CN113932351A - Non-uniform temperature field real-time regulation and control system and method based on artificial intelligence algorithm - Google Patents

Non-uniform temperature field real-time regulation and control system and method based on artificial intelligence algorithm Download PDF

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CN113932351A
CN113932351A CN202111303110.1A CN202111303110A CN113932351A CN 113932351 A CN113932351 A CN 113932351A CN 202111303110 A CN202111303110 A CN 202111303110A CN 113932351 A CN113932351 A CN 113932351A
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air
air supply
temperature
room
section
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CN113932351B (en
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王非
赵金驰
王昕�
刘禹宏
***
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University of Shanghai for Science and Technology
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    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F7/00—Ventilation
    • F24F7/04—Ventilation with ducting systems, e.g. by double walls; with natural circulation
    • F24F7/06—Ventilation with ducting systems, e.g. by double walls; with natural circulation with forced air circulation, e.g. by fan positioning of a ventilator in or against a conduit
    • F24F7/08—Ventilation with ducting systems, e.g. by double walls; with natural circulation with forced air circulation, e.g. by fan positioning of a ventilator in or against a conduit with separate ducts for supplied and exhausted air with provisions for reversal of the input and output systems
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
    • F24F11/63—Electronic processing
    • F24F11/64—Electronic processing using pre-stored data
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/70—Control systems characterised by their outputs; Constructional details thereof
    • F24F11/72—Control systems characterised by their outputs; Constructional details thereof for controlling the supply of treated air, e.g. its pressure
    • F24F11/74—Control systems characterised by their outputs; Constructional details thereof for controlling the supply of treated air, e.g. its pressure for controlling air flow rate or air velocity
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/70—Control systems characterised by their outputs; Constructional details thereof
    • F24F11/80—Control systems characterised by their outputs; Constructional details thereof for controlling the temperature of the supplied air
    • F24F11/83—Control systems characterised by their outputs; Constructional details thereof for controlling the temperature of the supplied air by controlling the supply of heat-exchange fluids to heat-exchangers
    • F24F11/84—Control systems characterised by their outputs; Constructional details thereof for controlling the temperature of the supplied air by controlling the supply of heat-exchange fluids to heat-exchangers using valves
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/89—Arrangement or mounting of control or safety devices
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F7/00—Ventilation
    • F24F7/003—Ventilation in combination with air cleaning
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F8/00—Treatment, e.g. purification, of air supplied to human living or working spaces otherwise than by heating, cooling, humidifying or drying
    • F24F8/10—Treatment, e.g. purification, of air supplied to human living or working spaces otherwise than by heating, cooling, humidifying or drying by separation, e.g. by filtering
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F2110/00—Control inputs relating to air properties
    • F24F2110/10—Temperature
    • 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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  • Combustion & Propulsion (AREA)
  • Mechanical Engineering (AREA)
  • General Engineering & Computer Science (AREA)
  • Signal Processing (AREA)
  • Physics & Mathematics (AREA)
  • Fuzzy Systems (AREA)
  • Mathematical Physics (AREA)
  • Fluid Mechanics (AREA)
  • Air Conditioning Control Device (AREA)
  • Feedback Control In General (AREA)

Abstract

The invention discloses a non-uniform temperature field real-time regulation and control system and a method based on an artificial intelligence algorithm, wherein the method comprises the following steps: the fresh air pipe comprises a fresh air and return air mixing section, an air filtering section integrally communicated with the fresh air and return air mixing section, a surface cooling section integrally communicated with the air filtering section, a fan section integrally communicated with the surface cooling section and an air outlet section integrally communicated with the fan section, wherein a surface cooler is arranged in the surface cooling section, and a water quantity regulating valve is arranged at an inlet section of the surface cooler; the air supply pipe is communicated with the air outlet section, a plurality of temperature sensors are arranged in the room, and an air supply temperature sensor is arranged in the system air supply pipe; an air return pipe is arranged in the room. According to the invention, the device is more intelligent, high in precision and strong in stability, and can be used for constructing a non-uniform temperature device according to self requirements, thereby solving the defect that most ventilation systems face the indoor single parameter requirement and can only finally construct a consistent indoor parameter environment.

Description

Non-uniform temperature field real-time regulation and control system and method based on artificial intelligence algorithm
Technical Field
The invention relates to the technical field of ventilation systems, in particular to a non-uniform temperature field real-time regulation and control system and method based on an artificial intelligence algorithm.
Background
The main purpose of the heating, ventilating and air conditioning is to create appropriate environmental parameters (temperature, wind speed, humidity, pollutant concentration, etc.) for the building to ensure the requirements of indoor personnel or equipment. The unreasonable ventilation of the air conditioner brings about a plurality of problems, although the ventilation system is continuously developed by the concept of guaranteeing the requirements, most of the ventilation systems face the indoor single-parameter requirement (such as the indoor design temperature of 26 ℃) and finally the generally consistent indoor parameter environment is created. In many cases, different areas or locations in the same room may have different parameter requirements. As in data centers, the high density of electronic components causes thermal coupling resulting in the presence of high temperature environments. Due to the hardware layout, the thermal load is unevenly distributed in space. Against this background, the object of the present invention is to create a method which makes it possible to solve the problem of inhomogeneous temperature field control. The prior art means is to adopt single-loop feedback control to solve the problem, but the coupling exists among various points in the actual room, and the system is difficult to stabilize.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide the system and the method for regulating and controlling the non-uniform temperature field in real time based on the artificial intelligence algorithm, which are more intelligent, have high precision and strong stability, can construct a non-uniform temperature device according to the self requirement, and solve the defect that most ventilation systems face the requirement of indoor single parameter and can only finally construct a consistent indoor parameter environment at present. To achieve the above objects and other advantages in accordance with the present invention, there is provided a non-uniform temperature field real-time regulation system based on artificial intelligence algorithm, comprising:
the fresh air pipe comprises a fresh air and return air mixing section, an air filtering section integrally communicated with the fresh air and return air mixing section, a surface cooling section integrally communicated with the air filtering section, a fan section integrally communicated with the surface cooling section and an air outlet section integrally communicated with the fan section, wherein a surface cooler is arranged in the surface cooling section, and a water quantity regulating valve is arranged at an inlet section of the surface cooler;
the air supply pipe is communicated with the air outlet section, the air supply pipe is connected with a plurality of branch air supply pipes, the air supply pipe is provided with a plurality of branch air supply pipelines, each branch air supply pipeline is provided with an air supply outlet air valve and a temperature sensor, the branch air supply pipelines are communicated with a room, a plurality of temperature sensors are arranged in the room, a return air pipe is arranged in the room, the temperature sensors are in signal connection with an editable logic controller, and the editable logic controller is in signal connection with an embedded computer, a surface cooler, an air supply outlet air valve, a temperature sensor and a water quantity regulating valve.
Preferably, the temperature sensor is used for reading the indoor temperature and converting a physical signal into an electric signal to be transmitted into the programmable logic controller, and the programmable logic controller is used for converting the obtained electric signal into a digital signal to be transmitted to the embedded computer or the programmable logic controller is used for controlling the sizes of the blast outlet air valve and the surface cooler through the electric signal.
Preferably, the embedded computer is used for completing a control algorithm of the indoor non-uniform environmental field, and digital commands obtained by the algorithm are converted into digital signals and returned to the programmable logic controller.
A non-uniform temperature field real-time regulation and control system method based on an artificial intelligence prediction algorithm comprises the following steps:
s1, presetting the air volume and the water volume change range of the indoor room, and determining the interval;
s2, establishing a physical model required by a simulated thermal environment by using CFD software;
s3, setting the boundary conditions of CFD calculation, and selecting a plurality of simulation working conditions required by the prediction algorithm;
s4, extracting the data of each working condition, and sorting and calculating the data;
s5, reconstructing new flow field information according to the formula 5, and storing the new flow field information in a text file for subsequent adjustment and analysis;
s6, storing the optimal air supply parameters obtained in S5 into an embedded computer;
s7, inputting the air supply parameter result to a Programmable Logic Controller (PLC) from an embedded computer in the form of an electric signal;
s8, collecting temperature signals in the user-defined non-uniform temperature field through a thermocouple temperature sensor;
s9, acquiring signals through a temperature sensor and inputting the signals to a PLC;
s10, the temperature value read by the PLC realizes data exchange by using a transmission control protocol through a modbus module in python, and a data signal is transmitted to the embedded computer;
s11, the embedded computer compares the measured room temperature and the corrected result as signal to obtain the best air supply parameter and the best working condition value under the required temperature value;
s12, transmitting the corrected data back to the PLC by the same data exchange method as in the step S10;
and S13, transmitting the result of the algorithm through an electric signal by a PLC to control each branch air valve and the water quantity regulating valve, controlling the actual air supply temperature and air supply speed of the room, and completing closed-loop control.
A non-uniform temperature field real-time regulation and control system method based on an artificial intelligence mechanical learning algorithm comprises the following steps:
s1, presetting air supply parameter boundary conditions of a certain experimental cabin or room environment, namely the air supply temperature range and the air supply speed range of each air supply outlet, and sequentially designing a plurality of groups of air supply conditions in a prediction range;
and S2, selecting a training set. Setting a first group as an initial training working condition, operating the room air conditioning unit for seven days after the temperature in a room is stable (determined by a temperature sensor in the room), automatically reading and storing data every hour, wherein the stored data are respectively the input end: 4 indoor temperature monitoring points and 1 outdoor temperature; output end: the air supply speed and the air supply temperature of each of the 4 air supply outlets are controlled. Summarizing the read data set to a PLC and converting the data set into an electric signal for an embedded computer to calculate;
s3, training the output end and the input end by adopting a bp neural network to obtain an initial network;
s4, operating the room air conditioning unit for seven days again by taking the second group as an input parameter within a preset air supply parameter range, automatically reading stored data every hour, sending the obtained output value to each air valve and a water quantity regulating valve, and controlling the room temperature;
s5, merging the first group and the second group of monitoring input set output sets, optimizing and preprocessing the data set, eliminating redundant or gross errors and data appearing many times, and training the processed data result to obtain a new network;
s6, operating the room air conditioning unit for seven days again by taking the third group as an input parameter within a preset air supply parameter range, automatically reading stored data every hour, sending the obtained output value to each air valve and a water quantity regulating valve, and controlling the room temperature;
and S7, combining the data results of S5 and S6 again, optimizing the data set to obtain a new network, and realizing continuous updating and iteration of the network.
Compared with the prior art, the invention has the beneficial effects that:
(1) the invention is based on the programmable logic controller, according to the feedback instruction of the embedded computer, and then the opening of the air valve and the water valve of the air conditioning unit are controlled through the connection of the wires, and under the combined action of the controlled air valve and the water valve through the air pipe, the water pipe, the air conditioning box and the like, the actual air supply condition (the air supply speed and the air supply temperature of each air supply outlet) sent into a room is obtained, and finally the temperature of the indoor temperature sensor is regulated and controlled to the target range.
(2) According to the invention, data of each sensor monitoring point in a room are summarized by using the editable logic controller, information is input into the embedded AI controller, corresponding air supply parameters are obtained by calculating by using an artificial intelligence algorithm of a non-uniform temperature field, and the temperature between each point in the room is decoupled and analyzed by using the algorithm, so that the real-time regulation and control of the non-uniform temperature field based on the artificial intelligence algorithm are realized.
Drawings
FIG. 1 is a schematic diagram of a non-uniform temperature field real-time regulation system connection for a non-uniform temperature field real-time regulation system and method based on an artificial intelligence algorithm in accordance with the present invention;
FIG. 2 is a logic relationship diagram of a non-uniform temperature field real-time regulation system and method based on artificial intelligence algorithm according to the present invention;
FIG. 3 is a flow chart of a monitoring and correcting system of the intrinsic orthogonal decomposition method of the non-uniform temperature field real-time regulation and control system and method based on the artificial intelligence algorithm.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1-3, a non-uniform temperature field real-time regulation and control system based on artificial intelligence algorithm includes: the fresh air pipe comprises a fresh air and return air mixing section, an air filtering section integrally communicated with the fresh air and return air mixing section, a surface cooling section integrally communicated with the air filtering section, a fan section integrally communicated with the surface cooling section and an air outlet section integrally communicated with the fan section, wherein a surface cooler is arranged in the surface cooling section, and a water quantity regulating valve is arranged at an inlet section of the surface cooler; the air supply pipe is communicated with the air outlet section and is provided with a plurality of branch air supply pipelines, each branch air supply pipeline is provided with an air supply outlet air valve and an air supply temperature sensor, the branch air supply pipeline is communicated with a room, a plurality of temperature sensors are arranged in the room, one end of the room far away from the branch air supply pipeline is connected with an air return pipe, the temperature sensor is in signal connection with an editable logic controller, the surface cooler is provided with a cooling water pipe, the cooling water pipe is provided with a cooling water pipe valve, the editable logic controller is connected with an embedded computer, a surface air cooler, an air supply outlet air valve, a temperature sensor and the cooling water pipe valve by signals, firstly the temperature sensor is arranged at a proper position, the signals are sent to the editable logic controller and the embedded computer in sequence through system starting. By means of the existing intelligent algorithm, the control signals obtained through operation are returned to the editable logic controller, and then the opening degrees of air valves and cooling water valves of different air supply outlets are controlled, the air supply speed and the air supply temperature are influenced, and finally the set target non-uniform temperature environment is obtained. The invention is used for building a self-defined non-uniform temperature field in a space, the device has the advantages of self-adaptability of temperature building, high precision, easy installation and strong stability, and the actual air supply condition (the air supply speed and the air supply temperature of each air supply outlet) sent into a room is obtained under the combined action of the controlled air valve and the controlled water valve through the air pipe, the water pipe, the air conditioning box and the like, so that the temperature of the indoor temperature sensor is finally regulated to the target range.
Furthermore, the temperature sensor is used for reading the indoor temperature and converting a physical signal into an electric signal to be transmitted into the programmable logic controller, and the programmable logic controller is used for converting the obtained electric signal into a digital signal to be transmitted to the embedded computer or the programmable logic controller and used for controlling the sizes of the air valve of the air supply outlet and the surface cooler through the electric signal.
Furthermore, the embedded computer is used for finishing a control algorithm of the indoor non-uniform environmental field, and digital commands obtained by the algorithm are converted into digital signals and returned to the programmable logic controller.
Example 1
Prediction algorithm
POD method basic principle: according to the method, an environment field in a building is simulated through Computational Fluid Dynamics (CFD) to obtain a small number of data samples, the number of the samples is determined by judging errors between a difference value adjacent sample result and a CFD simulation result, and then a causal relationship between an air supply parameter and an indoor environment field is established through a fluent sample data extraction characteristic by using an intrinsic Orthogonal Decomposition (POD) method, so that the flow field information under any air supply parameter can be reconstructed quickly. On the basis, air supply parameter optimizing calculation is provided according to different target temperatures of a plurality of demand points, namely temperature values of the demand points under all air supply parameters in an interval are reconstructed, the error between the temperature values and the set target temperature is judged, and the optimal air supply parameter meeting the demand is determined.
Prediction-based intelligent control algorithm:
s1, presetting the air volume and the water volume change range of the indoor room, and determining the interval;
s2, establishing a physical model required by a simulated thermal environment by using CFD software;
s3, setting the boundary conditions of CFD calculation, and selecting a plurality of simulation working conditions required by the prediction algorithm;
s4, extracting the data of each working condition, and sorting and calculating the data, wherein the calculation theory and details are as follows:
first, the flow field variable U in the data may be defined by a boundary parameter (q)1,q2,…qn) Uniquely determined, then for a sample database consisting of M flow field variables
Figure BDA0003339105830000061
A set of most representative intrinsic orthogonal bases can be obtained by extracting the POD mode
Figure BDA0003339105830000062
Even a sample databaseThe projection of any vector on the orthogonal basis is maximized, namely:
Figure BDA0003339105830000071
in the formula, λiThe eigenvalues corresponding to the autocovariance matrix composed of the sample data are arranged in the order from big to small, and the magnitude of the eigenvalue represents the energy content of the vector. Thus, there are:
Figure BDA0003339105830000072
in the formula (I), the compound is shown in the specification,
Figure BDA0003339105830000073
i.e. all the modalities, v, in the sample databaseiFor the reordered lambdai，
Figure BDA0003339105830000074
Is λiThe corresponding feature vector. Since any vector in the database can be projected onto the derived modality, then:
Figure BDA0003339105830000075
Figure BDA0003339105830000076
in the formula (I), the compound is shown in the specification,
Figure BDA0003339105830000077
and solving to obtain modal coefficients corresponding to any group of parameters through the difference, and linearly combining the modal coefficients with the modal to obtain predicted flow field data. Namely:
Figure BDA0003339105830000078
and S5, reconstructing new flow field information according to the formula 5, and storing the new flow field information into a text file for subsequent adjustment and analysis.
The optimization formula is defined as:
Figure BDA0003339105830000079
wherein E is the average error, n is the number of selected reconstruction points, UiIs a reconstructed value of the ith point, ViIs the target value of the ith point. The optimization process finds the air supply parameter when the E value is minimum.
S6, storing the optimal air supply parameters obtained in S5 into an embedded computer;
s7, inputting the air supply parameter result to a Programmable Logic Controller (PLC) from an embedded computer in the form of an electric signal;
s8, collecting temperature signals in the user-defined non-uniform temperature field through a thermocouple temperature sensor;
s9, acquiring signals through a temperature sensor and inputting the signals to a PLC;
s10, the temperature value read by the PLC realizes data exchange by using a transmission control protocol through a modbus module in python, and a data signal is transmitted to the embedded computer;
s11, comparing the actual temperature of the room with the embedded computer to obtain the difference value delta t between the preset temperature value and the actual temperature value:
if delta t is less than 0.5 ℃, the algorithm effect is considered to be good, and the optimal air supply parameter and the optimal working condition value under the required temperature value are directly obtained;
if delta t is more than or equal to 0.5 ℃, correcting the parameters:
T′m＝Tm-(Tc-Tm)＝2Tm-Tc 7
in formula (II) T'mTo a new target temperature, TmIs the original target temperature (desired temperature), TcThe measured temperature is used.
Inputting the corrected result as a signal to obtain an optimal air supply parameter and an optimal working condition value under a required temperature value;
s12, transmitting the corrected data back to the PLC by the same data exchange method as in the step S9;
and S13, transmitting the result of the algorithm through an electric signal by a PLC to control each branch air valve and the water quantity regulating valve, controlling the actual air supply temperature and air supply speed of the room, and completing closed-loop control.
The method adopts a working method of centralized sampling and centralized output of a Programmable Logic Controller (PLC), reduces the interference influence of outside air parameters, saves time, and ensures that the algorithm is reliably and quickly carried out; the embedded AI controller (raspberry group) can realize remote control of target room parameters and release manpower and material resources, has strong intelligent algorithm computing capability, is high in optimization computing process efficiency, is easy to obtain optimal design parameters, and is favorable for creating an expected non-uniform temperature field. The two controllers are communicated through a Modbus module of the Python platform to realize transmission control, and the Modbus controller has an authoritative and reliable supporting platform to ensure efficient and stable operation.
Example 2
Mechanical learning training
S1, presetting air supply parameter boundary conditions of a certain experimental cabin or room environment, namely the air supply temperature range and the air supply speed range of each air supply outlet, and sequentially designing a plurality of groups of air supply conditions in a prediction range;
and S2, selecting a training set. Setting a first group as an initial training working condition, operating the room air conditioning unit for seven days after the temperature in a room is stable (determined by a temperature sensor in the room), automatically reading and storing data every hour, wherein the stored data are respectively the input end: 4 indoor temperature monitoring points and 1 outdoor temperature; output end: the air supply speed and the air supply temperature of each of the 4 air supply outlets are controlled. Summarizing the read data set to a PLC and converting the data set into an electric signal for an embedded computer to calculate;
s3, training an output end and an input end by adopting a bp neural network, setting five inputs, namely a hidden layer containing ten neurons and an output layer containing five neurons, and obtaining an initial network;
s4, operating the room air conditioning unit for seven days again by taking the second group as an input parameter within a preset air supply parameter range, automatically reading stored data every hour, sending the obtained output value to each air valve and a water quantity regulating valve, and controlling the room temperature;
s5, merging the first group and the second group of monitoring input set output sets, optimizing and preprocessing the data set, eliminating redundant or gross errors and data appearing many times, and training the processed data result to obtain a new network;
s6, operating the room air conditioning unit for seven days again by taking the third group as an input parameter within a preset air supply parameter range, automatically reading stored data every hour, sending the obtained output value to each air valve and a water quantity regulating valve, and controlling the room temperature;
and S7, combining the data results of S5 and S6 again, optimizing the data set to obtain a new network, and realizing continuous updating and iteration of the network.
The number of devices and the scale of the processes described herein are intended to simplify the description of the invention, and applications, modifications and variations of the invention will be apparent to those skilled in the art.
While embodiments of the invention have been described above, it is not limited to the applications set forth in the description and the embodiments, which are fully applicable in various fields of endeavor to which the invention pertains, and further modifications may readily be made by those skilled in the art, it being understood that the invention is not limited to the details shown and described herein without departing from the general concept defined by the appended claims and their equivalents.

Claims (5)

1. The utility model provides a real-time regulation and control system of inhomogeneous temperature field based on artificial intelligence algorithm which characterized in that includes:
the fresh air pipe comprises a fresh air and return air mixing section, an air filtering section integrally communicated with the fresh air and return air mixing section, a surface cooling section integrally communicated with the air filtering section, a fan section integrally communicated with the surface cooling section and an air outlet section integrally communicated with the fan section, wherein a surface cooler is arranged in the surface cooling section, and a water quantity regulating valve is arranged at an inlet section of the surface cooler;
the air supply pipe is communicated with the air outlet section, the air supply pipe is connected with a plurality of branch air supply pipes, each branch air supply pipe is provided with an air supply outlet air valve and an air supply temperature sensor, the branch air supply pipes are communicated with a room, a plurality of temperature sensors are arranged in the room, a return air pipe is arranged in the room, the temperature sensors are in signal connection with an editable logic controller, and the editable logic controller is in signal connection with an embedded computer, a surface cooler, an air supply outlet air valve, a temperature sensor and a water quantity regulating valve.
2. The non-uniform temperature field real-time regulation and control system based on the artificial intelligence algorithm as claimed in claim 1, wherein the temperature sensor is used for reading indoor temperature and converting physical signals into electrical signals to be transmitted to the programmable logic controller, the programmable logic controller is used for converting the obtained electrical signals into digital signals to be transmitted to the embedded computer, the embedded computer obtains the valve position corresponding to each valve and returns the valve position information to the editable logic controller in the form of digital model, and the programmable logic controller is used for controlling the size of the blast outlet air valve and the flow of the coil pipe of the air conditioning cabinet through the electrical signals.
3. The algorithm for calculating the valve position information corresponding to each valve by the embedded computer can adopt an intelligent algorithm based on mechanical learning or a prediction intelligent algorithm to realize the regulation and control of the indoor non-uniform temperature field as claimed in claim 2.
4. The non-uniform temperature field real-time regulation and control system method based on the mechanical learning intelligent algorithm as claimed in claim 3, characterized by comprising the following steps:
s1, presetting air supply parameter boundary conditions of a certain experimental cabin or room environment, namely the air supply temperature range and the air supply speed range of each air supply outlet, and sequentially designing a plurality of groups of air supply conditions in a prediction range;
and S2, selecting a training set. Setting a first group as an initial training working condition, operating the room air conditioning unit for seven days after the temperature in a room is stable (determined by a temperature sensor in the room), automatically reading and storing data every hour, wherein the stored data are respectively the input end: 4 indoor temperature monitoring points and 1 outdoor temperature; output end: the air supply speed and the air supply temperature of each of the 4 air supply outlets are controlled. Summarizing the read data set to a PLC and converting the data set into an electric signal for an embedded computer to calculate;
s3, training the output end and the input end by adopting a bp neural network to obtain an initial network;
s4, operating the room air conditioning unit for seven days again by taking the second group as an input parameter within a preset air supply parameter range, automatically reading stored data every hour, sending the obtained output value to each air valve and a water quantity regulating valve, and controlling the room temperature;
s5, merging the first group and the second group of monitoring input set output sets, optimizing and preprocessing the data set, eliminating redundant or gross errors and data appearing many times, and training the processed data result to obtain a new network;
s6, operating the room air conditioning unit for seven days again by taking the third group as an input parameter within a preset air supply parameter range, automatically reading stored data every hour, sending the obtained output value to each air valve and a water quantity regulating valve, and controlling the room temperature;
and S7, combining the data results of S5 and S6 again, optimizing the data set to obtain a new network, and realizing continuous updating and iteration of the network.
5. The non-uniform temperature field real-time regulation and control system method based on the intelligent prediction algorithm as claimed in claim 3, characterized by comprising the following steps:
s1, presetting the air volume and the water volume change range of the indoor room, and determining the interval;
s2, establishing a physical model required by a simulated thermal environment by using CFD software;
s3, setting the boundary conditions of CFD calculation, and selecting a plurality of simulation working conditions required by the prediction algorithm;
s4, extracting the data of each working condition, and sorting and calculating the data;
s5, reconstructing new flow field information according to the formula 5, and storing the new flow field information in a text file for subsequent adjustment and analysis;
s6, storing the optimal air supply parameters obtained in S5 into an embedded computer;
s7, inputting the air supply parameter result to a Programmable Logic Controller (PLC) from an embedded computer in the form of an electric signal;
s8, collecting temperature signals in the user-defined non-uniform temperature field through a thermocouple temperature sensor;
s9, acquiring signals through a temperature sensor and inputting the signals to a PLC;
s10, the temperature value read by the PLC realizes data exchange by using a transmission control protocol through a modbus module in python, and a data signal is transmitted to the embedded computer;
s11, the embedded computer compares the measured room temperature and the corrected result as signal to obtain the best air supply parameter and the best working condition value under the required temperature value;
s12, transmitting the corrected data back to the PLC by the same data exchange method as in the step S10;
and S13, transmitting the result of the algorithm through an electric signal by a PLC to control each branch air valve and the water quantity regulating valve, controlling the actual air supply temperature and air supply speed of the room, and completing closed-loop control.
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