CN106227034A - The Temperature fusion of isolated organ perfusion instrument and control system - Google Patents

Abstract

The present invention relates to Temperature fusion and the control system of a kind of isolated organ perfusion instrument, including main control unit, the four-way temperature collecting cell electrically connected with described main control unit, compressor unit and Ventilating valve unit, and temperature control unit；Described temperature control unit includes that Temperature fusion subelement and temperature based on fuzzy controller based on improving Bayesian Estimation control subelement.Use the data fusion subelement improving Bayesian Estimation, false with the data overcoming possible sensor fault or environmental disturbances to cause, and four drive test amount data fusion are become a circuit-switched data, secondly use fuzzy-adaptation PID control subelement to implement high-accuracy and constant temperature control.The present invention not only can effectively promote the temperature control precision of isolated organ perfusion instrument, and can reduce the risk measuring inefficacy that external interference is brought, and improves stability of instrument, has potential economic worth.

Description

The Temperature fusion of isolated organ perfusion instrument and control system

Technical field

The invention belongs to medical instruments field, the Temperature fusion of a kind of isolated organ perfusion instrument and control system.

Background technology

Maintaining low temperature environment is the basis protecting isolated organ cell tissue, and its protection mechanism is: organ removal and local Ischemia causes ATP (adenosine triphosphate) energy and oxygen supply to lack, and makes organ cell be converted into anoxia from aerobic metabolism rapidly Metabolism, generates lactic acid and proton, and cell depolarization phenomenon occurs, and then cause ion concentration unbalance and necrocytosis.Relevant It is the immediate cause causing cell death that scholar finds that proton and calcium concentration rise.But, the ultimate principle of biochemical reaction is all Molecule activity and migration, and it is by the heat domination obtained, in other words, temperature reduction can cause molecule activity to slow down, from And make intracellular biochemical response activity the weakest.Research proves, along with the change relevant with ischemic hypoxia Process is aborted, and the further deterioration of isolated organ is the most effectively stoped.

Research shows, the optimal storage temperature of isolated organ is 4 DEG C, now can not only avoid direct frost damage, Er Qie great The histiocytic energy metabolism level of big reduction, improves tissue ischemia resisting ability, but high-precision liquid temperature control ratio is in high precision Motor control is much more difficult, especially the flowing primer solution of large space, and its reason is not only the problem of high-precision sensor, more It is important that temperature control liquid itself has big inertia, large dead time and nonlinear feature.

For this feature, intelligent control algorithm such as fuzzy control, neutral net, PREDICTIVE CONTROL, PID and heredity is used to calculate Method can reach the purpose that system quickly responds, but single algorithm cannot meet the requirement of high accuracy and quickly response simultaneously, and Being mutually combined and sufficiently complex between intelligent algorithm.Additionally, in tradition isolated organ perfusion instrument Temperature fusion and control method In, due to the factor such as temperature sensor fault or environmental disturbances, it can be provided that false temperature measuring data, this wrong data Generation will automatically set to instrument and bring misleading, bring erroneous judgement also can to organ caregiver, finally bring inestimable Life and economic loss.

Summary of the invention

Goal of the invention: provide a kind of isolated organ perfusion Temperature fusion of instrument and control system, deposits solving prior art The problems referred to above, improve temperature control precision, and reduce incipient fault cause isolated organ preserve lost efficacy risk.

Technical scheme:

Beneficial effect: the present invention not only can promote the temperature control precision of isolated organ perfusion instrument effectively, in vitro device Official provides stable temperature environment, and can reduce and measure, due to what temperature sensor fault or environmental disturbances were brought, the wind lost efficacy Danger, improves the stability of instrument, has potential economic worth.

Accompanying drawing explanation

Fig. 1 is that the perfusion instrument constant temperature control circuit of the present invention connects block diagram.

Fig. 2 is the four tunnel temperature sensor scheme of installations of the present invention.

Fig. 3 is that the TSic506 transmission bag of the present invention reads flow chart.

Fig. 4 is compressor and the Ventilating valve scheme of installation of the present invention.

Fig. 5 is dsp chip and the drive circuit optical coupling isolation circuit figure of the present invention.

Fig. 6 is that the SCI serial ports of the present invention controls program flow diagram.

Fig. 7 a to Fig. 7 d is the temperature sensor fault simulation curve of the present invention, wherein, I road: the most by mistake survey fault；The II road: downward spike surveys fault by mistake；Ii I road: upwards spike surveys fault by mistake；Iv road: shaking interference fault).

Fig. 8 a to Fig. 8 c is the bayesian data fusion curve of the present invention, and wherein Fig. 8 a is that tradition bayesian data fusion is bent Line；Fig. 8 b1 to 8b4 is m²=0.1, m²=0.4, m²=0.8 and m²Improvement bayesian data fusion curve when=1；Fig. 8 c is Cascade improves bayesian data fusion curve.

Fig. 9 is the DC frequency-changeable compressor control principle drawing of the present invention.

Figure 10 is the fuzzy-adaptation PID control schematic diagram of the present invention.

Figure 11 a to Figure 11 c is the dynamic performance testing curve of the present invention, and wherein, Figure 11 a is variations in temperature test curve；

Figure 11 b is perfusion flow rate environments change curve；Figure 11 c is operational mode change test curve.

Detailed description of the invention

The invention provides Temperature fusion and the control system of a kind of isolated organ perfusion instrument, by perfusion instrument thermostatic control electricity Road and perfusion instrument temperature control unit two parts composition, to promote temperature control precision and to reduce the risk that measurement was lost efficacy.

Seeing Fig. 1, perfusion instrument constant temperature control circuit connects block diagram with DSP (TMS320F2812) chip as main control unit, outward Enclose unit to include: four-way temperature collecting cell, compressor unit, Ventilating valve unit and other auxiliary unit.Wherein, four-way Channel temp collecting unit is used for temperature acquisition, compressor and Ventilating valve unit and controls for temperature, each unit and the configuration of DSP Interface list is as shown in table 1.

Table 1 DSP peripheral unit interface configures

Seeing Fig. 2 and Fig. 3, four-way temperature collecting cell utilizes TSic506 digital sensor to implement to gather, and two-way is installed In heat vent end (in Fig. 2 1. and 2. position), another two-way is installed on cold vent end (in Fig. 2 3. and 4. position), with balance Temperature contrast in organ container.TSic506 transmission bag is made up of 1 start bit, 8 data bit and 1 parity check bit, transmission Baud rate is 8000Hz, and 11 megadyne temperature degrees of data are made up of, including high 3 megadyne temperature degrees of data and least-significant byte temperature number 2 transmission bags According to.TSic506 read operation completes in DSP Interruption, i.e. when temperature accumulator count to 1 second, and four tunnel temperature sensing Device starts to read temperature data.When reading temperature data, transmission start bit causes at the I/O pin trailing edge of TMS320F2812 One interruption, and turn to interrupt service routine, comprising a counting circulation inside interrupt routine, counting is incremented by an internal memory every time Position, until TSic506 signal rising edge, this count value i.e. each read cycle.After obtaining read cycle, interrupt clothes Business program waits rear 9 trailing edges (8 bit data, 1 bit parity check), waits that read cycle samples next after each trailing edge Position, finally reads out the digital value Digital_T of each sensor output.Temperature T of actual institute measuring point is:

Wherein, HT=60 is the temperature upper limit of sensor measurement；LT=-10 is limit value at a temperature of sensor measurement； Digital_T is the digital value of sensor output；T is the temperature value of actual institute measuring point.

Seeing Fig. 4 and Fig. 5, describe compressor unit and Ventilating valve unit, it is respectively used to produce and conducting cold-hot wind. The rotating speed of DC frequency-changeable compressor and directional information, evaporating fan and condenser fan is controlled by vaporizer and cold by serial communication The temperature of condenser is conducted to ventilation shaft, controls Ventilating valve unlatching dutycycle simultaneously and is eventually controlled to reach controlled organ container Air quantity.Utilizing SCIATXD and SCIARXD mouth to be connected to three phase variable frequency driven compressor end, DSP main control chip was every output in 1 second Control the rotating speed of compressor, folding and direction state, and the current rotating speed of real-time reception direct current compressor, radiator temperature, bus The states such as voltage, and adjust the information such as compressor rotary speed, Ventilating valve dutycycle in real time according to reading state, to reach accurately to control System.Wherein, drive circuit mainly utilizes pulse length modulation principle, by change output square wave dutycycle make load current power from 0-100% changes, and finally changes compressor rotary speed.Additionally, dsp chip and drive circuit use light-coupled isolation (ISO801, ISO802) protect with implementing circuit.

In a further embodiment, the computing formula of compressor unit organ container acquisition refrigerating capacity (heating capacity) is:

$Q=\[(1-\mathrm{\ξ})\×({\mathrm{\μ}}_0-2{\mathrm{\μ}}_k+{\mathrm{\η\μ}}_k)\×\frac{{\mathrm{vnq}}_{0v}}{3.6\×{10}^6}\](kW/h)$

Wherein, ξ is that ventilation shaft transmits energy loss efficiency, 0≤ξ≤1；μ₀And μ_kIt is respectively cold wind valve and hot-blast valve The unlatching dutycycle of door, 0≤μ₀≤ 1,0≤μ_k≤1；η ≈ 90% is the refrigerating efficiency of compressor；q_0vFor compressor unit volume Refrigerating capacity；N is compressor rotary speed, r/h；V is the every discharge capacity that turns of compressor, cm³/r；Q is that the refrigerating capacity that organ container obtains (heats Amount), do not consider container self-radiating, if Q ＞ 0, vessel temp reduces, Q ＜ 0, and vessel temp raises, Q=0, then temperature keeps Constant.

Above-mentioned each parameter utilizes DSP implement to control, according to refrigerating capacity the most measurable subsequent time temperature:

$T_n=T_c+\frac{3.6\×{10}^6\×Q}{c_0{m}_0+c_1{m}_1}$

Wherein, T_cFor Current Temperatures；T_nFor temperature after predicting 1 second；c₀、c₁It is respectively air & organ perfusion solution specific heat Hold；m₀、m₁It is respectively air and the primer solution quality of the organ that circulates in 1 second in organ container.

Seeing Fig. 6, in a further embodiment, DSP serial communication controls detailed description of the invention and is, host computer communication every 1 Second sends a frame (16 byte), and content comprises control direction, rotary speed information；Driven compressor plate be communication from machine, receive main frame One frame responds immediately to a frame (16 byte), and content comprises compressor current operation state.Main frame receives after machine return state, reads Take the status information comprehensive distinguishing decision-makings such as the voltage of DC frequency-changeable compressor, electric current, rotating speed, heatsink temperature, content of policy decision bag Include: setting and start dwell time interval, current protection, voltage protection and high temperature protection, table 2 is communication frames format list.

Decision-making protection particular content includes: I, compressor power on first and could must run after 20 seconds, the minimum that 2 times start Time interval is 3 minutes, uses intervalometer sum counter to realize；II, when electric current reaches 10A, electric machine frequency and compressor turn Speed no longer raises, and along with electric current raises, its rotating speed actively declines, until closing during 13A；III, (it is higher than less than 20V when voltage Time 28V), implement low pressure (high pressure) protection, when voltage reduces (rising) further, and compressor operating maximum speed progressively declines, Until being automatically switched off during 18V (30V)；IV, when radiator temperature reaches 60 DEG C, compressor rotary speed no longer raises, along with temperature Raising, compressor rotary speed reduces further, until 70 DEG C are automatically switched off.

Table 2 communication frames format list

Seeing Fig. 1, perfusion instrument thermostatic control algorithm as processor, implements algorithm bag with DSP (TMS320F2812) chip Include: based on improving the Temperature fusion unit of Bayesian Estimation and temperature control unit based on fuzzy controller.The former implements Reason is the fault of single temperature sensor TSic506 and environmental disturbances may cause the falseness of certain paths data insincere, Therefore four-way data fusion is become a road temperature data, and reducing potential measurement risk, the latter merges utilize with the temperature obtained Data compare with setting data, draw the control output of compressor, to promote temperature-controlled precision.

In a further embodiment, the work process of the Temperature fusion subelement improving Bayesian Estimation is specific as follows:

Step I, historical data and current data is utilized to calculate the Virtual factor of every road sensing data

$f_j^k=\frac{m^2}{m^2-{\left(max\right(\left|z_j^k-z_j^{k-1}\right|\left)\right)}^2}$

Wherein,For jth sensor at the Virtual factor in k moment,For sensor number (channel number)；K be the moment mark, if full segment data to be divided into n section, then k ∈ 1,2 ..., n；M is self-defined single logical The expection difference that road is maximum；Max () is maximizing function；WithIt is respectively k moment and k-1 moment section jth sensing Device measured temperature data.

Step II, utilize according to Virtual factor and single sensor variance Gauss distribution Bayesian Estimation implement temperature melt Close:

$t_k=\underset{j=1}{\overset{4}{\Σ}}\frac{1}{\mathrm{\δ}\×\[f_j^k\×{\left({\mathrm{\σ}}_j^k\right)}^2\]}{z}_j^k$

Wherein, δ is to simplify the variance merging factor that formula is expressed:

$\mathrm{\δ}=\underset{j=1}{\overset{4}{\Σ}}\frac{1}{f_j^k\×{\left({\mathrm{\σ}}_j^k\right)}^2}$

In formula, t_kFusion value for k moment four tunnel temperature sensor；For jth sensor in the standard deviation in k moment；Jth sensor is at the temperature data in k moment, and a length of N/n, N are total length of data.

If step III single order fused data still cannot eliminate false data error, then adjust repeatedly adjustment m value, implement level Connection merges, and adjustment number of times is considered as port number, utilizes step II fusion formula to melt the variant fusion results secondary again obtained Close.

Seeing Fig. 7 a to Fig. 7 d, test utilizes sampling length 300, four tunnel temperature sensor simulation numbers of sample frequency 1Hz Executing bayesian algorithm convergence analysis factually, four road temperature datas all realize being cooled to the process of 4 DEG C from 20 DEG C, and fault type is arranged For: I road: DATA REASONING error is ± 0.1 DEG C, but it comprises one section and surveys region the most by mistake, is characterized as sensor fault；Ii Road: DATA REASONING error is ± 0.2 DEG C, but it comprises two downward spike and surveys region by mistake, characterizes sensor and surveys temporarily by mistake；The III road: DATA REASONING error is ± 0.4 DEG C, but it comprises two upwards spike survey regions by mistake, characterizes sensor and surveys temporarily by mistake； Iv road: DATA REASONING error is ± 0.8 DEG C, its jittering noise region is relatively big, characterizes the overall situation noise of sensor.Improving In Bayesian Estimation method, the temperature sensor data of Jiang Zhe tetra-tunnel different faults type is respectively divided into 30 sections (n=30), every section 10s data, the expection difference of single temperature sensor value maximum is set as m²=0.1, m²=0.4, m²=0.8 and m²=1 is the most equal Level.

See Fig. 8 a to Fig. 8 c, in tradition Bayesian Estimation curve, derive from the temperature sensor of ii road and ii I road temperature The temperature jitter deviation of degree sensor is inhibited, and after merging, environment noise is obviously reduced that (blend curve is defeated relative to four roads simultaneously Enter curve the mildest), but blend curve still suffers from persistently surveying by mistake, illustrates that fault sensor cannot effectively be known by the method Not；Improve in Bayes's curve, as the less (m of m value²=0.1) time, it is possible to the lasting mistake that effectively suppression sensor fault brings Survey, but more weak for interim peak restrained performance；Otherwise as the relatively big (m of m value²=0.8,1) time, it is possible to suppress interim spike, and More weak to persistently surveying rejection by mistake；Only as the moderate (m of m value²=0.4), time, both could be inhibited simultaneously, and compares biography System Bayesian Estimation, becomes apparent from noise suppressed performance；Cascade improves in Bayesian Fusion curve, by m²=0.1, m²= 0.4, m²=0.8 and m²=1 four road blend curve is as improving the new input of Bayesian Estimation, it can be seen that secondary merges the On the basis of Single cell fusion, error is suppressed further, eliminates persistent error and spike error interference the most simultaneously, and bent Line noise is less.

Seeing Fig. 9, the work process that temperature based on fuzzy (PID) controller controls subelement is concrete For: when temperature deviation relatively big (more than predetermined value), such as,Use fuzzy controller, to improve rapidity and to subtract Little overshoot, wherein temperature deviationFor temperature average in 10s and setting value average deviation；When temperature deviation is less (less than pre- Definite value) time, such asUse fuzzy-adaptation PID control, to reduce the steady-state error of system, improve control accuracy, wherein, Δ T is for measuring temperature and design temperature deviation.

In a further embodiment, the enforcement step of fuzzy controller is as follows:

Step I, Δ u, tri-domains of Δ T and cT are quantified as seven grades, and build equilateral triangle membership function, its In, Δ u is for controlling variable quantity；Δ T is for measuring temperature and design temperature deviation；CT is temperature deviation rate of change；Seven ranking scores Yong not bear big (NB), negative in (NM), negative little (NS), zero (ZE), the least (PS), the center linguistic variable value such as (PM) and honest (PB) Represent；As a example by domain Δ T, to either element, membership function A (Δ T) ∈ [0,1]；Closer to 1, then it belongs to the degree of A more High.

Step II, self-defined fuzzy control rule table, utilize fuzzy Control direct current variable frequency motor rotating speed to use If Δ T&cT, Then Δ u is fuzzy rule.Such as, when Δ T and cT is NM, illustrate that actual speed exceeds rotating speed of target a lot, And rotation speed change makes it deviate more from setting speed, therefore controlled quentity controlled variable Δ u should be set and control output as NM to reduce, describe by this, Structure fuzzy control rule table is as shown in table 3:

Fuzzy control rule table (the Δ u) of table 3 fuzzy controller

Step III, according in I membership function degree of membership maximum principle implement defuzzification, obtain fuzzy control search table, As shown in table 4, and for compressor rotary speed control.

Fuzzy control search table (the Δ u) of table 4 fuzzy controller

Seeing Figure 10, in a further embodiment, the basic thought of fuzzy controller is, first with Fuzzy Control Device processed obtains three regulation parameters of PID regulation, implements PID followed by these three parameter and controls, is embodied as step as follows:

Step I, by Δ T, k_p、k_iAnd k_dFour domains be quantified as 5 grades of-2 ,-1,0,1,2}, and be divided into negative in (NM), bear the linguistic variables such as little (NS), zero (ZE), the least (PS), center (PM), and build equilateral triangle membership function, wherein, k_p、k_iAnd k_dIt is respectively PID controller proportionality coefficient, integral coefficient and differential coefficient；

Step II, self-defined fuzzy control rule table, i.e. by Δ T domain tier definition k_p、k_iAnd k_dDomain grade, such as table 5 Shown in；

The fuzzy control rule table of table 5 fuzzy controller

Step III, according in I membership function degree of membership maximum principle implement defuzzification, obtain fuzzy control search table, As shown in table 6.It can be seen that when design temperature and actual temperature negative sense deviation are bigger, proportionality coefficient k_pWith integral coefficient k_iChoosing Take relatively big, and differential coefficient k_dChoose less, along with negative sense deviation progressively reduces, and start when positivity bias, ratio system Number k_pWith integral coefficient k_iValue is gradually reduced, and differential coefficient k_dValue is gradually increasing.

The fuzzy control search table of table 6 fuzzy controller

Step IV, utilize the ratio obtained in III, integration and differential coefficient to implement PID compressor rotary speed to control:

$u\left(k\right)=k_{p}e\left(k\right)+k_i\underset{j=0}{\overset{k}{\Σ}}e\left(j\right)T+k_d\frac{e\left(k\right)-e(k-1)}{T}$

Wherein, u (k) is controller output, and e (k) and e (k-1) is respectively the k moment and the k-1 moment sets and output Deviation；T is the sampling period；K is sampling instant, k=1,2 ...；J is accumulator register.

Seeing Figure 11 a to Figure 11 c, in dynamic performance testing, test I utilizes room temperature liquid water as primer solution, temperature Degree environmental condition is: 5 DEG C, 5 DEG C, 10 DEG C, 5 DEG C, 0 DEG C, flow conditions is: 40mL/min, is wherein manually set the temperature moment and is 16.7min, 25min, 41.7min, 58.3min and 83.3min, corresponding design temperature is 5 DEG C, 15 DEG C, 10 DEG C, 0 DEG C, 5 DEG C and 10 DEG C, to observe temperature changing process.From Figure 11 a it can be seen that temperature setting changing value (absolute value) is the biggest, initial rate of change The biggest, when beyond certain value (more than 18 DEG C), initial rate of change reaches maximum, now fuzzy control table value be NB or PB, compressor rotary speed is 6000r/min, cooling and warming valve dutycycle 100%, and rate temperature change is 3.4 DEG C/min；Test II, under the temperature environment of 20 DEG C, is 5 DEG C and 10 DEG C at 0min and 10min moment design temperature, and wherein perfusion flow rate environments divides Other 40mL/min, 60mL/min and 80mL/min, observe its temperature variation curve, from Figure 11 b it can be seen that when design temperature be When 5 DEG C, 15～10 DEG C and 10～5 DEG C of transformation periods are 1.2 minutes and 6.2 minutes, and i.e. design temperature is with actual temperature difference more Greatly, rate temperature change is the fastest, this be the cv parameter by fuzzy controller and the ratio of fuzzy controller and differential coefficient certainly Fixed, and when ambient temperature is higher than design temperature, under the biggest situation of flow velocity, temperature reduces the slowest, rises the fastest, this be because of Extraneous temperature interference is introduced for primer solution；Test III more only controls DC frequency-changeable compressor, only control Ventilating valve accounts for The dynamic property of empty ratio and simultaneously both control Three models.From Figure 11 c it can be seen that implement rotating speed control merely with compressor System has delay to temperature control effect, and not only compressor response speed is relatively slow, and ventilation shaft transmission requires time for；Merely with valve Door dutycycle implement temperature control, although response speed quickly, but stable state effect is the best, it is impossible to reach set temperature control essence Degree, this is due to the bigger reason of the jump step-length of dutycycle, and Valve controlling combines with compressor control, to a certain degree Upper equilibrium response speed and stable state accuracy.

In steady-state behaviour is tested, test is compared under 40mL/min, 60mL/min, 80mL/min flow conditions, sets temperature Degree is 5 DEG C, steady-state offset under the conditions of 15 DEG C, as shown in table 7.It can be seen that irrigate flow velocity steady-state offset amount and setting in table It is worth unrelated, but relevant with perfusion flow velocity, and flow velocity the biggest temperature temperature-controlled precision is the lowest, and when irrigating flow velocity and being 40mL/min, it is by mistake Difference standard deviation promotes 53.49% than 80mL/min.

Table 7 steady-state offset list

Claims (9)

1. the Temperature fusion of isolated organ perfusion instrument and control system, it is characterised in that include main control unit, with described master Four-way temperature collecting cell, compressor unit and the Ventilating valve unit of control unit electrical connection, and temperature control unit；Institute State temperature control unit to include based on improving the Temperature fusion subelement of Bayesian Estimation and temperature based on fuzzy controller Control subelement.

The Temperature fusion of isolated organ the most according to claim 1 perfusion instrument and control system, it is characterised in that described four Having two-way to be installed on heat vent end in the collection terminal of channel temperature collecting unit, additionally two-way is installed on cold vent end.

The Temperature fusion of isolated organ the most according to claim 1 perfusion instrument and control system, it is characterised in that described pressure Contracting machine unit and Ventilating valve unit are respectively used to produce and conducting cold-hot wind；Compressor unit is controlled by DSP serial communication Rotating speed and directional information, evaporating fan and condenser fan the temperature of vaporizer and condenser is conducted to ventilation shaft, simultaneously DSP controls Ventilating valve unit unlatching dutycycle and is eventually controlled to reach the air quantity of controlled organ container.

The Temperature fusion of isolated organ the most according to claim 3 perfusion instrument and control method, it is characterised in that described device Official's container obtains the computing formula of refrigerating capacity or heating capacity:

$Q=\[(1-\mathrm{\ξ})\×({\mathrm{\μ}}_0-2{\mathrm{\μ}}_k+{\mathrm{\η\μ}}_k)\×\frac{{\mathrm{vnq}}_{0v}}{3.6\×{10}^6}\];$

Wherein, ξ is that ventilation shaft transmits energy loss efficiency, 0≤ξ≤1；μ₀And μ_kIt is respectively cold wind valve and Hot-blast valve Open dutycycle, 0≤μ₀≤ 1,0≤μ_k≤1；η is the refrigerating efficiency of compressor；q_0vFor compressor refrigerating effect per unit swept volume；N is Compressor rotary speed；V is that compressor is every turns discharge capacity；Q is refrigerating capacity or the heating capacity of organ container acquisition, does not consider that container self dissipates Heat, if Q>0, vessel temp reduces, Q<0, and vessel temp raises, Q=0, and vessel temp keeps constant；

Above-mentioned each parameter utilizes DSP implement to control, according to refrigerating capacity prediction subsequent time temperature:

$T_n=T_c+\frac{3.6\&times;{10}^6\&times;Q}{c_0{m}_0+c_1{m}_1}$

Wherein, T_cFor Current Temperatures；T_nFor temperature after predicting 1 second；c₀、c₁It is respectively air & organ perfusion solution specific heat capacity；m₀、 m₁It is respectively air quality and the primer solution quality of the organ that circulates in 1 second in organ container.

The Temperature fusion of isolated organ the most according to claim 3 perfusion instrument and control system, it is characterised in that pass through The detailed process of rotating speed and directional information that DSP serial communication controls compressor unit is: DSP is host computer communication, within every 1 second, sends One frame, content comprises control direction, rotary speed information；Driven compressor plate be communication from machine, receive main frame one frame and respond immediately to one Frame, content comprises compressor current operation state；Host computer communication receives after machine return state, makes specific aim and adjusts or report to the police Measure.

The Temperature fusion of isolated organ the most according to claim 1 perfusion instrument and control system, it is characterised in that described base Specific as follows in the work process of the Temperature fusion subelement improving Bayesian Estimation:

Step 6A, historical data and current data is utilized to calculate the Virtual factor of every road sensing data

$f_j^k=\frac{m^2}{m^2-{\left(max\right(|z_j^k-z_j^{k-1}|\left)\right)}^2};$

Wherein,For jth sensor at the Virtual factor in k moment, { 1,2,3,4} is sensor number (channel number) to j ∈； K be the moment mark, if full segment data to be divided into n section, then k ∈ 1,2 ..., n}；M is the expection that self-defined single passage is maximum Difference；Max () is maximizing function；WithIt is respectively k moment and k-1 moment section jth sensor measured temperature number According to；

Step 6B, utilize according to Virtual factor and single sensor variance Gauss distribution Bayesian Estimation implement Temperature fusion:

$t_k={\mathrm{\&Sigma;}}_{j=1}^4\frac{1}{\mathrm{\&delta;}\&times;\&lsqb;f_j^k\&times;{\left({\mathrm{\&sigma;}}_j^k\right)}^2\&rsqb;}{z}_j^k;$

Wherein, δ is to simplify the variance merging factor that formula is expressed:

$\mathrm{\&delta;}={\mathrm{\&Sigma;}}_{j=1}^4\frac{1}{f_j^k\&times;{\left({\mathrm{\&sigma;}}_j^k\right)}^2};$

In formula, t_kFusion value for k moment four tunnel temperature sensor；For jth sensor in the standard deviation in k moment；Jth Individual sensor is at the temperature data in k moment, and a length of N/n, N are total length of data；

If step 6C single order fused data still cannot eliminate false data error, then adjust m value, implement cascade fusion, will adjust Number of times is considered as port number, utilizes the step 6B fusion formula variant fusion results secondary again to obtaining to merge.

The Temperature fusion of isolated organ the most according to claim 1 perfusion instrument and control system, it is characterised in that described base Temperature in fuzzy controller controls the work process of subelement particularly as follows: when temperature deviation is more than predetermined value, use mould Fuzzy controllers carries out temperature regulation, to improve rapidity and to reduce overshoot；When temperature deviation is less than predetermined value, use fuzzy PID controls to carry out temperature adjusting, to reduce the steady-state error of system, improves control accuracy.

The Temperature fusion of isolated organ the most according to claim 7 perfusion instrument and control system, it is characterised in that use mould Fuzzy controllers carries out thermoregulator specifically comprising the following steps that

Step 8A, Δ u, tri-domains of Δ T and cT are quantified as seven grades, and build equilateral triangle membership function, wherein, Δ U is for controlling variable quantity；Δ T is for measuring temperature and design temperature deviation；CT is temperature deviation rate of change；

Step 8B, self-defined fuzzy control rule table: by Δ T and cT domain tier definition Δ u domain grade；

Step 8C, according in step 8A membership function degree of membership maximum principle implement defuzzification, obtain fuzzy control search table, And control for compressor rotary speed.

The Temperature fusion of isolated organ the most according to claim 7 perfusion instrument and control system, it is characterised in that use mould The step that paste PID controller carries out temperature adjusting is specific as follows:

Step 9A, by Δ T, k_p、k_iAnd k_dFour domains are quantified as five grades, and build equilateral triangle membership function, wherein, Δ T is for measuring temperature and design temperature deviation；k_p、k_iAnd k_dIt is respectively PID controller proportionality coefficient, integral coefficient and differential system Number；

Step 9B, self-defined fuzzy control rule table, i.e. by Δ T domain tier definition k_p、k_iAnd k_dDomain grade；

Step 9C, according in step 9A membership function degree of membership maximum principle implement defuzzification, obtain fuzzy control search table；

Step 9D, utilize the ratio obtained in step 9C, integration and differential coefficient to implement PID compressor rotary speed to control:

$u\left(k\right)=k_{p}e\left(k\right)+k_i\underset{j=0}{\overset{k}{\&Sigma;}}e\left(j\right)T+k_d\frac{e\left(k\right)-e(k-1)}{T}$

Wherein, u (k) is controller output, and e (k) and e (k-1) are respectively the k moment and that the k-1 moment sets with output is inclined Difference；T is the sampling period；K is sampling instant, k=1,2 ...；J is accumulator register.