CN115978736B - Self-adaptive prediction wind-water linkage control method for subway station air conditioning system - Google Patents
Self-adaptive prediction wind-water linkage control method for subway station air conditioning system Download PDFInfo
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Abstract
The invention discloses a self-adaptive prediction wind-water linkage control method for a subway station air conditioning system, which belongs to the technical field of subway station air conditioning systems, and is used for acquiring data such as indoor and outdoor temperatures, indoor and outdoor humidity, indoor carbon dioxide concentration, station passenger flow parameters and the like of a subway station and acquiring data such as operation parameters, load parameters, energy consumption parameters and the like of each device of the subway station air conditioning system. In order to solve the problems that the operation energy efficiency of the air conditioning system of the subway station is low, the energy waste is serious, and the thermal comfort in the station cannot be well guaranteed, the self-adaptive prediction wind-water linkage control method for the air conditioning system of the subway station, disclosed by the invention, determines the operation mode of the air conditioning system through indoor and outdoor enthalpy value parameters and carbon dioxide concentration parameters in the station, realizes the on-line optimization of multiple parameters in the air conditioning system of the subway station, accurately predicts the load of the air conditioning system of the subway station, completes the self-adaptive prediction wind-water linkage control of the air conditioning system of the subway station, and realizes the fine regulation and control and energy-saving efficient operation of the air conditioning system of the subway station.
Description
Technical Field
The invention relates to the technical field of subway station air conditioning systems, in particular to a self-adaptive prediction wind-water linkage control method for a subway station air conditioning system.
Background
The existing subway station air conditioning system mainly comprises four parts: firstly, an air conditioner and a smoke exhaust system of a public area of a subway station are commonly called as a large system; secondly, a house for subway equipment management and a smoke discharging system are commonly called as a small system; thirdly, a tunnel ventilation air conditioner and a smoke discharging system; fourth, the cold water circulation system of the air conditioner of the subway station is commonly called as a water system. Wherein the "water system" with key regulation comprises the device: the water chiller, the cooling water pump, the chilled water pump, the cooling tower and the two-way valve; the major regulatory "large system" contains the devices: the device comprises a blower, a return exhaust fan, a fresh air fan and an air valve. According to the different opening and closing of each device of the large system and the opening and closing of the air valve, different operation modes can be realized: a fresh air mode, a small fresh air mode, a full return air mode and a ventilation mode.
Chinese patent publication No. CN107178849a discloses an evaporative cooling direct expansion air conditioning system for a subway station, which comprises a compressor device arranged in a public area of the subway station, a plurality of direct expansion air processing units arranged in parallel, an electronic throttling device, and an evaporative cooling device arranged outside each entrance and exit of the subway station; the inlet of the evaporative cooling device is connected with the outlet of the compressor device, the outlet of the evaporative cooling device is connected with the inlets of the plurality of direct expansion air treatment units which are arranged in parallel through the electronic throttling device, and the outlets of the direct expansion air treatment units are connected with the inlet of the compressor device after being connected in parallel. According to the air conditioning system provided by the invention, the evaporative cooling device is arranged to directly discharge the heat carried by the high-temperature gaseous refrigerant to the external atmosphere, so that a cooling tower and a water cooling chiller in the traditional subway station air conditioning system are omitted. However, the above patent suffers from the following drawbacks:
The subway station air conditioning system cannot automatically switch the operation mode according to parameters such as outdoor meteorological parameters, indoor environment parameters, carbon dioxide concentration and the like; the start, stop, loading and unloading of each device of the water system cannot be controlled according to parameters such as the temperature, humidity and passenger flow volume in the station, for example: the number of the cold water main engine, the number of the cooling water pumps, the number of the chilled water pumps, the number of the cooling tower fans and the number of the cooling tower fans; the large system and the water system cannot be adjusted correspondingly to provide the operation parameters of each device, such as: the water outlet temperature of chilled water of the water chiller, the water outlet temperature of cooling water, the running frequency of a chilled water pump, the running frequency of a cooling water pump, the running frequency of a fan of a cooling tower, the opening degree of a two-way valve, the running frequency of a blower and the like. The cooling capacity of the air conditioning system of the subway station cannot be matched with the heat and humidity removal requirements in the actual station, the overall operation energy efficiency is low, the energy waste is serious, and the thermal comfort in the station cannot be well ensured.
Disclosure of Invention
The invention aims to provide a self-adaptive prediction wind-water linkage control method for an air conditioning system of a subway station, which is characterized in that a wind system operation mode is determined according to the change of indoor and outdoor enthalpy values through the acquisition of real-time parameters, the multi-parameter optimization of the air conditioning system is realized through the related technology of data mining and artificial intelligence by utilizing association rules, the wind-water linkage control of the air conditioning system of the subway station is realized, the efficient and energy-saving management and control is realized, the technical support is provided for improving the performance, optimizing the control and saving the energy of the air conditioning system of the subway station, and the method accords with the current world and national energy-saving policy trend, so as to solve the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a self-adaptive prediction wind-water linkage control method for an air conditioning system of a subway station comprises the following steps of
Acquiring data such as indoor and outdoor temperatures, indoor and outdoor humidity, indoor carbon dioxide concentration, station passenger flow parameters and the like of a subway station;
acquiring data such as operation parameters, load parameters, energy consumption parameters and the like of each equipment of an air conditioning system of a subway station;
calculating to obtain indoor and outdoor air enthalpy values, designing a control method according to the enthalpy values and carbon dioxide parameters, and determining an air conditioning system operation mode;
the automatic switching of the system operation mode is realized by utilizing the wind-water linkage control device;
cleaning the equipment data, and obtaining the optimal running number and parameters of the equipment by using an association rule mining method;
optimal control of system equipment is realized by utilizing the wind-water linkage control device;
cleaning the load data, and obtaining influence parameters of load prediction by utilizing correlation analysis;
a neural network is established to obtain the predicted cooling load of the air conditioning system of the subway station;
the wind-water linkage control device is utilized to automatically regulate and control the system operation mode, and then the system equipment operation parameters are adaptively regulated and controlled according to the load prediction result, so that a subway station air-conditioning system adaptive prediction wind-water linkage control method is formed;
Wherein the wind-water linkage control device comprises
The sensing layer is composed of environment parameter sensors and equipment sensors;
the outdoor weather parameter acquisition is completed by a temperature and humidity sensor; the acquisition of indoor air parameters is completed by a temperature and humidity sensor and a carbon dioxide concentration meter;
the collection of the running parameters of the water chilling unit, the water pump and the cooling tower is completed by adding a temperature sensor, a flow rate sensor, a pressure sensor and a parameter record of the equipment at or near the equipment;
the air conditioning unit is provided with an air speed sensor and a carbon dioxide concentration meter at the positions of the air supply pipeline, the air return pipeline and the air exhaust pipeline;
converting the collected data signals into 4-20mA current signals or 0-10V voltage signals for transmission;
the transmission layer is used for summarizing and transmitting communication signals of all the devices through a field bus, and for the devices not easy to arrange communication lines, a wireless network networking technology is adopted for data transmission, and the data are communicated with the controller and the upper computer through a Modbus communication protocol;
the application layer comprises a controller and an upper computer, the self-adaptive prediction wind-water linkage control method of the subway station air conditioning system is arranged in the controller and the upper computer in a mode of designing a control module, a control cabinet is formed and arranged in an environmental control machine room, and the collected data are calculated, analyzed and control instructions are issued.
Further, during the refrigerating season, the control method is designed as follows
When the indoor CO 2 concentration is more than 1200ppm (settable), if the outdoor enthalpy value is more than the indoor enthalpy value, a small fresh air operation mode is adopted when fresh air needs cooling treatment;
the small fresh air fan and the valve D3 thereof are opened, the fresh air valve D4 is closed, the return air valve D1 is opened, and the exhaust valve D2 is closed;
when the outdoor enthalpy value is more than the indoor enthalpy value, cooling by using the cooling capacity of the fresh air;
when the predicted air conditioner load is smaller than the cold quantity caused by fresh air, the cold source system stops running and adopts a full ventilation running mode;
the air return valve D1 is closed, the small fresh air fan and the valve D3 thereof are closed, the fresh air valve D4 and the exhaust valve D2 are opened, and the operation frequencies of the air blower and the exhaust fan are adjusted according to the indoor temperature;
when the outdoor enthalpy value is less than the indoor enthalpy value, free refrigeration can be performed by using outdoor fresh air;
when the predicted air conditioner load is larger than the cold quantity brought by the fresh air, distributing the respective cold quantity proportion born by the outdoor fresh air and the cold source system according to meteorological conditions, and determining the water outlet temperature of the cold machine;
the operation is performed in a full ventilation mode, and the operation frequency of the blower and the exhaust fan is adjusted according to the indoor temperature;
when the indoor CO 2 concentration is less than 1200ppm (settable), if the outdoor enthalpy value is more than the indoor enthalpy value, the internal circulation mode is adopted for operation;
The cold energy demand is all borne by the refrigerating unit, and the water outlet temperature of the refrigerating unit is set according to load prediction;
the air exhaust valve D2, the small fresh air fan, the valve D3 and the fresh air valve D4 are closed, the return air valve D1 is opened, the fresh air fan is closed, the air blower and the exhaust fan are operated, and the operating frequencies of the air blower and the exhaust fan are adjusted according to the indoor temperature and humidity.
Further, in the transition season, the control method of the design is that
The transitional refrigerating unit does not operate;
when the outdoor temperature is higher than 12 ℃, the large system is operated in a full ventilation mode;
the return air valve D1 is closed, the small fresh air fan and the valve D3 thereof are closed, and the fresh air valve D4 and the exhaust valve D2 are opened;
adjusting the operation frequency of the blower and the exhaust fan according to the indoor carbon dioxide concentration;
when the outdoor temperature is 5-12 ℃, the small fresh air unit is closed, the return air valve D1 and the fresh air valve D4 are opened, the exhaust valve D2, the small fresh air fan and the valve D3 thereof are closed, and the operation frequencies of the air blower and the exhaust fan are adjusted according to the indoor carbon dioxide concentration;
in winter, the control method is as follows
When the indoor CO 2 concentration is less than 1200ppm, the large system does not operate;
when the indoor CO 2 concentration is more than 1200ppm, the small fresh air fan and the valve D3 thereof are opened, the fresh air valve D4 is closed, the exhaust valve D2 is closed, the return air valve D1 is opened, the small fresh air fan is opened, and the operation frequencies of the air feeder and the exhaust fan are adjusted according to the indoor carbon dioxide concentration.
Further, cleaning the equipment data to obtain continuous, complete and high-quality parameter data;
the control method for designing the running number of the air-conditioning water system equipment according to the parameter data comprises the following steps of
When the current ratio of the water chilling unit is more than 95% and the actual outlet water temperature of chilled water of the water chilling unit is more than the set outlet water temperature of chilled water of the water chilling unit, starting loading operation of the water chilling unit, and additionally starting a water chilling unit;
when the sum of the current ratios of the water chilling units is less than or equal to 100 percent and the actual water outlet temperature of chilled water of the water chilling units is less than the set water outlet temperature of chilled water of the water chilling units, starting load shedding operation of the water chilling units, and closing one water chilling unit;
when the cooling tower fan reaches the minimum frequency setting and the cooling tower water outlet temperature is less than the set water outlet temperature, starting load shedding operation of the cooling tower, closing the cooling tower fan and closing a corresponding cooling tower butterfly valve;
when the cooling tower fan reaches the highest frequency setting and the cooling tower outlet water temperature is more than the set outlet water temperature, loading operation of the cooling tower is started, the cooling tower fan is started, and a corresponding cooling tower butterfly valve is started.
Further, the chilled water outlet temperature and the cooling water outlet temperature of the main machine of the cold water are automatically set;
Automatically setting a temperature difference or a pressure difference PID of the water supply and return to adjust the operating frequencies of the chilled water pump and the cooling water pump;
automatically setting the running frequency of the fan of the approximation PID regulating cooling tower;
automatically setting the air supply temperature PID of the air system to adjust the opening of the two-way valve;
and (3) automatically setting the air supply temperature PID of the air system and adjusting the operating frequency of each fan.
Further, load data of the air conditioning system of the subway station are obtained through calculation of chilled water inlet and outlet temperatures and chilled water flow of the water chiller;
obtaining relevant factors influencing the load of the air conditioning system of the subway station through correlation analysis;
and (5) acquiring real-time data and historical data by using the sensor, and constructing a neural network load prediction model.
Further, performing a cleaning process on the device data, including:
acquiring attribute information of the equipment data, and classifying the equipment data according to the attribute information to obtain a plurality of pieces of different types of sub-equipment data;
generating a data cleaning task according to each piece of sub-equipment data, establishing a first queuing queue according to a plurality of data cleaning tasks, and acquiring a first queue length of the first queuing queue;
establishing a plurality of data cleaning processes, establishing a second queuing queue according to the plurality of data cleaning processes, and acquiring a second queue length of the second queuing queue;
When the first queue length is determined to be greater than the second queue length, distributing a data cleaning task for each data cleaning process in the second queue, and executing the corresponding data cleaning task based on the data cleaning process; meanwhile, the first queuing queue is corrected to obtain a corrected first queuing queue;
in the process of executing a corresponding data cleaning task by a data cleaning process, extracting features of the data cleaning task and determining feature data;
respectively cleaning the characteristic data by using a plurality of stored preset data cleaning rules, and counting the cleaning rate of each preset data cleaning rule on the characteristic data;
determining a preset data cleaning rule with the maximum cleaning rate as a target cleaning rule;
cleaning the residual data except the characteristic data in the data cleaning task based on the target cleaning rule;
monitoring the cleaning load capacity of each data cleaning process in the process of cleaning the residual data except the characteristic data in the data cleaning task, and establishing a third queuing queue from large to small;
when the cleaning load capacity of the last data cleaning process is smaller than the preset load capacity, dynamically distributing data cleaning tasks from the corrected first queuing queue to the rear preset number of data cleaning processes of the third queuing queue;
Repeating the above method until all data cleaning tasks are processed.
Further, after adaptively adjusting and controlling the operation parameters of the system equipment according to the load prediction result, the method further comprises the following steps:
collecting voice signals of regulatory personnel;
performing voice recognition on the voice signal to determine a voice regulation instruction;
setting n extraction indexes;
extracting the voice regulation instruction according to n extraction indexes, and determining an index value corresponding to each extraction index to obtain a vector A to be detected;
calculating the matching degree of the vector to be detected and each control instruction in a voice control database, wherein the voice control database comprises p voice control instructions, each voice control instruction comprises n numerical values corresponding to extracted data, and a data matrix B is formed;
wherein, the liquid crystal display device comprises a liquid crystal display device,the matching degree between the vector A to be detected and the t-th voice control instruction in the voice control database is obtained; />Is the i-th value in the vector A to be detected; />I=1, 2, 3 … … n, t=1, 2, 3 … … p for the values of the ith row and column in the data matrix B corresponding to the ith value in the vector a to be detected;
and correcting the voice control instruction according to the voice control instruction corresponding to the maximum matching degree to obtain a corrected voice control instruction, and regulating and controlling the operation parameters of the control system equipment according to the corrected voice control instruction.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the self-adaptive prediction wind-water linkage control method for the subway station air conditioning system, the running mode of the air conditioning system is determined through the indoor and outdoor enthalpy value parameter and the carbon dioxide concentration parameter in the subway station air conditioning system, and then the unattended automatic control of the subway station air conditioning system is realized through the automatic control device, so that the problem that the running mode of the subway station air conditioning system is manually set at present is solved.
2. According to the self-adaptive prediction wind-water linkage control method for the subway station air conditioning system, the data mining technology is fully utilized, the operation mechanism of the air conditioning system is combined, the system energy efficiency ratio is used as a guide, and the on-line optimization of multiple parameters in the subway station air conditioning system is realized; the load prediction technology is utilized to accurately predict the load of the air conditioning system of the subway station, preset parameters of all equipment in the system, complete the self-adaptive prediction wind-water linkage control of the air conditioning system of the subway station, realize the fine regulation and control and the energy-saving efficient operation of the air conditioning system of the subway station, and solve the problems of empirical control mode and invalid utilization of operation data at present.
Drawings
FIG. 1 is a flow chart of a method for adaptively predicting wind-water linkage control of an air conditioning system of a subway station according to the invention;
FIG. 2 is a system schematic diagram of a design control method of the present invention;
FIG. 3 is a schematic diagram of a neural network load prediction model of the present invention;
fig. 4 is a schematic diagram of one-key switching of the intelligent management and control system and BAS system of the subway station air conditioner of the present invention;
fig. 5 is a block diagram of the adaptive prediction wind-water linkage control system of the subway station air conditioning system.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Referring to fig. 1, a method for adaptively predicting wind-water linkage control of an air conditioning system of a subway station includes the following steps:
acquiring data such as indoor and outdoor temperatures, indoor and outdoor humidity, indoor carbon dioxide concentration, station passenger flow parameters and the like of a subway station, and acquiring data such as operation parameters, load parameters, energy consumption parameters and the like of various equipment of an air conditioning system of the subway station;
Calculating to obtain indoor and outdoor air enthalpy values, determining an air conditioning system operation mode according to the enthalpy values and carbon dioxide parameters, realizing automatic switching of the system operation mode by using a wind-water linkage control device, realizing optimal control of system equipment by using the wind-water linkage control device, automatically regulating and controlling the system operation mode by using the wind-water linkage control device, and then adaptively regulating and controlling the system equipment operation parameters according to a load prediction result to form a subway station air conditioning system adaptive prediction wind-water linkage control method;
the enthalpy value of air refers to the amount of insulation contained in the air, generally expressed by the symbol i on the basis of the unit mass of dry air, in kj/kg of dry air; the wet air enthalpy is equal to the sum of the enthalpy of 1kg dry air and the enthalpy of dkg water vapor.
Wet air enthalpy calculation formula:
i=1.01t+ (2500+1.84t) d or i= (1.01+1.84d) t+2500d (kj/kg dry air)
Wherein: t-air temperature DEG C
d-moisture content of air kg/kg dry air
1.01-average constant pressure specific heat kj/(kg.K) of dry air
1.84-average constant pressure specific heat kj/(kg.K) of water vapor
Latent heat of vaporization kj/kg of water at 2500-0deg.C
It is therefore clear how the indoor and outdoor air enthalpy values are calculated in particular.
An association rule is a logically implication relationship in the form of XY, where XI, YI and xy=Φ, X is called the front piece of the rule, Y is the result, and for association rule XY, there is a degree of support and a degree of trust.
The support degree refers to the frequency of the patterns appearing in the rule, and if s% of the transactions in the transaction database contain XY, the support degree of the association rule XY in D is referred to as s%, and may be expressed as probability P (XY), that is, support (XY) =p (XY). Trust refers to the strength of implications, i.e. c% of transactions containing X in transaction D contain XY at the same time. If the support degree of X is support (X), the trust degree of the rule is: support (XY)/support (X), which is a conditional probability P (y|x), i.e., confidence (XY) =p (y|x).
Taking the passenger flow, weather temperature, humidity and indoor temperature and humidity as X, taking the running number of the equipment as Y, and determining Y and the other parameters by calculating the support degree and the trust degree.
For example, in a market selling mobile phones, 70% of the mobile phone sales include sales of chargers, while 56% of the sales in all transactions include both mobile phones and chargers, in this example, the support is 56% and the confidence is 70%.
For example, a total of 10000 consumers purchased goods, 1000 people who purchased diapers, 2000 people who purchased beer, 500 people who purchased bread, 800 people who purchased diapers and beer, and 100 people who purchased bread of diapers.
Confidence level: the probability of purchasing X and Y together, for example, the probability of purchasing a person who purchased a diaper and beer together, is the confidence of purchasing beer when purchasing the diaper.
confidence (X- > y) =number of people buying { X, y } at the same time/number of people buying X;
confidence (Y- > x) =number of people buying { x, Y } at the same time/number of people buying Y;
confidence level of (diaper- > beer) =800/1000=0.8;
confidence level of (beer- > diaper) =800/2000=0.4;
since the rule confidence does not provide the proportion of this relationship in all transactions (the degree of coverage is low), i.e. whether the purchase behavior contained in the relationship is a general transaction behavior or an individual behavior is unknown. We can use the support statistic to measure the percentage of all transactions that contain the attribute values that appear in the association.
Support degree: probability of simultaneous occurrence of { X, Y };
for example: { diaper, probability of beer } simultaneous occurrence: support = number of people buying { X, Y } simultaneously/total number of people;
{ diaper, beer } support = 800/10000 = 0.08;
{ diaper, bread }, support = 100/10000 = 0.01.
The input passenger flow volume, weather temperature, humidity or indoor temperature and humidity are compared with the final energy consumption, and a certain logic relationship exists between the parameters, that is, for example, when the passenger flow volume is 1000, the power consumption is minimum, and the minimum power consumption can be calculated through the support degree and the trust degree.
It is therefore clear that the association rule mining method is utilized to obtain the optimal number of operating devices and parameters.
The correlation refers to the correlation between two or more random variables, which represents a quantifiable relationship between variables, such as outdoor temperature and power consumption of air conditioner in home, and statistics show that when the outdoor temperature rises, the power consumption is increased, and the power consumption can be known through correlation analysis, such as outdoor weather, temperature is a correlation, moisture in rainy days, or more indoor people and more indoor heat, and the correlation is provided between the two.
Cleaning equipment data, obtaining the optimal operation number and parameters of the equipment by using a correlation rule mining method, realizing optimal control of system equipment by using a wind-water linkage control device, automatically regulating and controlling a system operation mode by using the wind-water linkage control device, and then adaptively regulating and controlling the operation parameters of the system equipment according to a load prediction result to form a subway station air-conditioning system adaptive prediction wind-water linkage control method;
and cleaning the load data, obtaining influence parameters of load prediction by utilizing correlation analysis, establishing a neural network to obtain the predicted cold load of the air conditioning system of the subway station, utilizing a wind-water linkage control device to realize optimal control of system equipment, utilizing the wind-water linkage control device to automatically regulate and control the system operation mode, and then adaptively regulating and controlling the system equipment operation parameters according to the load prediction result to form the adaptive prediction wind-water linkage control method of the air conditioning system of the subway station.
The related parameters of the subway station air conditioning system are obtained through the sensors;
the method specifically comprises the following steps: outdoor air temperature, outdoor air humidity, indoor air temperature, indoor air humidity, indoor carbon dioxide concentration, station passenger flow volume, chilled water inlet and outlet temperature of a chiller, chiller energy consumption, chilled water pump operating frequency, chilled water flow, chilled water pump energy consumption, cooling water pump operating frequency, cooling water flow, cooling water pump energy consumption, cooling tower fan operating frequency, cooling tower energy consumption, two-way valve opening, back-exhaust fan operating frequency, back-exhaust fan energy consumption, back-exhaust valve opening, fresh air machine operating frequency, fresh air machine energy consumption, fresh air valve opening, blower operating frequency, blower energy consumption and the like.
Example two
Referring to fig. 2, indoor and outdoor enthalpy values are calculated according to outdoor air temperature, outdoor air humidity, indoor air temperature and indoor air humidity, and then a control method is designed according to the indoor and outdoor enthalpy values and indoor carbon dioxide concentration, so that an operation mode of an air conditioning system of a subway station is automatically adjusted.
The control method of the design is divided into the following steps:
first refrigerating season
When the indoor CO 2 concentration is more than 1200ppm (settable), if the outdoor enthalpy value is more than the indoor enthalpy value, when the fresh air needs to be cooled, a small fresh air operation mode is adopted, a small fresh air fan and a valve D3 thereof are opened, a fresh air valve D4 is closed, a return air valve D1 is opened, and an exhaust valve D2 is closed;
when the outdoor enthalpy value is greater than the indoor enthalpy value, the cooling capacity of fresh air can be utilized, when the predicted air conditioning load is smaller than the cold quantity caused by the fresh air, the cold source system stops running, a full ventilation running mode is adopted, the return air valve D1 is closed, the small fresh air fan and the valve D3 thereof are closed, the fresh air valve D4 and the exhaust valve D2 are opened, and the running frequencies of the air feeder and the exhaust fan are regulated according to the indoor temperature;
when the outdoor enthalpy value is smaller than the indoor enthalpy value, free refrigeration can be carried out by utilizing the outdoor fresh air, when the predicted air conditioner load is larger than the cold quantity brought by the fresh air, the cold quantity proportion born by the outdoor fresh air and the cold source system is distributed according to meteorological conditions, the outlet water temperature of the cold machine is determined, the operation is carried out in a full ventilation mode, and the operation frequency of the blower and the exhaust fan is regulated according to the indoor temperature.
When the indoor CO 2 concentration is less than 1200ppm (settable), if the outdoor enthalpy value is more than the indoor enthalpy value, the internal circulation mode is adopted to operate, the cold energy demand is all borne by the refrigerating unit, the cold energy outlet temperature is set according to load prediction, the air exhaust valve D2, the small fresh air fan and the valve D3 thereof and the fresh air valve D4 thereof are closed, the return air valve D1 is opened, the fresh air fan is closed, the air blower and the exhaust fan are operated, and the operating frequencies of the air blower and the exhaust fan are adjusted according to the indoor temperature and humidity.
(II) transition season
When the outdoor temperature is higher than 12 ℃, the large system is operated in a full ventilation mode, the return air valve D1 is closed, the small fresh air fan and the valve D3 thereof are closed, and the fresh air valve D4 and the exhaust air valve D2 are opened; and adjusting the operation frequency of the blower and the exhaust fan according to the indoor CO 2 concentration.
When the outdoor temperature is 5-12 ℃, the small fresh air unit is closed, the return air valve D1 and the fresh air valve D4 are opened, the exhaust valve D2, the small fresh air fan and the valve D3 thereof are closed, and the operation frequencies of the air blower and the exhaust fan are adjusted according to the indoor carbon dioxide concentration.
(III) winter season
When the indoor CO 2 concentration is less than 1200ppm, the large system does not operate;
when the indoor CO 2 concentration is more than 1200ppm, the small fresh air fan and the valve D3 thereof are opened, the fresh air valve D4 is closed, the exhaust valve D2 is closed, the return air valve D1 is opened, the small fresh air fan is opened, and the operation frequencies of the air feeder and the exhaust fan are adjusted according to the indoor carbon dioxide concentration.
The control method realizes the wind-water linkage control of the operation mode of the air conditioning system of the subway station.
In conclusion, the operation mode of the air conditioning system is determined through indoor and outdoor enthalpy value parameters and carbon dioxide concentration parameters in the subway station, and unmanned automatic control of the air conditioning system of the subway station is realized through the automatic control device, so that the problem of manually setting the operation mode of the air conditioning system of the subway station at present is solved.
Example III
The method comprises the steps of cleaning and processing parameters such as outdoor air temperature, outdoor air humidity, indoor air temperature, indoor air humidity, indoor carbon dioxide concentration, station passenger flow volume, chilled water inlet and outlet temperature of a chiller, chiller energy consumption, chilled water pump operating frequency, chilled water flow volume, chilled water pump energy consumption, cooling water pump operating frequency, cooling water flow volume, cooling water pump energy consumption, cooling tower fan operating frequency, cooling tower energy consumption, two-way valve opening, back-exhaust fan operating frequency, back-exhaust fan energy consumption, back-air valve opening, exhaust valve opening, fresh air machine operating frequency, fresh air machine energy consumption, fresh air valve opening, blower operating frequency, blower energy consumption and the like to obtain continuous, complete and high-quality parameter data.
The control method of the running number of the air-conditioning water system equipment is divided into the following steps:
1. the current ratio of the water chilling unit is more than 95%, and the actual water outlet temperature of chilled water of the water chilling unit is more than the set water outlet temperature of chilled water of the water chilling unit
Starting loading operation of the water chilling unit, and starting a water chilling unit;
2. when the sum of the current ratios of the water chilling units is less than or equal to 100%, and the actual water outlet temperature of chilled water of the water chilling units is less than the set water outlet temperature of chilled water of the water chilling units
Starting load shedding operation of the water chilling unit, and closing one water chilling unit;
3. the cooling tower fan reaches the minimum frequency setting and the water outlet temperature of the cooling tower is less than the set water outlet temperature
Starting load shedding operation of the cooling tower, closing a cooling tower fan and closing a corresponding cooling tower butterfly valve;
4. the fan of the cooling tower reaches the highest frequency setting and the water outlet temperature of the cooling tower is more than the set water outlet temperature
And starting loading operation of the cooling tower, starting a cooling tower fan and starting a corresponding cooling tower butterfly valve.
The reason for setting 95% and 100% as the limit is that the efficiency of the water chilling unit begins to be obviously reduced when the current ratio is 95% according to the maximum limit when the current ratio is 100%, in the industry, the performance of the water chilling unit can be quickly reduced after the water chilling unit reaches 50%, the performance reduction means that when the water chilling unit uses the same electricity, the output result is smaller and smaller, so that in order to maximize the power saving efficiency, a machine needs to be added when the power saving efficiency reaches 95%, and 100% is set because the maximum power consumption cannot exceed 100%, and faults occur after the maximum power consumption exceeds.
After the running number of each device of the air conditioning system is determined, the association rule mining technology is utilized, the load of the air conditioning system of the subway station and the indoor and outdoor environment are taken as constraint conditions, the running parameters of each device are taken as variables, and the overall running energy efficiency ratio of the system is taken as a target, so that the actual running data of the air conditioning system of the subway station are mined;
Digging out the operation parameters of each device of the system under the conditions of different loads, indoor and outdoor environments and the highest energy efficiency;
the method comprises the steps of automatically setting the outlet temperature of chilled water and the outlet temperature of cooling water of a cold water host, automatically setting the temperature difference of water supply and return or the operating frequency of a pressure difference PID regulating chilled water pump and a cooling water pump, automatically setting the operating frequency of a fan of an approximation PID regulating cooling tower, automatically setting the air supply temperature PID regulating two-way valve opening of an air system, and automatically setting the air supply temperature PID regulating the operating frequency of each fan of the air system.
The control method realizes the wind-water linkage control of the operation parameters of the air conditioning system of the subway station.
In conclusion, the data mining technology is fully utilized, the operation mechanism of the air conditioning system is combined, the system energy efficiency ratio is used as a guide, and the on-line optimization of multiple parameters in the air conditioning system of the subway station is realized.
Example IV
Referring to fig. 3, load data of the air conditioning system of the subway station is obtained through calculation of chilled water inlet and outlet temperature and chilled water flow rate of the chiller, and relevant factors affecting the load of the air conditioning system of the subway station are obtained through correlation analysis.
It should be noted that the common factors mainly include: outdoor temperature data, outdoor humidity data, station passenger flow data, outdoor temperature data of a day before the current moment, outdoor humidity data of a day before the current moment, passenger flow data of a week before the current moment, historical load data of 1 hour before the current moment, historical load data of 2 hours before the current moment, historical load data of 3 hours before the current moment, week data of a time dimension, hour data of a time dimension and month data of the time dimension.
And (5) acquiring real-time data and historical data by using the sensor, and constructing a neural network load prediction model.
In combination, the load prediction technology is utilized to accurately predict the load of the air conditioning system of the subway station, and preset parameters of all equipment in the system, so that the self-adaptive prediction wind-water linkage control of the air conditioning system of the subway station is completed, the fine regulation and control and the energy-saving efficient operation of the air conditioning system of the subway station are realized, and the problems of empirical control mode and ineffective utilization of operation data in the prior control mode are solved.
Example five
The wind-water linkage control device comprises a sensing layer, a transmission layer and an application layer.
Constructing a subway station air conditioning system self-adaptive prediction wind-water linkage control device framework by utilizing the internet of things technology;
the bottommost layer of the architecture is a sensing layer composed of all environment parameter sensors and equipment sensors, and is the core of the architecture of the Internet of things;
it should be noted that, the collection of outdoor weather parameters is completed by a temperature and humidity sensor; the acquisition of indoor air parameters is mainly completed by a temperature and humidity sensor and a carbon dioxide concentration meter; the acquisition of the running parameters of the equipment such as the water chilling unit, the water pump, the cooling tower and the like is mainly completed by adding a temperature sensor, a flow rate sensor, a pressure sensor and a parameter record of the equipment at or near the equipment; the air conditioning unit is characterized in that an air speed sensor and a carbon dioxide concentration meter are additionally arranged at the positions of an air supply pipeline, an air return pipeline and an air exhaust pipeline, and collected data signals are converted into 4-20mA current signals or 0-10V voltage signals for transmission.
The transmission layer is mainly used for summarizing and transmitting communication signals of all devices in a field bus mode, and in addition, for devices not easy to arrange communication lines, a wireless network networking technology is adopted for data transmission, and the data are communicated with the controller and the upper computer through a Modbus communication protocol.
The application layer comprises a controller and an upper computer, the self-adaptive prediction wind-water linkage control method of the subway station air conditioning system is built in the controller and the upper computer in a mode of designing a control module, a control cabinet is formed and arranged in an environmental control machine room, and the collected data are calculated, analyzed and control instructions are issued.
Referring to fig. 4, it should be noted that the intelligent management and control system for air conditioner of subway station can communicate with the original BAS system of subway station in real time, and operate by a one-key switching manner.
Example six
Further, performing a cleaning process on the device data, including:
acquiring attribute information of the equipment data, and classifying the equipment data according to the attribute information to obtain a plurality of pieces of different types of sub-equipment data;
generating a data cleaning task according to each piece of sub-equipment data, establishing a first queuing queue according to a plurality of data cleaning tasks, and acquiring a first queue length of the first queuing queue;
Establishing a plurality of data cleaning processes, establishing a second queuing queue according to the plurality of data cleaning processes, and acquiring a second queue length of the second queuing queue;
when the first queue length is determined to be greater than the second queue length, distributing a data cleaning task for each data cleaning process in the second queue, and executing the corresponding data cleaning task based on the data cleaning process; meanwhile, the first queuing queue is corrected to obtain a corrected first queuing queue;
in the process of executing a corresponding data cleaning task by a data cleaning process, extracting features of the data cleaning task and determining feature data;
respectively cleaning the characteristic data by using a plurality of stored preset data cleaning rules, and counting the cleaning rate of each preset data cleaning rule on the characteristic data;
determining a preset data cleaning rule with the maximum cleaning rate as a target cleaning rule;
cleaning the residual data except the characteristic data in the data cleaning task based on the target cleaning rule;
monitoring the cleaning load capacity of each data cleaning process in the process of cleaning the residual data except the characteristic data in the data cleaning task, and establishing a third queuing queue from large to small;
When the cleaning load capacity of the last data cleaning process is smaller than the preset load capacity, dynamically distributing data cleaning tasks from the corrected first queuing queue to the rear preset number of data cleaning processes of the third queuing queue;
repeating the above method until all data cleaning tasks are processed.
The working principle of the technical scheme is as follows: the attribute information includes a type of data.
In the process of executing a corresponding data cleaning task by a data cleaning process, extracting features of the data cleaning task and determining feature data; the feature data is core data capable of representing a data cleansing task, and data representing the data cleansing task can be standardized.
Respectively cleaning the characteristic data by using a plurality of stored preset data cleaning rules, and counting the cleaning rate of each preset data cleaning rule on the characteristic data; determining a preset data cleaning rule with the maximum cleaning rate as a target cleaning rule; the method and the device avoid the technical problems that in the prior art, the cleaning rule is formulated after experimental cleaning is carried out through a large amount of data, and the data cleaning program is written before the data cleaning in the software development process, so that the data cleaning efficiency is low. Based on the scheme, the target cleaning rule suitable for each data cleaning task is accurately determined, and the data cleaning efficiency and accuracy are improved.
Monitoring the cleaning load capacity of each data cleaning process in the process of cleaning the residual data except the characteristic data in the data cleaning task, and establishing a third queuing queue from large to small; when the cleaning load capacity of the last data cleaning process is smaller than the preset load capacity, dynamically distributing data cleaning tasks from the corrected first queuing queue to the rear preset number of data cleaning processes of the third queuing queue; repeating the above method until all data cleaning tasks are processed. The preset number is 3. And the data cleaning tasks are dynamically distributed to different data cleaning processes, so that the data cleaning burden of equipment corresponding to each data cleaning process is conveniently lightened, the data cleaning efficiency is improved, and the utilization rate of the equipment is improved.
The beneficial effects of the technical scheme are that: the target cleaning rule of the corresponding data cleaning task is determined for the equipment data, the data cleaning tasks processed by the data cleaning process are dynamically distributed, the data cleaning efficiency and the data cleaning accuracy are improved, meanwhile, the data cleaning burden of the equipment corresponding to each data cleaning process is also reduced, and the utilization rate of the equipment is improved.
Example seven
Further, after adaptively adjusting and controlling the operation parameters of the system equipment according to the load prediction result, the method further comprises the following steps:
collecting voice signals of regulatory personnel;
performing voice recognition on the voice signal to determine a voice regulation instruction;
setting n extraction indexes;
extracting the voice regulation instruction according to n extraction indexes, and determining an index value corresponding to each extraction index to obtain a vector A to be detected;
calculating the matching degree of the vector to be detected and each control instruction in a voice control database, wherein the voice control database comprises p voice control instructions, each voice control instruction comprises n numerical values corresponding to extracted data, and a data matrix B is formed;
wherein, the liquid crystal display device comprises a liquid crystal display device,the matching degree between the vector A to be detected and the t-th voice control instruction in the voice control database is obtained; />Is the i-th value in the vector A to be detected; />I=1, 2, 3 … … n, t=1, 2, 3 … … p for the ith row and column values in the data matrix B corresponding to the ith value in the vector a to be detected.
And correcting the voice control instruction according to the voice control instruction corresponding to the maximum matching degree to obtain a corrected voice control instruction, and regulating and controlling the operation parameters of the control system equipment according to the corrected voice control instruction.
The technical scheme has the working principle and beneficial effects that: the extraction index comprises an instruction length, an instruction reading time, a keyword of an instruction, a head end identification word, a tail end identification word and the like of the instruction. Extracting the voice regulation instruction according to n extraction indexes, and determining an index value corresponding to each extraction index to obtain a detection vector; calculating the matching degree of the detection vector and each control instruction in the voice control database, correcting the voice control instruction according to the voice control instruction corresponding to the maximum matching degree, obtaining a corrected voice control instruction, and controlling the operation parameters of the control system equipment according to the corrected voice control instruction. The voice control instruction correction method can effectively avoid inaccuracy of voice control instructions sent out due to inaccuracy of recognition of voice signals of users, inaccuracy of voice signals sent out by regulatory personnel and other factors, correct the voice control instructions to obtain corrected voice control instructions, regulate and control operation parameters of regulatory system equipment according to the corrected voice control instructions, and facilitate ensuring that the regulatory personnel can manually regulate the adaptive parameters through voice after adaptively regulating the operation parameters of the regulatory system equipment, realizing voice intelligent control, and ensuring the accuracy of the finally determined operational parameters of the regulatory system equipment. Based on the formula, the matching degree of the detection vector and each control instruction in the voice control database is accurately calculated, so that the voice control instruction corresponding to the maximum matching degree is conveniently determined, the voice regulation instruction is accurately corrected, and the accurate regulation and control of the operation parameters of the regulation and control system equipment is realized.
In summary, through the acquisition of real-time parameters, the operation mode of the air system is determined according to the change of indoor and outdoor enthalpy values, then the related technology of data mining and artificial intelligence is utilized to realize the optimization of multiple parameters of the air conditioning system by utilizing the association rule, the air-water linkage control of the air conditioning system of the subway station is realized, the load prediction technology is utilized on the basis, the self-adaptive prediction air-water linkage control method of the air conditioning system of the subway station is formed, the efficient and energy-saving management and control is realized, the technical support is provided for improving the performance, optimizing the control and saving the energy of the air conditioning system of the subway station, and the current world and national energy-saving policy trend is met.
The foregoing is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art, who is within the scope of the present invention, should be covered by the protection scope of the present invention by making equivalents and modifications to the technical solution and the inventive concept thereof.
Claims (8)
1. A self-adaptive prediction wind-water linkage control method for an air conditioning system of a subway station is characterized by comprising the following steps of
Acquiring parameters of indoor and outdoor temperatures, indoor and outdoor humidity, indoor carbon dioxide concentration and station passenger flow;
Acquiring operation parameters, load parameters and energy consumption parameters of all equipment of an air conditioning system of a subway station;
calculating to obtain indoor and outdoor air enthalpy values, designing a control method according to the enthalpy values and carbon dioxide parameters, and determining an air conditioning system operation mode;
the automatic switching of the system operation mode is realized by utilizing the wind-water linkage control device;
cleaning the equipment data, and obtaining the optimal running number and parameters of the equipment by using an association rule mining method;
optimal control of system equipment is realized by utilizing the wind-water linkage control device;
cleaning the load data, and obtaining influence parameters of load prediction by utilizing correlation analysis;
a neural network is established to obtain the predicted cooling load of the air conditioning system of the subway station;
the wind-water linkage control device is utilized to automatically regulate and control the system operation mode, and then the system equipment operation parameters are adaptively regulated and controlled according to the load prediction result, so that a subway station air-conditioning system adaptive prediction wind-water linkage control method is formed;
wherein the wind-water linkage control device comprises
The sensing layer is composed of environment parameter sensors and equipment sensors;
the outdoor weather parameter acquisition is completed by a temperature and humidity sensor; the collection of indoor air parameters is completed by a temperature and humidity sensor and a carbon dioxide concentration meter;
The collection of the running parameters of the water chilling unit, the water pump and the cooling tower is completed by adding a temperature sensor, a flow rate sensor, a pressure sensor and a parameter record of the equipment at or near the equipment;
the air conditioning unit is provided with an air speed sensor and a carbon dioxide concentration meter at the positions of the air supply pipeline, the air return pipeline and the air exhaust pipeline;
converting the collected data signals into 4-20mA current signals or 0-10V voltage signals for transmission;
the transmission layer is used for summarizing and transmitting communication signals of all the devices through a field bus, and for the devices not easy to arrange communication lines, a wireless network networking technology is adopted for data transmission, and the data are communicated with the controller and the upper computer through a Modbus communication protocol;
the application layer comprises a controller and an upper computer, the self-adaptive prediction wind-water linkage control method of the subway station air conditioning system is arranged in the controller and the upper computer in a mode of designing a control module, a control cabinet is formed and arranged in an environmental control machine room, and the collected data are calculated, analyzed and control instructions are issued.
2. The adaptive prediction wind-water linkage control method for the air conditioning system of the subway station according to claim 1, wherein the control method is as follows in the refrigerating season
When indoorConcentration of>1200ppm, if outdoor enthalpy value>When the indoor enthalpy value is needed to be cooled, a small fresh air operation mode is adopted;
the small fresh air fan and the valve D3 thereof are opened, the fresh air valve D4 is closed, the return air valve D1 is opened, and the exhaust valve D2 is closed;
when the outdoor enthalpy value is more than the indoor enthalpy value, cooling by using the cooling capacity of the fresh air;
when the predicted air conditioner load is smaller than the cold quantity caused by fresh air, the cold source system stops running and adopts a full ventilation running mode;
the air return valve D1 is closed, the small fresh air fan and the valve D3 thereof are closed, the fresh air valve D4 and the exhaust valve D2 are opened, and the operation frequencies of the air blower and the exhaust fan are adjusted according to the indoor temperature;
when the outdoor enthalpy value is less than the indoor enthalpy value, refrigerating by using outdoor fresh air;
when the predicted air conditioner load is larger than the cold quantity brought by the fresh air, distributing the respective cold quantity proportion born by the outdoor fresh air and the cold source system according to meteorological conditions, and determining the water outlet temperature of the cold machine;
the operation is performed in a full ventilation mode, and the operation frequency of the blower and the exhaust fan is adjusted according to the indoor temperature;
when indoorConcentration of<1200ppm, if outdoor enthalpy value>When the indoor enthalpy value is obtained, an internal circulation mode is adopted for operation;
the cold energy demand is all borne by the refrigerating unit, and the water outlet temperature of the refrigerating unit is set according to load prediction;
The exhaust valve D2, the small fresh air fan, the valve D3 and the fresh air valve D4 are closed, the return air valve D1 is opened, the blower and the exhaust fan are operated, and the operating frequency of the blower and the exhaust fan is adjusted according to the indoor temperature and humidity.
3. The adaptive prediction wind-water linkage control method for the air conditioning system of the subway station according to claim 2, wherein the control method is as follows in transition season
When the outdoor temperature is higher than 12 ℃, the large system is operated in a full ventilation mode;
the return air valve D1 is closed, the small fresh air fan and the valve D3 thereof are closed, and the fresh air valve D4 and the exhaust valve D2 are opened;
adjusting the operation frequency of the blower and the exhaust fan according to the indoor carbon dioxide concentration;
when the outdoor temperature is 5-12 ℃, the return air valve D1 and the fresh air valve D4 are opened, the exhaust air valve D2, the small fresh air fan and the valve D3 thereof are closed, and the operation frequencies of the air blower and the exhaust fan are adjusted according to the indoor carbon dioxide concentration;
in winter, the control method is that
When indoorConcentration of<At 1200ppm, the large system is not running;
when indoorConcentration of>When 1200ppm is reached, the small fresh air fan and the valve D3 thereof are opened, the fresh air valve D4 is closed, the exhaust valve D2 is closed, the return air valve D1 is opened, and the operation frequency of the air blower and the exhaust fan is regulated according to the indoor carbon dioxide concentration.
4. The method for adaptively predicting wind-water linkage control of the air conditioning system of the subway station according to claim 1, wherein,
cleaning the equipment data to obtain continuous, complete and high-quality parameter data;
the control method for designing the running number of the air-conditioning water system equipment according to the parameter data comprises the following steps of
When the current ratio of the water chilling unit is more than 95% and the actual outlet water temperature of chilled water of the water chilling unit is more than the set outlet water temperature of chilled water of the water chilling unit, starting loading operation of the water chilling unit, and additionally starting a water chilling unit;
when the sum of the current ratios of the water chilling units is less than or equal to 100 percent and the actual water outlet temperature of chilled water of the water chilling units is less than the set water outlet temperature of chilled water of the water chilling units, starting load shedding operation of the water chilling units, and closing one water chilling unit;
when the cooling tower fan reaches the minimum frequency setting and the cooling tower water outlet temperature is less than the set water outlet temperature, starting load shedding operation of the cooling tower, closing the cooling tower fan and closing a corresponding cooling tower butterfly valve;
when the cooling tower fan reaches the highest frequency setting and the cooling tower outlet water temperature is more than the set outlet water temperature, loading operation of the cooling tower is started, the cooling tower fan is started, and a corresponding cooling tower butterfly valve is started.
5. The method for adaptively predicting wind-water linkage control of the air conditioning system of the subway station according to claim 1, wherein,
automatically setting the chilled water outlet temperature and the cooling water outlet temperature of the cold water main machine;
automatically setting a temperature difference or a pressure difference of water supply and return, and then adjusting the operating frequencies of the chilled water pump and the cooling water pump through a PID;
automatically setting approximation degree, and then adjusting the operating frequency of a cooling tower fan through PID;
automatically setting the air supply temperature of an air system, and then adjusting the opening of a two-way valve through PID;
and automatically setting the air supply temperature of the air system, and then adjusting the operating frequency of each fan through PID.
6. The method for adaptively predicting wind-water linkage control of the air conditioning system of the subway station according to claim 1, wherein,
calculating the water inlet and outlet temperature of chilled water of the water chiller and the flow rate of the chilled water to obtain load data of an air conditioning system of the subway station;
obtaining relevant factors influencing the load of the air conditioning system of the subway station through correlation analysis;
and (5) acquiring real-time data and historical data by using the sensor, and constructing a neural network load prediction model.
7. The adaptive predictive wind-water linkage control method for an air conditioning system of a subway station according to claim 1, wherein the cleaning process of the equipment data comprises:
Acquiring attribute information of the equipment data, and classifying the equipment data according to the attribute information to obtain a plurality of pieces of different types of sub-equipment data;
generating a data cleaning task according to each piece of sub-equipment data, establishing a first queuing queue according to a plurality of data cleaning tasks, and acquiring a first queue length of the first queuing queue;
establishing a plurality of data cleaning processes, establishing a second queuing queue according to the plurality of data cleaning processes, and acquiring a second queue length of the second queuing queue;
when the first queue length is determined to be greater than the second queue length, distributing a data cleaning task for each data cleaning process in the second queue, and executing the corresponding data cleaning task based on the data cleaning process; meanwhile, the first queuing queue is corrected to obtain a corrected first queuing queue;
in the process of executing a corresponding data cleaning task by a data cleaning process, extracting features of the data cleaning task and determining feature data;
respectively cleaning the characteristic data by using a plurality of stored preset data cleaning rules, and counting the cleaning rate of each preset data cleaning rule on the characteristic data;
Determining a preset data cleaning rule with the maximum cleaning rate as a target cleaning rule;
cleaning the residual data except the characteristic data in the data cleaning task based on the target cleaning rule;
monitoring the cleaning load capacity of each data cleaning process in the process of cleaning the residual data except the characteristic data in the data cleaning task, and establishing a third queuing queue from large to small;
when the cleaning load capacity of the last data cleaning process is smaller than the preset load capacity, dynamically distributing data cleaning tasks from the corrected first queuing queue to the rear preset number of data cleaning processes of the third queuing queue;
repeating the above method until all data cleaning tasks are processed.
8. The method for adaptively predicting wind-water linkage control of an air conditioning system of a subway station according to claim 1, further comprising, after adaptively adjusting and controlling the operation parameters of the system equipment according to the load prediction result:
collecting voice signals of regulatory personnel;
performing voice recognition on the voice signal to determine a voice regulation instruction;
setting n extraction indexes;
extracting the voice regulation instruction according to n extraction indexes, and determining an index value corresponding to each extraction index to obtain a vector A to be detected;
Calculating the matching degree of the vector to be detected and each control instruction in a voice control database, wherein the voice control database comprises p voice control instructions, each voice control instruction comprises n numerical values corresponding to extracted data, and a data matrix B is formed;wherein (1)>The matching degree between the vector A to be detected and the t-th voice control instruction in the voice control database is obtained; />Is the i-th value in the vector A to be detected; />I=1, 2, 3 … … n, t=1, 2, 3 … … p for the values of the ith row and column in the data matrix B corresponding to the ith value in the vector a to be detected;
and correcting the voice control instruction according to the voice control instruction corresponding to the maximum matching degree to obtain a corrected voice control instruction, and regulating and controlling the operation parameters of the system equipment according to the corrected voice control instruction.
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