample digital driving values (R _Ti , G _Ti , B _Ti ) obtained in step 3, and use the least square method to fit the following formula from (R _Ti , G _Ti , B _Ti ) to The mapping matrix M _T , M _T is a 3×11 matrix;

Step 5. For the reference imaging system, according to the CIELAB coordinates of the N color samples obtained in step 2 and the digital driving values (R _Si , G _Si , B _Si ) of the N color samples obtained in step 3, using the least squares method to fit Predict the mapping matrix H _SI to (R _Si , G _Si , B _Si ), H _SI is a 3×10 matrix;

Step 6. For the digital image acquired by the imaging system to be calibrated in any scene under any imaging environment, using the mapping matrix M _T obtained in step 4, the digital driving value of each pixel (R _Tj ', G _Tj ', B _Tj ') to predict the corresponding CIELAB space coordinates Where j=1,2,3,...,N', N' is the total number of pixels of the digital image acquired by the imaging system to be calibrated;

Step 7. For the CIELAB space coordinates of each pixel of the imaging system to be calibrated obtained in step 6 Using the mapping matrix H _SI obtained in step 5, the calibrated digital driving value (R _Sj ', G _Sj ', B _Sj ') corresponding to each pixel is predicted by the following formula, that is, the color matching between the two imaging systems is completed. Information calibration, so that the digital image acquired by the imaging system to be calibrated in a certain scene in any imaging environment has the same digital driving value as the reference imaging system;

An input module connected with the display module for inputting information;

Connected with the filter circuit module, it is used to detect and analyze the air monitoring and analysis module of the control component;

The alarm module is connected with the air monitoring analysis module and is used for alarming abnormal signals.

The advantages and positive effects of the present invention are: the present invention detects the type of air through the air detection module, and after signal conversion, it is analyzed by the air monitoring and analysis module. When an abnormal situation occurs, the alarm module can be used to give an alarm prompt to avoid air bubbles or a large number of Air enters the infusion pipeline to eliminate potential safety hazards, especially during pressurized infusion to ensure patient safety; at the same time, a status evaluation module is equipped to analyze and display the patient's personal information, treatment information, comparative analysis of treatment process, simulated recovery date, etc. in real time; It is conducive to improving the information management of the hospital, enhancing the safety of the patient's infusion process, and improving the work efficiency of medical staff.

Description of drawings

Fig. 1 is the infusion air monitoring system and the method structure schematic diagram based on the single-chip microcomputer that the embodiment of the present invention provides;

In the figure: 1. Air detection module; 2. Signal amplification module; 3. A/D conversion module; 4. Feedback module; 5. Identification module; 6. Single-chip microcomputer module; 7. Filter circuit module; 8. Status evaluation module; 9. Display module; 10. Input module; 11. Air monitoring and analysis module; 12. Alarm module.

Detailed ways

In order to further understand the content, features and effects of the present invention, the following examples are given, and detailed descriptions are given below with reference to the accompanying drawings.

The structure of the present invention will be described in detail below in conjunction with the accompanying drawings.

As shown in Figure 1, the transfusion air monitoring system and method based on the single-chip microcomputer include an air detection module 1 for detecting air components;

Connected with the air detection module 1, a signal amplification module 2 for amplifying the monitored air component signal;

Connected with the signal amplification module 2, it is used to convert the analog signal into an A/D conversion module 3 of a digital signal;

Connected with the A/D conversion module 3, a feedback module 4 for feedback control of the signal;

Connected with the feedback module 4, an identification module 5 for signal identification;

Be connected with identification module 5, be used for processing and the single-chip microcomputer module 6 of storage of signal;

Be connected with single-chip microcomputer module (MCS-51) 6, be used for the state evaluation module 8 of the filter circuit module 7 of signal filtering and information state evaluation;

Connected with the state assessment module 8, a display module 9 for displaying information;

Connected with the display module 9, an input module 10 for inputting information;

Connected with the filter circuit module 7, an air monitoring and analysis module 11 for detecting and analyzing the control components;

It is connected with the air monitoring analysis module 11 and is used for an alarm module 12 for alarming abnormal signals.

Further, the alarm module 12 includes an LED signal light and a buzzer alarm.

Further, the air detection module 1 includes a CO2 sensor for detecting gas types, an N2 sensor for detecting nitrogen, and a temperature sensor for detecting temperature.

The wireless positioning method of the single-chip microcomputer module specifically includes the following steps:

The coordinates of the anchor node within the communication range of the node O to be located are A _i ( _xi , y _i ), where i=0,1,...,n(n≥4);

Step 1: The node to be positioned samples the received signal r(t) to obtain the sampled signal r(n), where n=0,1,...,N-1, N represents the number of subcarriers contained in the OFDM symbol, and records at the same time The sending node of the received signal is A _i ( _xi , y _i );

Step 2: Calculate the cross-correlation value E according to the sampling signal r(n):

Step 3: According to the logarithmic distance path loss model, the following formula is used to calculate the distance between the node to be located and the anchor node A _i :

Pr(d _i )=Pr(d ₀ )-10·γlg(d _i )+X _σ ;

Among them, Pr(d′ _i ) represents the cross-correlation value obtained when the distance from the sender is d′ _i , Pr(d ₀ ) represents the cross-correlation value obtained at a distance of d ₀ = 1 meter from the sender, and γ represents the path loss factor , lg( ) means the logarithmic operation with base 10, and X _σ follows a Gaussian distribution with mean 0 and standard deviation σ;

Use the above formula to calculate the distance between each anchor node and the node O to be positioned as d′ _i , and the coordinates of the corresponding anchor nodes are A _i (xi _, y _i ), where i=0,1,2, ...,n;

Step 4: Estimate the coordinates O(x,y) of the node to be positioned according to the adaptive distance correction algorithm;

The specific methods of step two include:

The first step is to construct a correlation window consisting of a continuous sampling sequence of length l at the same sampling position in consecutive m OFDM symbols, then the logarithmic likelihood function Λ(τ) corresponding to the correlation window is expressed as:

Among them, the independent variable τ represents the starting point of the correlation window, and m represents the number of consecutive OFDM symbols;

The second step is to slide the correlation window for N+L sampling point lengths to obtain the maximum value of the logarithmic likelihood function Λ(τ), and the sampling time corresponding to this value is the starting position of the OFDM symbol

in, Indicates the value of the independent variable τ when the function reaches the maximum value, Λ(τ) represents the logarithmic likelihood function, m represents the number of continuous OFDM symbols, l represents the length of the continuous sampling sequence at the same sampling position, r(n) represents Sampling signal, N represents the number of subcarriers contained in the OFDM symbol, L represents the number of sampling points in the cyclic prefix part of the OFDM symbol, |·| is a modulo operator;

The third step, according to the starting position of the OFDM symbol Calculate the cross-correlation value E:

Step four specifically includes:

The first step is to select the differential correction point, determine the coordinates of the positioning intersection point and the complex number of positioning intersection points, and calculate the distance between the positioning intersection points;

Select the anchor node A ₀ with the smallest distance value from d′ _i (i=0,1,2,…,n) as the difference correction point, and then take the 3 smallest distance values from the remaining distance values, assuming these 3 The distance values are d′ ₁ , d′ ₂ and d′ ₂ respectively, and the corresponding anchor node coordinates are A ₁ (x ₁ ,y ₁ ), A ₂ (x ₂ ,y ₂ ) and A ₃ (x ₃ , y ₃ ), taking the anchor node A _i (xi _, y _i ) as the center and d′ _i as the radius to make three positioning circles i, where i=1, 2, 3, there are 6 intersections of the three positioning circles Type, there are two intersection points between two circles, these two intersection points are two equal real number intersection points, or two unequal real number intersection points, or two complex number intersection points; from the two intersection points of two positioning circles, Select the intersection point with the smaller distance from the center coordinates of the third positioning circle as the positioning intersection point to participate in the positioning of the node to be positioned; determine the number m' of three positioning intersection points and complex positioning intersection points by 3 positioning circles, The coordinates of the positioning intersection determined by 2 and positioning circle 3 are A'(x ₁ ,y ₁ ), the coordinates of the positioning intersection determined by positioning circle 1 and positioning circle 3 are B'(x ₂ ,y ₂ ), and the coordinates of the positioning intersection determined by positioning circle 1 and 3 are B'(x 2 ,y 2 ). The coordinates of the positioning intersection determined with the positioning circle 2 are C′(x ₃ , y ₃ ), and the distances between the positioning intersections A’ and B’, B’ and C’, A’ and C’ are d ₁₂ , d ₂₃ , d ₁₃ :

The second step is to set the threshold T, the individual difference coefficient correction coefficient ω, and the parameter λ (λ>0);

The third step is to judge whether d′ ₁ , d′ 2 , and d _{′ 3} need to be corrected according to the distances d ₁₂ , d ₂₃ , and d ₁₃ between _the three positioning intersection points. If d ₁₂ <T, d ₂₃ <T, d ₁₃ <T, then there is no need to correct d′ ₁ , d′ ₂ , d′ ₃ , go to the fifth step, otherwise, it is necessary to make corrections to d′ ₁ , d′ ₂ , d′ ₃ four steps;

The fourth step is to adjust the direction correction factors λ ₁ , λ ₂ and λ ₃ of the three measurement distances, and correct d′ ₁ , d′ ₂ , and d′ ₃ according to the following adaptive distance correction formula, and obtain the corrected distances as d ₁ , d ₂ , d ₃ :

Among them, d _i represents the correction distance between the node to be located and the anchor node A _i , d _0i represents the actual distance between the difference correction point A ₀ and the anchor node A _i , d′ _0i represents the difference between the correction point A ₀ and the anchor node The measurement distance between A _i , ω represents the individual difference coefficient correction coefficient, λ _i represents the direction correction factor, and exp(·) represents the exponential function;

According to the corrected distances d ₁ , d ₂ , d ₃ , recalculate the corrected distances d ₁₂ , d ₂₃ , and d ₁₃ between the three positioning intersection points, and return to the third step;

The fifth step is to calculate the positioning coordinate O(x ₀ ,y ₀ ) of the node to be positioned according to the following formula:

Wherein, α ₁ , α ₂ , and α ₃ represent the weights of x′ ₁ , x′ ₂ , and x′ ₃ respectively, and β ₁ , β ₂ , and β ₃ represent the weights of y′ ₁ , y′ ₂ , and y′ ₃ respectively.

The method for calibrating the color information of the display module specifically includes the following steps:

Step 1. Select the calibration color card and the calibration light source. The calibration color card has no less than 24 color samples. According to the spectral reflectance ρ _i (λ) of the calibration color card N color samples and the spectral intensity distribution of the calibration light source Color matching function combined with CIE1931 standard chromaticity system Calculate the CIEXYZ tristimulus value (X _i , Y _i , Z _i ) of the N color samples of the calibration color card under the CIE1931 standard chromaticity system through the following two formulas;

Calculate the CIEXYZ tristimulus value (X _W , Y _W , Z _W ) of the calibration light source under the CIE1931 standard chromaticity system by the following formula;

Among them, Δλ is the spectral sampling interval used in the calculation, taking 5nm, i is the serial number of the N color samples of the calibration color card, i=1,2,3,...,N;

Step 2. Substitute (X _i , Y _i , Zi ) and (X _W , Y _W , Z _{W )} obtained in step 1 into the following two formulas to calculate the coordinates of each color sample in the uniform color space CIELAB

Step 3: Use the reference imaging system and the imaging system to be calibrated respectively to image the N color samples under the calibration light source, record and obtain the color information of the digital image, and read the corresponding digital driver of each color sample in the two imaging systems Values (R _Si , G _Si , B _Si ) and (R _Ti , G _Ti , B _Ti );

Step 4. For the imaging system to be calibrated, according to the CIELAB coordinates of the N color samples obtained in step 2 and the N color sample digital driving values (R _Ti , G _Ti , B _Ti ) obtained in step 3, and use the least square method to fit the following formula from (R _Ti , G _Ti , B _Ti ) to The mapping matrix M _T , M _T is a 3×11 matrix;

Step 5. For the reference imaging system, according to the CIELAB coordinates of the N color samples obtained in step 2 and the digital driving values (R _Si , G _Si , B _Si ) of the N color samples obtained in step 3, using the least squares method to fit Predict the mapping matrix H _SI to (R _Si , G _Si , B _Si ), H _SI is a 3×10 matrix;

Step 6. For the digital image acquired by the imaging system to be calibrated in any scene under any imaging environment, using the mapping matrix M _T obtained in step 4, the digital driving value of each pixel (R _Tj ', G _Tj ', B _Tj ') to predict the corresponding CIELAB space coordinates Where j=1,2,3,...,N', N' is the total number of pixels of the digital image acquired by the imaging system to be calibrated;

Step 7. For the CIELAB space coordinates of each pixel of the imaging system to be calibrated obtained in step 6 Using the mapping matrix H _SI obtained in step 5, the calibrated digital driving value (R _Sj ', G _Sj ', B _Sj ') corresponding to each pixel is predicted by the following formula, that is, the color matching between the two imaging systems is completed. Information calibration, so that the digital image acquired by the imaging system to be calibrated in a certain scene in any imaging environment has the same digital driving value as the reference imaging system;

The detection of air is carried out by the air detection module 1, and the detected air data is amplified by the signal amplifier 2, and the amplified signal is converted into a digital signal by the A/D signal converter 3, and passed by the feedback control module 4 Carry out signal feedback adjustment, identify the amplified signal through the identification module 5, pass the identified signal to the single-chip microcomputer module 6 for data processing and storage, filter out the clutter through the filter circuit module 6, and only allow the air signal waveform to pass through. The air monitoring and analysis module 7 detects and identifies, identifies, confirms and analyzes. When abnormal, the air monitoring and analysis module 7 detects the air electrical signal and triggers the LED signal indicator light and the buzzer alarm in the alarm module 12 to alarm and notify the medical staff During use, the patient information status can be entered through the input module 10, stored by the single-chip microcomputer module 6, evaluated by the status evaluation module 8 for various indicators and information, and displayed in real time by the display screen 9.

The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the present invention in any form. Any simple modifications made to the above embodiments according to the technical essence of the present invention, equivalent changes and modifications, all belong to this invention. within the scope of the technical solution of the invention.

Claims (4)

1. The utility model provides a transfusion air monitoring system based on singlechip which characterized in that, transfusion air monitoring system based on singlechip includes:

the air detection module is used for detecting air components;

the signal amplification module is connected with the air detection module and is used for amplifying the monitored air component signals;

the A/D conversion module is connected with the signal amplification module and is used for converting the analog signal into a digital signal;

the feedback module is connected with the A/D conversion module and is used for performing feedback control on the signal;

the identification module is connected with the feedback module and used for signal identification;

the singlechip module is connected with the identification module and is used for processing and storing signals;

the filter circuit module is connected with the singlechip module and is used for filtering signals and the state evaluation module is used for evaluating information states;

the wireless positioning method of the single chip microcomputer module specifically comprises the following steps:

the coordinate of the anchor node in the communication range of the node O to be positioned is A_i(x_i,y_i) Wherein i is 0,1, …, n (n is more than or equal to 4);

the method comprises the following steps: sampling a received signal r (t) by a node to be positioned to obtain a sampling signal r (N), wherein N is 0,1, …, N-1, N represents the number of subcarriers contained in an OFDM symbol, and simultaneously recording the number of a transmitting node of the received signal as A_i(x_i,y_i)；

Step two: from the sampled signal r (n), a cross-correlation value E is calculated:

step three: according to the logarithmic distance path loss model, the node to be positioned and the anchor node A are calculated according to the following formula_iThe distance between:

Pr(d_i)＝Pr(d₀)-10·γlg(d_i)+X_σ；

wherein, Pr (d'_i) Representing distance d 'from transmitting end'_iTime-derived cross-correlation value, Pr (d)₀) Indicating distance from sender d₀The cross-correlation value obtained at 1 meter, γ represents the path loss factor, lg (·) represents a logarithmic operation with a base of 10, X_σObeying a Gaussian distribution with a mean value of 0 and a standard deviation of sigma;

calculating the distances d 'between each anchor node and the node O to be positioned by utilizing the formula'_iThe coordinates of the corresponding anchor nodes are respectively A_i(x_i,y_i) Where i is 0,1,2, …, n;

step four: estimating the coordinates O (x, y) of the node to be positioned according to a self-adaptive distance correction algorithm;

the specific method of the second step comprises the following steps:

firstly, constructing a correlation window consisting of continuous sampling sequences with the length of l at the same sampling position in m continuous OFDM symbols, and then expressing a log-likelihood function Λ (τ) corresponding to the correlation window as follows:

wherein, the argument τ represents the starting point of the correlation window, and m represents the number of consecutive OFDM symbols;

and secondly, sliding the correlation window by the length of N + L sampling points to obtain the maximum value of the log-likelihood function Lambda (tau), wherein the sampling time corresponding to the maximum value is the initial position of the OFDM symbol

Wherein,representing the value of an independent variable tau when the function obtains the maximum value, representing a log-likelihood function by Λ (tau), representing the number of continuous OFDM symbols by m, representing the length of continuous sampling sequences at the same sampling position by L, representing a sampling signal by r (N), representing the number of subcarriers contained in the OFDM symbols by N, representing the number of sampling points of a cyclic prefix part in the OFDM symbols by L, and being a modulo operator by L;

thirdly, according to the starting position of the OFDM symbolCalculating a cross-correlation value E:

the fourth step specifically comprises:

firstly, selecting a differential correction point, determining a coordinate of a positioning intersection point and a plurality of positioning intersection points, and calculating the distance between the positioning intersection points;

from d'_i(i-0, 1,2, …, n) selecting the anchor node A with the smallest distance value₀For the difference correction point, the 3 smallest distance values are extracted from the remaining distance values, assuming that these 3 are distance values d'₁、d′₂And d'₃The coordinates of the corresponding anchor nodes are respectively A₁(x₁,y₁)、A₂(x₂,y₂) And A₃(x₃,y₃) Respectively with anchor nodes A_i(x_i,y_i) Is the center of a circle, d'_iThree positioning circles i are made for the radius, wherein i is 1,2 and 3, 6 intersection conditions of the three positioning circles exist, two intersection points exist between the two circles, and the two intersection points are two equal real number intersection points or two unequal real number intersection points or two complex number intersection points; selecting one intersection point with a smaller distance from the center coordinates of the third positioning circle from two intersection points of the two positioning circles as a positioning intersection point to participate in positioning of the node to be positioned; the three positioning intersection points and the number m 'of the plurality of positioning intersection points are determined by 3 positioning circles, and the coordinates of the positioning intersection points determined by the positioning circles 2 and 3 are A' (x)₁,y₁) And the coordinates of the positioning intersection point defined by the positioning circle 1 and the positioning circle 3 are B' (x)₂,y₂) The coordinate of the positioning intersection point defined by the positioning circle 1 and the positioning circle 2 is C' (x)₃,y₃) The distances between the positioning intersection points A 'and B', B 'and C', A 'and C' are d₁₂、d₂₃、d₁₃：

Secondly, setting a threshold T, an individual difference coefficient correction coefficient omega and a parameter lambda (lambda > 0);

thirdly, according to the distances d between the three positioning intersection points₁₂、d₂₃And d₁₃Judging whether d 'is needed'₁、d′₂、d′₃Make a correction if d₁₂<T、d₂₃<T、d₁₃<T, then do not need to be d'₁、d′₂、d′₃Correcting, executing the fifth step, otherwise, d 'needs to be corrected'₁、d′₂、d′₃Correcting and executing the fourth step;

fourthly, adjusting direction correction factors lambda of three measuring distances₁、λ₂And λ₃D 'is corrected according to the following adaptive distance correction formula'₁、d′₂、d′₃Obtaining a corrected distance d₁、d₂、d₃：

Wherein d is_iRepresenting the node to be positioned and the anchor node A_iCorrected distance between d_0iRepresenting a differential correction point A₀And anchor node A_iActual distance between, d'_0iRepresenting a differential correction point A₀And anchor node A_iA measured distance therebetween, ω represents an individual difference coefficient correction coefficient, λ_iRepresents the directional correction factor, exp (-) represents the exponential function;

according to the corrected distance d₁、d₂、d₃Re-solving the distance d between the three corrected positioning intersections₁₂、d₂₃、d₁₃Returning to the third step;

fifthly, calculating the positioning coordinate O (x) of the node to be positioned according to the following formula₀,y₀)：

wherein alpha is₁、α₂、α₃Respectively represent x'₁、x′₂、x′₃weight of (1), beta₁、β₂、β₃Are respectively y'₁、y′₂、y′₃The weight of (c);

the display module is connected with the state evaluation module and used for displaying information;

the display module color information calibration method specifically comprises the following steps:

selecting a calibration color card and a calibration light source, wherein the calibration color card is not less than 24 color samples, and the spectral reflectance rho of the N color samples of the calibration color card is determined_i(lambda) and spectral intensity distribution of a calibration light sourceColor matching function in combination with CIE1931 standard chromaticity systemCalculating CIEXYZ tristimulus values (X) of N color samples of the calibration color chart under a CIE1931 standard chromaticity system through the following two formulas_i,Y_i,Z_i)；

The CIEXYZ tristimulus value (X) of the calibration light source under the CIE1931 standard chromaticity system is calculated by the following formula_W,Y_W,Z_W)；

Wherein, Δ λ is a spectrum sampling interval adopted in the calculation, 5nm is taken, i is a serial number of N color samples of the calibration color chart, and i is 1,2,3, …, N;

step two, the (X) obtained in the step one_i,Y_i,Z_i) And (X)_W,Y_W,Z_W) Substituting the two formulas to calculate the coordinate of each color sample in the uniform color space CIELAB

Step three, respectively adopting a reference imaging system and an imaging system to be calibrated to image N color samples under a calibration light source, recording and acquiring color information of the digital image, and reading corresponding digital driving values (R) of each color sample in the two imaging systems_Si,G_Si,B_Si) And (R)_Ti,G_Ti,B_Ti)；

Step four, for the imaging system to be calibrated, according to the CIELAB coordinates of the N color samples obtained in the step twoAnd the digital driving values (R) of the N color samples obtained in the third step_Ti,G_Ti,B_Ti) Fitting the formula (R) by using a least square method_Ti,G_Ti,B_Ti) Predict toMapping matrix M of_T，M_TIs a 3 × 11 matrix;

step (ii) ofFifthly, for the reference imaging system, obtaining the CIELAB coordinates of the N color samples according to the step twoAnd the digital driving values (R) of the N color samples obtained in the third step_Si,G_Si,B_Si) Fitting out a product ofPredict to (R)_Si,G_Si,B_Si) Mapping matrix H of_SI，H_SIIs a 3 × 10 matrix;

step six, adopting the mapping matrix M obtained in the step four to the digital image obtained by the imaging system to be calibrated in any scene under any imaging environment_TFrom a digital drive value (R) per pixel by the following formula_Tj',G_Tj',B_Tj') predict the corresponding CIELAB space coordinatesWherein j is 1,2,3, …, N' is the total number of pixels of the digital image acquired by the imaging system to be calibrated;

step seven, the CIELAB space coordinate of each pixel of the imaging system to be calibrated obtained in the step sixAdopting the mapping matrix H obtained in the step five_SIThe calibrated digital drive value (R) for each pixel is predicted by the following equation_Sj',G_Sj',B_Sj') to finish the color information calibration between the two imaging systems, so that the imaging system to be calibrated can be in any imaging environmentA digital image acquired of a scene has digital drive values consistent with a reference imaging system;

the input module is connected with the display module and used for inputting information;

the air monitoring and analyzing module is connected with the filter circuit module and is used for detecting and analyzing the control components;

and the alarm module is connected with the air monitoring and analyzing module and used for alarming an abnormal signal.

2. The single-chip microcomputer based transfusion air monitoring system as claimed in claim 1, wherein the alarm module comprises an LED signal lamp and a buzzer alarm.

3. The single-chip microcomputer based infusate air monitoring system as claimed in claim 1, wherein the air detection module includes a CO for detecting a gas type₂Sensor, N for detecting nitrogen₂The sensor, be used for detecting the temperature sensor of temperature.

4. The single-chip microcomputer based transfusion air monitoring system according to claim 1, wherein the single-chip microcomputer based transfusion air monitoring method of the single-chip microcomputer based transfusion air monitoring system detects air through an air detection module, amplifies detected air data through a signal amplifier, converts an analog signal into a digital signal through an A/D signal converter after the amplified signal, performs feedback regulation of the signal through a feedback control module, performs identification of the amplified signal through an identification module, transmits the identified signal to the single-chip microcomputer module for data processing and storage, filters out noise waves through a filter circuit module, allows only air signal waveforms to pass through, performs detection identification through the air monitoring analysis module, performs identification confirmation and analysis, and triggers an LED signal indicator lamp and a buzzer alarm in the alarm module after the air monitoring analysis module detects the air electrical signal in case of abnormality Alarming and informing medical personnel to carry out treatment; in the use process, the information input can be carried out on the information state of the patient through the input module, the information is stored by the single chip microcomputer module, the evaluation of each index and information is carried out through the state evaluation module, and the real-time display is carried out through the display screen.