CN118068895B - Venturi valve-based wind speed control system for air supplementing molded surface in fume hood - Google Patents
Venturi valve-based wind speed control system for air supplementing molded surface in fume hood Download PDFInfo
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- 239000003517 fume Substances 0.000 title claims description 32
- 230000001502 supplementing effect Effects 0.000 title description 12
- 238000009423 ventilation Methods 0.000 claims abstract description 146
- 238000004458 analytical method Methods 0.000 claims abstract description 22
- 238000012544 monitoring process Methods 0.000 claims abstract description 14
- 238000000034 method Methods 0.000 claims description 26
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- 238000004364 calculation method Methods 0.000 claims description 19
- 239000013598 vector Substances 0.000 claims description 18
- 230000000694 effects Effects 0.000 claims description 12
- 239000011159 matrix material Substances 0.000 claims description 12
- 238000005516 engineering process Methods 0.000 claims description 6
- 230000008569 process Effects 0.000 claims description 6
- 238000005065 mining Methods 0.000 claims description 3
- 238000003058 natural language processing Methods 0.000 claims description 3
- 230000000717 retained effect Effects 0.000 claims 1
- 238000006073 displacement reaction Methods 0.000 abstract description 5
- 230000009286 beneficial effect Effects 0.000 abstract description 2
- 238000011217 control strategy Methods 0.000 description 32
- 238000005286 illumination Methods 0.000 description 9
- 230000001105 regulatory effect Effects 0.000 description 7
- 238000000342 Monte Carlo simulation Methods 0.000 description 5
- 239000008186 active pharmaceutical agent Substances 0.000 description 5
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- 230000001276 controlling effect Effects 0.000 description 1
- 230000009193 crawling Effects 0.000 description 1
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Classifications
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B08—CLEANING
- B08B—CLEANING IN GENERAL; PREVENTION OF FOULING IN GENERAL
- B08B15/00—Preventing escape of dirt or fumes from the area where they are produced; Collecting or removing dirt or fumes from that area
- B08B15/02—Preventing escape of dirt or fumes from the area where they are produced; Collecting or removing dirt or fumes from that area using chambers or hoods covering the area
- B08B15/023—Fume cabinets or cupboards, e.g. for laboratories
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D27/00—Simultaneous control of variables covered by two or more of main groups G05D1/00 - G05D25/00
- G05D27/02—Simultaneous control of variables covered by two or more of main groups G05D1/00 - G05D25/00 characterised by the use of electric means
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B30/00—Energy efficient heating, ventilation or air conditioning [HVAC]
- Y02B30/70—Efficient control or regulation technologies, e.g. for control of refrigerant flow, motor or heating
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Abstract
The invention relates to the field of indoor ventilation, in particular to an indoor ventilation face wind speed control system based on a Venturi valve, which aims to solve the technical problems that the indoor ventilation face wind speed control system comprises a variable-air-quantity Venturi valve, a sensor system and a control system, wherein the variable-air-quantity Venturi valve is arranged on a Venturi ventilation cabinet and is connected with a ventilation pipeline, the sensor system comprises a door height sensor, a differential pressure sensor, a temperature sensor, a humidity sensor, a displacement sensor, a Venturi flowmeter and a regional existence sensor, the control system comprises a data acquisition module, an application scene analysis module, an artificial parameter regulation analysis module, an operation parameter monitoring module and an intelligent generation module, the sensor system is used for setting a sensor in a region needing ventilation according to the size and layout of a room, and the control system is used for calculating the minimum ventilation quantity of the room at the current moment, the ventilation quantity of the ventilation system and the surface wind speed of the ventilation system according to data measured by the sensor. The technical scheme has the beneficial effects that the energy efficiency is effectively reduced, and the safety of the environment is improved.
Description
Technical Field
The invention relates to the field of indoor ventilation, in particular to a ventilation cabinet inner air supplementing profile wind speed control system based on a Venturi valve.
Background
For the built-in fume hood, the air supply opening is arranged in the fume hood, and most of the air supply opening is arranged on the forehead of the fume hood. In this way, the air flow flows from the inside to the bottom. The comfort of the experimenter is much better than the external air supply, but it is easy to blow toxic and harmful gases out of the window and escape. Therefore, although the internal air-supplementing type fume hood effectively reduces energy efficiency, better technical means are required for ensuring the safety. Therefore, the invention provides the ventilation cabinet internal wind type control system based on the Venturi valve, which can prevent toxic substances from escaping when effectively reducing the energy efficiency.
Disclosure of Invention
The technical problem to be solved by the technical scheme is that modern laboratories are more and more standard, laboratory requirements are higher and higher, the fume hood is used as an important part of the laboratory, energy consumption is quite large, and accordingly, the application of the air supplementing fume hood is increased. The design of the air-supplementing type ventilating cabinet has the main advantages of saving energy, greatly reducing the air exhaust quantity of a laboratory, reducing the energy consumption of the laboratory, along with high comfort level and relatively easy control of the humidity of the laboratory room temperature. However, when the relationship among the air supply, the air inlet of the window and the air exhaust is changed in a certain direction, the coordination is not easy. And then the negative pressure protection function disorder of the fume hood core is caused, and toxic and harmful gas is leaked instead. For example, the front window of the fume hood is opened, the air exhaust requirement is increased, at this time, if the air supply increment is larger than the air exhaust increment, the negative pressure is easy to be disturbed, positive pressure is formed in the fume hood, and operators are exposed in danger. The venturi valve is air quantity regulating and controlling equipment based on venturi effect, and is a high-speed response high-precision air quantity control valve which is independent of pressure and has high regulating ratio. The venturi valve is applied to a ventilation system, so that pollutants harmful to health can be rapidly and effectively removed, and the construction of the ventilation control system is also the most important ring for guaranteeing the safety of a risk environment.
The Venturi valve-based ventilation cabinet inner air supplementing profile wind speed control system comprises a variable air quantity Venturi valve, a sensor system and a control system, wherein the variable Venturi valve is arranged on a Venturi ventilation cabinet and is connected with a ventilation pipeline, a control power supply is arranged on the variable air quantity Venturi valve, a guide plate, a window and an air supplementing wing are arranged on the Venturi ventilation cabinet, the guide plate is connected with the air supplementing wing, a grid is arranged at the joint of the air supplementing wing and the guide plate, an outlet is arranged on the guide plate, air flow at the outlet and the variable air quantity Venturi valve form circulation, so that the linear air flow entering the ventilation cabinet can effectively prevent disturbance of air flow, and the window regulating valve is arranged on the ventilation cabinet and is used for regulating the height of the window to form inner air circulation in the ventilation cabinet, thereby avoiding harmful substances from overflowing the ventilation cabinet.
The sensor system comprises a door height sensor, a differential pressure sensor, a temperature sensor, a humidity sensor, a displacement sensor, a venturi flowmeter and an area existence sensor, wherein the sensor can be used for setting the sensor according to the size and layout of a room in an area needing ventilation.
The control system comprises a data acquisition module, an application scene analysis module, an artificial parameter regulation and control analysis module, an operation parameter monitoring module and an intelligent generation module, wherein the sensor system is used for setting a sensor according to the size and layout of a room in a region needing ventilation, and the control system calculates the minimum ventilation volume of the room, the ventilation volume of the ventilation system and the surface wind speed of the ventilation system at the current moment according to data measured by the sensor.
The calculation formula of the minimum ventilation volume Vmin of the room is Vmin= (Fl x Fk x Fh)/hn, wherein Fl represents the length of the room, fk represents the width of the room, fh represents the height of the room, and hn represents the ventilation times;
the calculation formula of the air supply quantity Ls of the ventilation system is shown as ls=Vmin/wt, wherein Vmin represents the minimum ventilation quantity of a room, and wt represents ventilation time;
The calculation formula of the exhaust air quantity Lp of the ventilation system is that lp=vmin×85%, wherein Vmin represents the minimum ventilation quantity of the room;
The calculation formula of the window surface wind speed of the ventilation system is as follows: where Ls denotes the air supply of the ventilation system, tl denotes the length of the fume hood, tk denotes the width of the fume hood.
The system comprises a data acquisition module, an application scene analysis module, an artificial parameter regulation analysis module, an operation parameter monitoring module and a ventilation system, wherein the data acquisition module comprises a sensing system interface module and an API interface module, the sensing system interface module is used for acquiring and receiving data measured by a sensor, the API interface module is used for receiving local weather forecast related information, the application scene analysis module is used for extracting scene variables from the preprocessed application scene related information, calculating target variables according to the preprocessed data, analyzing and evaluating the scene variables and the target variables, the artificial parameter regulation analysis module is used for generating different parameter combination schemes according to parameters manually set and modified by a user and selecting a parameter combination scheme with optimal performance, and the operation parameter monitoring module is used for monitoring operation parameters of the ventilation system in real time, calculating system performance indexes and providing real-time feedback.
Further, the intelligent generation module comprises a control strategy intelligent generation module and a system autonomous regulation processing module, wherein the control strategy intelligent generation module divides the working mode of the ventilation system into an energy-saving mode and a conventional mode based on the regional existence data and automatically generates a control strategy by combining an optimally-expressed parameter combination scheme, and the system autonomous regulation processing module is used for receiving the data transmitted by the control strategy intelligent generation module and performing autonomous regulation based on the control strategy so as to realize intelligent control of the ventilation system.
Further, the specific processing procedure of the application scene analysis module is as follows:
a1, extracting scene information from the preprocessed application scene related information through a natural language processing technology and a text mining technology;
a2, further analyzing the scene information to obtain scene variables;
And A3, classifying and marking the scene keywords to obtain scene categories Ni, i=1, 2,3. Classifying scene variables containing scene keywords into belonging scene categories according to the corresponding scene keywords;
A4, calculating the minimum ventilation volume of the room, the air supply volume of the ventilation system, the air exhaust volume of the ventilation system and the window surface wind speed of the ventilation system at the current moment according to the data measured by the sensor;
a5, recording the minimum ventilation volume of the room within 24 hours, the air supply volume of the ventilation system, the air exhaust volume of the ventilation system and the window surface air velocity of the ventilation system as target variables;
a6, calculating the mean value and vector representation of each scene variable and each target variable for each scene variable and each target variable;
A7, calculating covariance matrixes between each scene variable and each target variable;
a8, calculating bias correlation coefficients for each group of covariance matrixes
A9, the bias correlation coefficientAnd a preset bias-correlation coefficient threshold valueJudging and comparing, ifA strong interaction between the scene variable and the target variable is indicated, the scene variable is preserved, and otherwise, the interaction between the scene variable and the target variable is indicated to be weak, and the scene variable is discarded.
Further, for each scene variable and each target variable, calculating the mean value and vector representation of each scene variable and each target variable, wherein the processing procedure is as follows:
a61, calculating the mean value mu x of each scene variable in the scene category, the mean value sigma x of each target variable, Where muρ represents the data in each scene variable,Representing the data in each target variable, ρ representing the data amount for each scene variable,Data representing each target variable, wherein mu xgj, mu xyj, mu xzj, mu xkj, mu xwj, mu xsj, mu xqj, mu xrj, mu xpj, mu xdj, mu xtj, mu xmj and mu xfj represent the average value of illumination, noise, vibration, area, temperature range, humidity range, air flow speed, space number, activity frequency of personnel in space, activity intensity, size of a fume hood, door height and area to be covered, and sigma a, sigma b, sigma c and sigma d represent the average value of minimum ventilation volume of a room, air volume of a ventilation system and air speed of a window surface of the ventilation system;
a62, taking the mean value of each scene variable as the vector representation of the scene variable The mean value of each target variable is used as the vector representation of the target variable
Further, the covariance matrix between each scene variable and each target variable is calculated, and the calculation process is as follows:
A71 for each scene variable Each target variableCalculating covariance between them Where E [ ] represents an expected value,AndVector representations of the scene variable and the target variable, respectively, μx and σx representing the average of the scene variable and the target variable, respectively;
and A72, filling the calculated covariance into the corresponding position of the covariance matrix to obtain the covariance matrix.
Further, the partial correlation coefficient is calculated by the following formula: Wherein the method comprises the steps of Representing scene variablesAnd a target variableThe covariance between the two is calculated by the method,Representing scene variablesIs a function of the variance of (a),Representing target variablesIs a variance of (c).
Further, the specific processing procedure of the control strategy intelligent generation module is as follows:
The method comprises the steps of C1, dividing the working mode of the ventilation system into an energy-saving mode and a conventional mode based on the region existence data, namely, for the condition that a person exists, recording the working mode of the ventilation system as the conventional mode, and for the condition that the person does not exist, recording the working mode of the ventilation system as the energy-saving mode;
Under the energy-saving mode, generating an energy-saving control strategy by adopting a Monte Carlo method based on reinforcement learning by taking the lower limit of the value range of a scene variable as the current operation parameter according to the parameter combination scheme of the optimal performance;
And C3, in a conventional mode, detecting the positions of the personnel according to the parameter combination scheme with optimal performance, calculating the personnel concentration according to the positions of the personnel, and calculating the required wind speed of the ventilation system according to the personnel concentration to generate a conventional control strategy, wherein the processing procedure is as follows:
c31, detecting the position of a person by using a sensor, and counting the number of people in a unit area;
C32, calculating the personnel concentration RMD according to the number of people in the unit area and the area of the area, wherein RS psi represents the number of people in the ith unit area, and MK represents the area of the area;
c33, calculating the required wind speed VF of the ventilation system according to the personnel density RMD and the air duct sectional area SJ of the ventilation system, wherein W represents a correction coefficient;
And C34, combining the parameter combination scheme with optimal performance and the required wind speed of the ventilation system, and generating a conventional control strategy by adopting a Monte Carlo method based on reinforcement learning.
The beneficial effects after this technical scheme improves are:
the intelligent control of the ventilation system is realized by combining the design of the variable Venturi valve and the ventilation cabinet and cooperating with the cooperation of the sensor and each module of the control system, the accuracy, the adaptability and the flexibility of the ventilation control are improved, and the requirements of different application scenes can be met.
And the scene variable and the target variable are analyzed and evaluated through the application scene analysis module, data support is provided, the requirements of complex application scenes are met, and the accuracy and reliability of data processing are improved.
The intelligent ventilation system comprises a control strategy intelligent generation module, a system autonomous regulation processing module and a ventilation system intelligent control module, wherein the control strategy intelligent generation module is used for dividing working modes according to regional existence data, automatically generating a control strategy by combining with an optimally-represented parameter combination scheme, improving the intelligent degree of the ventilation system, and realizing the intelligent control of the ventilation system by autonomous regulation according to the control strategy through the system autonomous regulation processing module.
Drawings
FIG. 1 is a schematic diagram of the overall system of the present invention.
Fig. 2 is a block diagram of a control system of the present invention.
Fig. 3 is a flowchart of a method of the application scene analysis module according to the present invention.
Fig. 4 is a block diagram of a venturi fume hood of the present invention.
FIG. 5 is a block diagram of a baffle and a wing of the present invention.
Detailed Description
In order that those skilled in the art may better understand the technical solutions of the present invention, the following detailed description of the present invention with reference to the accompanying drawings is provided for exemplary and explanatory purposes only and should not be construed as limiting the scope of the present invention.
A wind speed control system of a wind supplementing profile in a fume hood based on a Venturi valve comprises a variable air quantity Venturi valve 1, a sensor system 3 and a control system 4, wherein the variable Venturi valve 1 is installed on a Venturi fume hood 2 and is connected with a ventilation pipeline 10, a control power supply 11 is arranged on the variable air quantity Venturi valve 1, a guide plate 24, a window 21 and a wind supplementing wing 23 are arranged on the Venturi fume hood 2, the guide plate 24 is connected with the wind supplementing wing 23, a grid is arranged at the joint of the wind supplementing wing 23 and the guide plate 24, an outlet is arranged on the guide plate 24, airflow at the outlet and the variable Venturi valve 1 form circulation, and a window regulating valve 22 is arranged on the fume hood 2 and used for regulating the height of the window 21. The design makes the linear air flow entering the fume hood 2 form circulation to effectively prevent the turbulence of the air flow and form internal air circulation in the fume hood, thereby avoiding harmful substances from overflowing the fume hood 2.
The sensor system comprises a door height sensor, a differential pressure sensor, a temperature sensor, a humidity sensor, a displacement sensor, a venturi flowmeter and an area existence sensor, wherein the sensor can be used for setting the sensor according to the size and layout of a room in an area needing ventilation.
The control system comprises a data acquisition module, an application scene analysis module, an artificial parameter regulation and control analysis module, an operation parameter monitoring module and an intelligent generation module, and calculates the minimum ventilation volume of a room, the air supply volume of the ventilation system, the air exhaust volume of the ventilation system and the surface air speed of the ventilation system at the current moment according to data measured by the sensor.
The intelligent generation module comprises a control strategy intelligent generation module and a system autonomous regulation processing module, wherein the control strategy intelligent generation module divides the working mode of the ventilation system into an energy-saving mode and a conventional mode based on the regional existence data and automatically generates a control strategy by combining an optimally-expressed parameter combination scheme, and the system autonomous regulation processing module is used for receiving the data transmitted by the control strategy intelligent generation module and performing autonomous regulation based on the control strategy so as to realize intelligent control of the ventilation system.
The data acquisition module comprises a sensing system interface module and an API interface module, wherein the sensing system interface module is used for acquiring and receiving data measured by a sensor, the API interface module is used for receiving local weather forecast related information, the application scene analysis module is used for extracting scene variables from the preprocessed application scene related information, calculating target variables according to the preprocessed data and analyzing and evaluating the scene variables and the target variables, the artificial parameter regulation analysis module is used for generating different parameter combination schemes according to parameters manually set and modified by a user and selecting a parameter combination scheme with optimal performance, and the operation parameter monitoring module is used for monitoring operation parameters of the ventilation system in real time, calculating system performance indexes and providing real-time feedback.
The variable air volume venturi valve is manufactured by adopting a venturi principle, an external push rod type actuator drives an internal valve rod to linearly move, an internal cone structure changes the opening area of the valve along with the movement of the valve rod, the venturi valve has mechanical pressure independence, a spring in the cone structure can be automatically adjusted according to pressure change in a pipeline, the stable and unchanged air volume of the valve is ensured, and the specific structural principle is shown in patent number 201520385834.9 of my patent.
The system comprises a door height sensor, a pressure difference sensor, a temperature sensor, a humidity sensor, a displacement sensor, a venturi flowmeter and a region existence sensor, wherein the door height sensor is used for detecting the height of a window so as to automatically open or close a ventilation system when needed, the pressure difference sensor is used for monitoring the pressure difference between different regions in the ventilation system so as to ensure that air flows normally, the temperature sensor is used for measuring the indoor temperature so as to adjust the operation speed of the ventilation system when needed, the humidity sensor is used for measuring the indoor humidity so as to adjust the operation speed of the ventilation system when needed, the displacement sensor is used for detecting the opening of the venturi valve, the venturi flowmeter is used for measuring the pressure difference of the venturi valve, and the region existence sensor is used for detecting whether personnel exist in a specific region.
The data acquisition module is used for acquiring data measured by the sensor, acquiring application scene related information by crawling related websites and receiving external API data, wherein the data comprises room length, width, height, ventilation times, ventilation time, ventilation cabinet length, ventilation cabinet width, temperature data, humidity data, opening of a venturi valve, pressure difference of the venturi valve and regional existence data, and the regional existence data specifically refers to existence conditions of personnel in a region needing ventilation, including existence conditions of personnel and existence conditions of no personnel.
The application scene analysis module is used for extracting scene variables from the preprocessed application scene related information, calculating target variables according to the preprocessed data, and analyzing and evaluating the scene variables and the target variables;
the implementation needs to specifically explain that the specific processing procedure of the application scene analysis module is as follows:
a1, extracting scene information from the preprocessed application scene related information through a natural language processing technology and a text mining technology, wherein the scene information comprises scene keywords, environmental conditions, space structures, temperature control, humidity adjustment, air flow management, personnel number, activity intensity, specifications of a fume hood, door height and a range to be covered by an area, and the scene application keywords comprise, but are not limited to, a physicochemical laboratory, a biosafety laboratory, an animal raising room and the like;
A2, further analyzing scene information to obtain scene variables, wherein the scene variables comprise illumination, noise, vibration, area, temperature range, humidity range, air flow speed, space number, activity frequency of personnel in space, activity intensity, size of a ventilation cabinet, door height and area to be covered;
And A3, classifying and marking the scene keywords to obtain scene categories Ni, i=1, 2,3. Scene variables containing scene keywords are classified into belonging scene categories according to the corresponding scene keywords, i.e., ni= { xgj, xyj, xzj, xkj, xwj, xsj, xqj, xrj, xpj, xdj, xtj, xmj, xfj }; whereas xgj＝{xg1,xg2,xg3,……,xgm},xyj＝{xy1,xy2,xy3,……,xym},xzj＝{xz1,xz2,xz3,……,xzm},xkj＝{xk1,xk2,xk3,……,xkm},xwj＝{xw1,xw2,xw3,……,xwm},xsj＝{xs1,xs2,xs3,……,xsm},xqj＝{xq1,xq2,xq3,……,xqm},xrj＝{xr1,xr2,xr3,……,xrm},xpj＝{xp1,xp2,xp3,……,xpm},xdj＝{xd1,xd2,xd3,……,xdm},xtj＝{xt1,xt2,xt3,……,xtm},xfj＝{xf1,xf2,xf3,……,xfm}, wherein xgj represents the illumination data set contained in the scene category Ni, xyj represents the noise data set contained in the scene category Ni, xzj represents the vibration data set contained in the scene category Ni, xkj represents the area data set contained in the scene category Ni, xwj represents the temperature range data set contained in the scene category Ni, xsj represents the humidity range data set contained in the scene category Ni, xqj represents the airflow speed data set contained in the scene category Ni, xrj represents the space person number set contained in the scene category Ni, xpj represents the activity frequency data set in space of the person contained in the scene category Ni, xdj represents the activity intensity data set in space of the person contained in the scene category Ni, xtj represents the fume hood size data set contained in the scene category Ni, xmj represents the door height data set contained in the scene category Ni, and 2 represents the area data set to be covered in the scene category Ni;
A4, calculating the minimum ventilation volume of the room, the air supply volume of the ventilation system, the air exhaust volume of the ventilation system and the window surface wind speed of the ventilation system at the current moment according to the data measured by the sensor;
The calculation formula of the minimum ventilation volume Vmin of the room is Vmin= (Fl multiplied by Fk multiplied by Fh)/hn, wherein Fl represents the length of the room, fk represents the width of the room, fh represents the height of the room, and hn represents the ventilation times;
The calculation formula of the air supply quantity Ls of the ventilation system is that ls=vmin/wt, wherein Vmin represents the minimum ventilation quantity of a room, and wt represents ventilation time;
the calculation formula of the exhaust air quantity Lp of the ventilation system is that lp=vmin×85%, wherein Vmin represents the minimum ventilation quantity of a room;
the calculation formula of window surface wind speed of the ventilation system is as follows: Wherein Ls represents the air supply quantity of the ventilation system, tl represents the length of the ventilation cabinet, and Tk represents the width of the ventilation cabinet;
a5, recording the minimum ventilation volume of the room within 24 hours, the air supply volume of the ventilation system, the air exhaust volume of the ventilation system and the window surface air velocity of the ventilation system as target variables;
a6, calculating the average value and the vector representation of each scene variable and each target variable for each scene variable and each target variable, wherein the processing procedure is as follows:
a61, calculating the mean value mu x of each scene variable in the scene category, the mean value sigma x of each target variable, Where muρ represents the data in each scene variable,Representing the data in each target variable, ρ representing the data amount for each scene variable,Data representing each target variable, wherein mu xgj, mu xyj, mu xzj, mu xkj, mu xwj, mu xsj, mu xqj, mu xrj, mu xpj, mu xdj, mu xtj, mu xmj and mu xfj represent the average value of illumination, noise, vibration, area, temperature range, humidity range, air flow speed, space number, activity frequency of personnel in space, activity intensity, size of a fume hood, door height and area to be covered, and sigma a, sigma b, sigma c and sigma d represent the average value of minimum ventilation volume of a room, air volume of a ventilation system and air speed of a window surface of the ventilation system;
It should be noted that, when the average value of each scene variable is the situation that the multi-scene keyword is used for obtaining the application scene from the network, and after the classification in the step A3, one scene keyword corresponds to a plurality of category scene variables, and one scene variable corresponds to a plurality of data, for example, the illumination in the physicochemical laboratory, the biosafety laboratory and the animal feeding room contains a plurality of illumination data, and the illumination in the physicochemical laboratory, the biosafety laboratory and the animal feeding room contains a plurality of illumination data, so the average value of each scene variable is the average value of the corresponding data set; the average value of each target variable refers to the average value of each target variable data at each time within 24 hours, for example, the minimum ventilation of a room at each time within 24 hours is calculated and recorded as a minimum ventilation set of the room, the average value of the minimum ventilation set of the room is calculated as the average value of one target variable;
a62, taking the mean value of each scene variable as the vector representation of the scene variable The mean value of each target variable is used as the vector representation of the target variableFor example, the illumination data may have a mean value mu xgj, which may be represented as a vector of length T [ mu Xg1, mu Xg2,.. The.A. and. Mu. xgT ]. The mean vector is a vector containing a plurality of mean values, each element corresponding to a mean value of a variable, describing the central location of each scene variable in the dataset;
A7, calculating a covariance matrix between each scene variable and each target variable, wherein the covariance matrix is a symmetrical matrix, each element represents covariance between the scene variable and the target variable, and the calculation process is as follows:
A71 for each scene variable Each target variableCalculating covariance between them Where E [ ] represents an expected value,AndVector representations of the scene variable and the target variable, respectively, μx and σx representing the average of the scene variable and the target variable, respectively;
a72, filling the calculated covariance into the corresponding position of the covariance matrix to obtain the covariance matrix;
a8, calculating bias correlation coefficients for each group of covariance matrixes The partial correlation coefficient is calculated by the following formula: Wherein the method comprises the steps of Representing scene variablesAnd a target variableThe covariance between the two is calculated by the method,Representing scene variablesIs a function of the variance of (a),Representing target variablesThe bias correlation coefficient refers to a correlation measure between a scene variable and a target variable under the influence of other variables, and interference of the other variables can be eliminated by calculating the bias correlation coefficient, so that the degree of correlation between the two variables can be estimated more accurately;
A9, the bias correlation coefficient And a preset bias-correlation coefficient threshold valueJudging and comparing, ifIndicating that there is a strong interaction between the scene variable and the target variable, retaining the scene variable, otherwise indicating that there is a weak interaction between the scene variable and the target variable, discarding the scene variable, wherein the predetermined bias-correlation coefficient threshold valueCan be set specifically according to specific conditions, and the embodiment does not specifically limit specific data;
the artificial parameter regulation analysis module is used for generating different parameter combination schemes according to parameters manually set and modified by a user, calculating a target predicted value according to parameter combinations in the parameter combination schemes, and selecting a parameter combination scheme with optimal performance according to the target variable predicted value of each scheme and a target variable expected value of the user, wherein the parameter combination scheme comprises different value ranges and weights of different scene variables;
the implementation needs to be specifically explained, the specific processing procedure of the artificial parameter regulation analysis module is as follows:
B1, acquiring parameters manually set and modified by a user, wherein the parameters specifically refer to a value range, weight, expected value of a target variable and a time range of a target predicted value of a scene variable which are reserved by the user, wherein the time range of the target predicted value specifically refers to a certain reference time range which is set by the user and is used for predicting the value of the target variable at a certain moment in the future, and the parameter manually set and modified by the user can help to determine which time period of historical data needs to be used for prediction;
B2, generating cs different parameter combination schemes by using a parameter optimization algorithm according to parameters provided by a user, wherein each scheme comprises a group of specific parameter combinations, and the parameter optimization algorithm belongs to the prior art means, so that the embodiment does not make specific description;
And B3, for each parameter combination scheme, calculating the value of a target variable at a certain moment in the future, namely a target predicted value according to the parameter combination in the scheme, wherein the processing procedure is as follows:
b31, collecting historical data, including parameter values in each parameter combination scheme and corresponding target variable values, and dividing a data set into a training set and a testing set;
B32, selecting a numerical characteristic representation method, and converting parameter values in the parameter combination scheme into characteristic vectors which can be processed by the machine learning model;
B33, selecting a regression model, training the model by using a training set, and adjusting parameters of the model to enable the model to fit data better;
b34, evaluating the trained model by using a test set, calculating an error between a predicted result and a true value, and optimizing the model according to the evaluation result, wherein the method comprises the steps of adjusting super parameters and increasing training data quantity;
b35, predicting a target variable at a certain moment in the future by using the trained model to obtain a target variable predicted value phi of the parameter combination scheme;
B4, calculating the difference value epsilon between the target variable predicted value and the target variable expected value phi of each parameter combination scheme, Wherein φI represents the target variable predictive value of the I-th parameter combination method, cs represents the number of parameter combination schemes to evaluate the performance of each scheme;
B5, sorting the difference values epsilon according to the positive sequence of the difference values, and selecting a parameter combination scheme with the smallest difference value as a parameter combination scheme with optimal performance;
the intelligent control strategy generating module divides the working mode of the ventilation system into an energy-saving mode and a conventional mode based on the regional existence data, and automatically generates a control strategy by combining with a parameter combination scheme with optimal performance and transmits the control strategy to the autonomous system regulation processing module;
The implementation needs to specifically explain that the specific processing procedure of the control strategy intelligent generation module is as follows:
The method comprises the steps of C1, dividing the working mode of the ventilation system into an energy-saving mode and a conventional mode based on the region existence data, namely, for the condition that a person exists, recording the working mode of the ventilation system as the conventional mode, and for the condition that the person does not exist, recording the working mode of the ventilation system as the energy-saving mode;
under the energy-saving mode, according to the parameter combination scheme with optimal performance, taking the lower limit of the value range of the scene variable as the current operation parameter, and adopting a Monte Carlo method based on reinforcement learning to generate an energy-saving control strategy so as to achieve the aim of energy saving;
And C3, in a conventional mode, detecting the positions of the personnel according to the parameter combination scheme with optimal performance, calculating the personnel concentration by combining the positions of the personnel, calculating the required wind speed of the ventilation system according to the personnel concentration, and generating a conventional control strategy to ensure the comfort level of the personnel, wherein the processing process is as follows:
c31, detecting the position of a person by using a sensor, and counting the number of people in a unit area;
C32, calculating the personnel concentration RMD according to the number of people in the unit area and the area of the area, Wherein RS psi represents the number of people in the ith unit area, and MK represents the area of the area;
C33, calculating the required wind speed VF of the ventilation system according to the personnel density RMD and the air duct sectional area SJ of the ventilation system, Wherein W represents a correction coefficient, the specific value of which can be adjusted according to actual demands and design requirements, and generally, when the personnel concentration is high, the wind speed of the ventilation system needs to be increased to improve the air flow and the fresh air supply, so that the value of the correction coefficient can be properly improved in calculation;
c34, generating a conventional control strategy by adopting a reinforcement-learning-based Monte Carlo method in combination with an optimal-performance parameter combination scheme and a required wind speed of a ventilation system, wherein the reinforcement-learning-based Monte Carlo method belongs to the prior art means, so that the embodiment does not make specific description;
the system autonomous regulation processing module is used for receiving the data transmitted by the control strategy intelligent generation module, and performing autonomous regulation based on the control strategy to realize intelligent control of the ventilation system;
The automatic regulation and control are carried out based on a control strategy, particularly, a venturi valve is utilized for quick response, the air discharge quantity of the valve is quickly regulated, the constant wind speed of the surface of the ventilation cabinet is ensured, and the control precision is within +/-5%;
the operation parameter monitoring module is used for monitoring operation parameters of the ventilation system in real time, calculating the performance index of the system, providing real-time feedback and facilitating users to know the state of the system at any time;
The implementation needs to specifically explain that the processing procedure of the operation parameter monitoring module is as follows:
d1, monitoring the ventilation system in real time to obtain operation parameters of the ventilation system, wherein the operation parameters comprise system regulation response time, system regulation rate, system regulation actual value and target value, system regulation success times and total regulation times;
D2, calculating system parameter regulation precision and system regulation processing capacity according to the operation parameters;
the calculation formula of the system parameter regulation precision CTa specifically comprises the following steps: wherein ctθ represents a θ -th system regulation target value, ctsθ represents a θ -th system regulation actual value, and wx represents sample data, i.e., the corresponding number of the system regulation target value and the actual value;
The calculation formula of the system regulation processing capacity CTb specifically comprises the following steps:
wherein TX represents the system regulation response time, cv represents the system regulation rate, cg represents the system regulation success times, zc represents the total regulation times, beta 1, beta 2 and beta 3 represent the proportionality coefficients of the various items, the size of the proportionality coefficients is a specific numerical value obtained by quantizing the various parameters, the subsequent comparison is convenient, and the proportionality coefficients are only required to have no influence on the proportionality relation between the parameters and the quantized numerical values;
d3, calculating a system performance index Q, Q=gamma×CTa+ (1-gamma) ×CTb according to the system parameter regulation precision CTa and the system regulation processing capacity CTb, wherein gamma represents a weight coefficient and is used for balancing the influence of the system parameter regulation precision and the system regulation processing capacity on the system performance;
And D4, judging and comparing the system performance index Q with a preset system performance index Q threshold, if the system performance index Q is larger than or equal to the Q threshold, indicating that the system performance meets the expectations, recording the control strategy implemented in the current application scene, otherwise, indicating that the system performance does not meet the expectations, marking the control strategy implemented in the current application scene, and re-executing the control strategy intelligent generation module to generate a new control strategy, wherein the preset system performance index Q threshold can be specifically set according to specific conditions, and the embodiment does not specifically limit specific data.
Finally, the foregoing description of the preferred embodiment of the invention is provided for the purpose of illustration only, and is not intended to limit the invention to the particular form disclosed, but on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the invention.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
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