CN109899935A - A kind of rail traffic refrigeration system and its intelligent adjusting method, device - Google Patents
A kind of rail traffic refrigeration system and its intelligent adjusting method, device Download PDFInfo
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Abstract
It includes: the workload demand estimated in the following preset period of time back court that the present invention, which discloses a kind of rail traffic refrigeration system and its intelligent adjusting method, device, method, determines the workload demand deviation after preset period of time and in current time；According to the operating parameter of workload demand bias adjustment refrigeration system；According to the return air temperature of the refrigeration system after target temperature and adjusting operating parameter, continue to adjust operating parameter, to meet the workload demand in the following preset period of time back court.As a result, when flow of the people changes rapidly, the operating parameter of refrigeration system can be adjusted in time according to workload demand deviation, to reach while guaranteeing efficiency, improve the purpose of users'comfort.And after according to workload demand bias adjustment operating parameter, can continue to adjust according to return air temperature, can solve refrigeration system due to apart from user area farther out caused by delay issue so that adjust it is more accurate.
Description
Technical field
The present invention relates to technical field of rail traffic, in particular to a kind of rail traffic refrigeration system and its intelligence
Adjusting method, device.
Background technique
Currently, the large-scale track traffic hub development of domestic city is rapid, using subway station, railway station as the rail of representative
The construction of the various auxiliary facilities of road transport hub is also particularly important, and wherein, the selection of refrigeration system is even more the most important thing.But
It is that the demand of the refrigeration system of rail traffic has certain difference compared to the Central air-conditioning unit of other stable operations.Track
In traffic, flow of the people timesharing extramalization can cause the acute variation of refrigeration duty to not only result in electric power if can not adjust in time
Waste reduces efficiency, can also adversely affect to the comfort level of passenger.And consider influence of the noise to passenger, usual feelings
Under condition, air-conditioner set can be placed in the place far from client area by track traffic hub, and resulting delay effect can be into one
Step influences the real-time modulability of refrigeration system.
For in the related technology, the regulative mode of the refrigeration system in rail traffic is relatively fixed, lower, the Yi Zao of intelligence
The problem of at energy waste and influencing users'comfort, currently no effective solution has been proposed.
Summary of the invention
To solve in the related technology, the regulative mode of the refrigeration system in rail traffic is relatively fixed, and intelligence is lower, easily
The problem of causing energy waste and influencing users'comfort, the embodiment of the present invention provide a kind of Rail Transit System and its intelligent tune
Save method, apparatus.
In a first aspect, the embodiment of the present invention provides a kind of intelligent adjusting method of rail traffic refrigeration system, the method
Include:
Estimate the workload demand in the following preset period of time back court, determine after the preset period of time with it is negative in current time
Lotus demand disruption；
According to the operating parameter of refrigeration system described in the workload demand bias adjustment；
According to the return air temperature of the refrigeration system after target temperature and the adjusting operating parameter, continue described in adjusting
Operating parameter, to meet the workload demand in the following preset period of time back court.
Further, the workload demand estimated in the following preset period of time back court includes:
Determine the total number of persons in the current time and the workload demand in the current time；
Estimate personnel's amount of having a net increase of of the following preset period of time；
It is estimated according to the total number of persons, the workload demand in the current time, personnel's amount of having a net increase of following pre-
If the workload demand in period back court.
Further, pre- according to the total number of persons, the workload demand in the current time, personnel's amount of having a net increase of
Estimate the workload demand in the following preset period of time back court, realized by following formula:
φ 1=φ * (Q1/Q)；
Wherein, φ 1 is the workload demand in the following preset period of time back court, and φ is the load in the current time
Demand, Q1 are the total number of persons after the following preset period of time, and Q is the total number of persons at the current time, Q1=Q+ Δ Q, Δ Q
For personnel's amount of having a net increase of.
Further, the net increment Delta Q of the personnel is determined by following formula:
Δ Q=α * Δ Qp+ β * Δ Qm- Δ Qc；
Wherein, α, β are correction factor, alpha+beta=1, Δ Qp be the ticketing system of the rail traffic count it is described not
Carry out the total number of persons entered the station in preset period of time, Δ Qm be in past preset period of time that the access control system of the rail traffic counts into
The total number of persons to stand, Δ Qc be website regular bus arrive at a station time of departure management system statistics in the following preset period of time by bus
The total number of persons left.
Further, it is determined according to the workload demand inclined with the workload demand in current time after the preset period of time
Difference is determined by following formula:
Δ φ=φ 1- φ；
Wherein, Δ φ is the workload demand deviation, and φ 1 is the workload demand in the following preset period of time back court, φ
For the workload demand in the current time.
Further, the operating parameter of the refrigeration system according to the workload demand bias adjustment includes:
Judge whether the workload demand deviation is zero；
If it is, keeping current operating parameter constant；
If it is not, then being modified to the workload demand deviation；
According to operating parameter described in revised workload demand bias adjustment.
Further, if it is not, then being modified to the workload demand deviation, comprising:
Determine the heat loss of the air supply duct of the refrigeration system under the current time；
The workload demand deviation is modified according to the heat loss, the positive and negative situation of the workload demand deviation.
Further, according to the heat loss, the positive and negative situation of the workload demand deviation to the workload demand deviation
It is modified, comprising:
If the workload demand deviation is positive value, revised workload demand deviation=Δ φ+qs* γ；
If the workload demand deviation is negative value, revised workload demand deviation=Δ φ-qs* γ；
Wherein, Δ φ is the workload demand deviation, qs is the heat loss, γ is correction factor.
Further, the operating parameter includes at least: compressor operating frequency, rotation speed of fan, according to revised negative
Lotus demand disruption adjusts the operating parameter
When revised workload demand deviation is positive value, improves the running frequency of the compressor, increases the blower
Revolving speed；
When revised workload demand deviation is negative value, reduces the running frequency of the compressor, reduces the blower
Revolving speed.
Further, the operating parameter includes at least compressor operating frequency, rotation speed of fan, according to target temperature and adjusts
The return air temperature of the refrigeration system after saving the operating parameter continues to adjust the operating parameter, comprising:
Determine target temperature deviation and temperature drop rate；
Judge whether the target temperature deviation and the temperature drop rate meet the first preset condition or the second default item
Part；
If meeting first preset condition, improves the running frequency of compressor, increases rotation speed of fan；
If meeting second preset condition, reduces the running frequency of compressor, reduces rotation speed of fan；
If being both unsatisfactory for first preset condition, it is also unsatisfactory for second preset condition, then keeps compressor
Current operation frequency is constant, current rotation speed of fan is constant.
Further, first preset condition are as follows: the target temperature deviation is greater than preset value and the temperature drop rate
More than or equal to 0；
Second preset condition are as follows: the target temperature difference is less than the opposite number of the preset value, and the temperature drop rate
Less than or equal to 0；
Wherein, the preset value is positive number.
Further, the target temperature deviation delta T is determined by following formula:
Δ T=T1-T；
The temperature drop rate dT is determined by following formula:
DT=T1-T2；
Wherein, T1 is the return air temperature, T is the target temperature, T2 is return air temperature before preset duration.
Second aspect, the embodiment of the present invention provide a kind of intelligent regulating device of rail traffic refrigeration system, described device
For executing method described in first aspect, described device includes:
Module is estimated, for estimating the workload demand in the following preset period of time back court；
Determining module, for estimated according to the workload demand that module is estimated determine after the preset period of time with work as
Workload demand deviation in the preceding moment；
Adjustment module, the operating parameter for the refrigeration system according to the workload demand bias adjustment；
The determining module is also used to returning according to the refrigeration system after target temperature and the adjusting operating parameter
Air temperature continues to adjust the operating parameter, to meet the workload demand in the following preset period of time back court.
The third aspect, the embodiment of the present invention provide a kind of rail traffic refrigeration system, the rail traffic refrigeration system packet
Device described in second aspect is included,
The rail traffic refrigeration system is the direct refrigeration-type magnetic suspension air-conditioner set of rail traffic water cooling.
It applies the technical scheme of the present invention, the workload demand in the following preset period of time back court is estimated, after determining preset period of time
And the workload demand deviation in current time；According to the operating parameter of workload demand bias adjustment refrigeration system；According to target
Temperature and the return air temperature for adjusting the refrigeration system after operating parameter continue to adjust operating parameter, to meet the following preset period of time
Workload demand in back court.As a result, when flow of the people changes rapidly, refrigeration system can be adjusted in time according to workload demand deviation
Operating parameter improves the purpose of users'comfort to reach while guaranteeing efficiency.And according to workload demand bias adjustment
After operating parameter, can continue to adjust according to return air temperature, can solve refrigeration system due to apart from user area farther out and
Caused delay issue, so that adjusting more accurate.
Detailed description of the invention
Fig. 1 is a kind of flow chart of the intelligent adjusting method of rail traffic refrigeration system according to an embodiment of the present invention；
Fig. 2 is a kind of flow chart of the intelligent adjusting method of rail traffic refrigeration system according to an embodiment of the present invention；
Fig. 3 is a kind of flow chart of the intelligent adjusting method of rail traffic refrigeration system according to an embodiment of the present invention；
Fig. 4 is a kind of flow chart of the intelligent adjusting method of rail traffic refrigeration system according to an embodiment of the present invention；
Fig. 5 is a kind of flow chart of the intelligent adjusting method of rail traffic refrigeration system according to an embodiment of the present invention；
Fig. 6 is the structural representation of a kind of rail traffic refrigeration system and place place relationship according to an embodiment of the present invention
Figure；
Fig. 7 is a kind of flow chart of the intelligent adjusting method of rail traffic refrigeration system according to an embodiment of the present invention；
Fig. 8 is a kind of flow chart of the intelligent adjusting method of rail traffic refrigeration system according to an embodiment of the present invention；
Fig. 9 is a kind of structural block diagram of rail traffic refrigeration system according to an embodiment of the present invention.
Specific embodiment
Present invention is further described in detail in the following with reference to the drawings and specific embodiments, it should be understood that described herein
Specific embodiment be only used to explain the present invention, be not intended to limit the present invention.
In subsequent description, it is only using the suffix for indicating such as " module ", " component " or " unit " of element
Be conducive to explanation of the invention, itself there is no a specific meaning.Therefore, " module ", " component " or " unit " can mix
Ground uses.
It should be noted that, in this document, the terms "include", "comprise" or its any other variant are intended to non-row
His property includes, so that the process, method, article or the device that include a series of elements not only include those elements, and
And further include other elements that are not explicitly listed, or further include for this process, method, article or device institute it is intrinsic
Element.In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that including being somebody's turn to do
There is also other identical elements in the process, method of element, article or device.
In order to solve in the related technology, the regulative mode of the refrigeration system in rail traffic is relatively fixed, and intelligence is lower,
The problem of easily causing energy waste and influencing users'comfort, the embodiment of the present invention provides a kind of intelligence of rail traffic refrigeration system
It is adjustable method, as shown in Figure 1, method includes:
Step S101, the workload demand in the following preset period of time back court is estimated；
Step S102, the workload demand deviation after preset period of time and in current time is determined according to workload demand；
Step S103, according to the operating parameter of workload demand bias adjustment refrigeration system；
Step S104, the return air temperature of the refrigeration system after adjusting operating parameter is determined；
Step S105, continued to adjust operating parameter according to return air temperature and target temperature, after meeting the following preset period of time
Workload demand in.
As a result, when flow of the people changes rapidly, the operating parameter of refrigeration system can be adjusted in time according to workload demand deviation,
Rail traffic refrigeration system can be improved to the adaptability of short time internal loading acute variation, promote rail traffic refrigeration system and be directed to
The adjusting real-time of client area load variations, to improve the comfort level of user, and according to load need while guaranteeing efficiency
After seeking bias adjustment operating parameter, it can carry out continuing to adjust according to return air temperature, refrigeration system can be solved due to apart from user
Region farther out caused by delay issue so that adjust it is more accurate.
It will be appreciated that rail traffic refrigeration system can be air conditioner, for example, the direct refrigeration-type magnetcisuspension of rail traffic water cooling
Floating air-conditioner set.Regulative mode can be adjusted in real time, can also adjust at times, periodic adjustment or it is intermittent adjust, can root
It is configured according to actual conditions, the present invention is without limitation.
In one possible implementation, the operating parameter of refrigeration system can be frequency, the rotation speed of fan of compressor
Deng, and return air temperature can be measured according to devices such as temperature sensors.And according to workload demand determine after preset period of time with it is current when
The workload demand deviation in field is carved, is determined by following formula: Δ φ=φ 1- φ；Wherein, Δ φ is load demand disruption, φ
1 is the workload demand in the following preset period of time back court, and φ is the workload demand in current time.
In one possible implementation, as shown in Fig. 2, step S101, estimate it is negative in the following preset period of time back court
Lotus demand includes:
Step S201, the total number of persons in current time and the workload demand in current time are determined；
Step S202, personnel's amount of having a net increase of of the following preset period of time is estimated；
Step S203, when estimating following default according to total number of persons, the workload demand in current time, personnel's amount of having a net increase of
Workload demand in section back court.
Wherein, the workload demand in preset period of time back court is determined by following formula:
φ 1=φ * (Q1/Q)；Wherein, φ 1 is the workload demand in the following preset period of time back court, and φ is current time
Interior workload demand, Q1 are the total number of persons after the following preset period of time, and Q is the total number of persons at current time, Q1=Q+ Δ Q, Δ Q
For personnel's amount of having a net increase of.
It should be noted that workload demand refers in a certain given time field the load of (railway station, subway station).Time point can
To be arbitrarily designated.Refrigeration duty is according to heating equipment in the external world incoming heat (by way of thermally conductive, convection current, radiation), station
It is determined with personnel amount in station.Heating equipment includes lighting apparatus, working equipment (such as computer, display screen) in station
It is estimated Deng, this partial heat needs according to equipment power dissipation.Since this partial heat changes over time very little, can be set to
Constant term.The incoming heat in the external world can change in one day, but heat stagnation is larger, and variation is slower, be for refrigeration
The influence that real-time of uniting is adjusted is little.The principal element for influencing workload demand is flow of personnel, it is therefore desirable to according to personnel amount
Calculated load demand (one workload demand can be calculated by constant, can refer to 100~300W/ people value).
Wherein, the net increment Delta Q of personnel is determined by following formula:
Δ Q=α * Δ Qp+ β * Δ Qm- Δ Qc；
Wherein, α, β are correction factor, and alpha+beta=1, Δ Qp is when presetting in the future that the ticketing system of rail traffic counts
The total number of persons entered the station in section, the personnel that Δ Qm enters the station in the past preset period of time for the access control system statistics of rail traffic are total
Number, Δ Qc be website regular bus arrive at a station time of departure management system statistics the personnel that leave are total by bus in the following preset period of time
Number.
It will be appreciated that compared to other places, as railway station, this large-scale Rail Transit System website of subway station have
Perfect ticketing system, access control system and website regular bus arrives at a station time of departure management system, can be by these systems and refrigeration
System links, and reaches prediction flow of the people variation, and carry out anticipation adjusting according to flow of the people situation of change auxiliary cooling system, mentions
The technical effect of top adjustment real-time.
The arrive at a station statistical conditions of time of departure management system of ticketing system, access control system, website regular bus are done briefly below
Explanation.
Ticketing system can carry out demographics by counter, since the mode that enters the station that current large-scale station is carried out is mostly real
Name, which takes ticket, to enter the station, therefore, according to the record of ticketing system it is estimated that the number upper limit to enter the station in the Δ t time.For example, current
Moment is t1, is t2 at the time of after the Δ t time, then entering the people at station in [t1, t2] time range, riding time is substantially
Positioned at [t1+ Δ t1, t2+ Δ t2], the time of departure can be found in this area according to ticketing system, i.e., by total number of persons by bus
ΔQp.Δ t1 herein indicates that the minimum time that passenger arrives in good time for your train, Δ t2 indicate the maximum time that passenger arrives in good time for your train, this two
A parameter can carry out sampling statistics according to station historical record, and mean value is taken to be configured, can also be according to lasting record
Carry out dynamic adjustment.
Access control system is in statistical number of person, it is assumed that in certain period of time, flow of the people is uniform.Can be by gate inhibition
Completely count total number of persons.It should be noted that although individually using ticketing system and access control system come carry out can by the way of demographics
Row, but error is larger.Therefore, it total number of persons and actually enters the station the error of number to reduce entering the station of estimating, using Δ Q=
α * Δ Qp+ β * Δ Qm- Δ Qc formula combines two kinds of statisticals, is modified to mutual statistical result, to improve number
Determining accuracy.In this formula, α, β are correction factor, and correction factor can be determined according to the actual situation, due to logical
In normal situation, the mode of the statistical number of person that access control system uses is, when personnel are by safety check and gate inhibition, carries out according to counter
Statistics, error is relatively small, then β value can be larger, and the value of α is between (0.2~0.5), alpha+beta=1.
Website regular bus arrives at a station time of departure management system when counting number leaving from station, and can be ignored can not be by due to certain reason
When personnel by bus, then in the following preset period of time, the total number of persons that leaves, which is equal to get on the bus and take in this period in our station, to be owned
By our station or the starting station the train number of our station number.It is denoted as Δ Qc.
In one possible implementation, as shown in figure 3, step S103, system of being freezed according to workload demand bias adjustment
The operating parameter of system includes:
Step S301, judge whether workload demand deviation is zero；If so, thening follow the steps S302；If it is not, then executing
Step S303；
Step S302, the current operating parameter of holding is constant；
Step S303, workload demand deviation is modified；
Step S304, according to revised workload demand bias adjustment operating parameter.
It will be appreciated that, without being adjusted, in refrigeration duty deviation non-zero, then may be used when workload demand deviation is zero
After being modified to workload demand deviation, further according to revised refrigeration duty bias adjustment operating parameter, i.e., sent out to refrigeration system
Adjustment signal is sent, to improve the accuracy adjusted.
In one possible implementation, it as shown in figure 4, step S303, being modified to workload demand deviation, wraps
It includes:
Step S3031, the heat loss of the air supply duct of refrigeration system under current time is determined；
Step S3032, workload demand deviation is modified according to heat loss, the positive and negative situation of workload demand deviation.
Wherein, workload demand deviation is modified according to heat loss, the positive and negative situation of workload demand deviation, comprising: if
Workload demand deviation is positive value, then revised workload demand deviation=Δ φ+qs* γ；If workload demand deviation is negative value,
Revised workload demand deviation=Δ φ-qs* γ；Wherein, Δ φ is load demand disruption, qs is heat loss, γ is amendment
Coefficient.
Heat loss qs=m*cp* (T3-T4), wherein m is air quantity (m3/s), and cp is the specific heat at constant pressure (J/ (kg* of air
DEG C)), T4 is the air outlet temperature of refrigeration system, T3 is that (structural block diagram shown in fig. 6 can be more for air outlet temperature inside station
Clearly show the point of T3, T4).
In Δ φ > 0, unit load increases, when ambient temperature is constant (high temperature), it is desirable that the leaving air temp of refrigeration system
It reducing, then the temperature in wind pipe is also required to reduce, and wind pipe heat loss can be increased with it since pipe internal-external temperature difference becomes larger,
Therefore the value of appropriate great load demand disruption is needed.That is Δ φ=Δ φ+qs* γ (γ=0.05~0.1)
In Δ φ < 0, unit load increases, when ambient temperature is constant (high temperature), it is desirable that and air conditioning exhausting temperature increases,
Then temperature is also required to increase in wind pipe, and wind pipe heat loss can reduce therewith since pipe internal-external temperature difference becomes smaller, can
Suitably to reduce the value of workload demand deviation.That is Δ φ=Δ φ-qs* γ (γ=0.05~0.1).
It, then can be according to heat loss to negative since the positive and negative situation of workload demand deviation can cause the variation of heat dissipation of pipeline intensity
Lotus demand disruption is further corrected, to improve the accuracy for determining workload demand deviation.
In one possible implementation, operating parameter includes at least: compressor operating frequency, rotation speed of fan, according to
Revised workload demand bias adjustment operating parameter includes: to improve compression when revised workload demand deviation is positive value
The running frequency of machine increases rotation speed of fan；When revised workload demand deviation is negative value, the operation frequency of compressor is reduced
Rate reduces rotation speed of fan.
It will be appreciated that above-mentioned regulative mode is that load prejudges adjustment mechanism, it can be in the half of the following preset period of time
Interior completion, and the remaining time can further be adjusted refrigeration system according to return air temperature, so that when future is default
Refrigerating capacity/heating capacity of Duan Hou, refrigeration system are more nearly true workload demand, with further while guaranteeing efficiency,
Improve the comfort level of user.Following implementations do brief introduction to the mode being adjusted according to return air temperature.
In one possible implementation, as shown in figure 5, operating parameter includes at least compressor operating frequency, blower
Revolving speed, then step S105, according to return air temperature and target temperature continue adjust operating parameter, include: to meet workload demand
Step S501, target temperature deviation and temperature drop rate are determined；
Step S502, judge whether target temperature deviation and temperature drop rate meet the first preset condition or the second default item
Part；
If step S503, meeting the first preset condition, improves the running frequency of compressor, increases rotation speed of fan；
If step S504, meeting the second preset condition, reduces the running frequency of compressor, reduces rotation speed of fan；
If step S505, being both unsatisfactory for the first preset condition, it is also unsatisfactory for the second preset condition, then keeps compressor
Current operation frequency is constant, current rotation speed of fan is constant.
In one possible implementation, the first preset condition are as follows: target temperature deviation is greater than preset value and temperature drop speed
Rate is greater than or equal to 0；Second preset condition are as follows: target temperature difference is less than the opposite number of preset value, and temperature drop rate is less than or equal to
0；Wherein, preset value is positive number.Target temperature deviation delta T is determined by following formula: Δ T=T1-T；Temperature drop rate dT passes through
Following formula determines: dT=T1-T2；Wherein, T1 is return air temperature, T is target temperature, T2 is return air temperature before preset duration
Degree.Structural block diagram shown in Fig. 6 clearer can show the point of T1.Wherein, preset value can be 2, and preset duration can be
60S.It will be appreciated that when the frequency of compressor increases or decreases, amplitude all can be Δ F, Δ F can be with 1~5Hz of value, Δ F
Value can be configured according to station space size.
It will be appreciated that regulative mode can be periodic adjustment, after the completion of this is adjusted according to return air temperature,
It can continue to be adjusted by return air temperature and target temperature deviation in next cycle.If being adjusted by return air temperature
Mode and when conflicting in such a way that the workload demand deviation estimated is adjusted, can preferentially be carried out by the workload demand deviation estimated
It adjusts.
Fig. 7 shows a kind of process of the intelligent adjusting method of rail traffic refrigeration system according to an embodiment of the present invention
Figure, as shown in fig. 7, this method comprises:
Step S701, known air-conditioner set air outlet temperature T1 and station inside air outlet temperature T2 and the load of anticipation
Deviation delta φ；
Step S702, the heat loss of t moment air supply duct: qs=m*cp* (T2-T1) is calculated；
Step S703, Δ φ > 0? if so, S704 is thened follow the steps, if not, thening follow the steps S705；
Step S704, Δ φ=Δ φ+qs* γ；Step S708 is executed afterwards；
Step S705, Δ φ < 0? if so, S706 is thened follow the steps, if not, thening follow the steps S707；
Step S706, Δ φ=Δ φ-qs* γ；Step S708 is executed afterwards；
Step S707, rear to execute step S709 without adjusting；
Step S708, compressor frequency F is adjusted, load deviation demand is met；
Step S709, load deviation control is exited.
Workload demand deviation can further be corrected according to heat loss as a result, determine that workload demand is inclined to improve
The accuracy of difference.
Fig. 8 shows a kind of process of the intelligent adjusting method of rail traffic refrigeration system according to an embodiment of the present invention
Figure, as shown in figure 8, this method comprises:
Step S801, current return air temperature detected value T1 is recorded and transferred, return air temperature detected value T2 (60s before 60s is transferred
Before), transfer setting temperature T in current station；
Step S802, target temperature deviation delta T=T1-T；Temperature drop rate dT=T1-T2 (before 60s)；
Step S803, Δ T > 2 and dT >=0? if so, S804 is thened follow the steps, if not, thening follow the steps S805；
Step S804, F=F+ Δ F；Step S808 is executed afterwards；
Step S805, Δ T < -2 and dT≤0? if so, thening follow the steps S806；If not, thening follow the steps S807；
Step S806, F=F- Δ F；Step S808 is executed afterwards；
Step S807, compressor frequency is constant；Step S809 is executed afterwards；
Step S808, compressor frequency, which is adjusted, completes；
Step S809, temperature deviation control is exited.
It can further be adjusted according to return air temperature as a result, so that after the following preset period of time, the refrigerating capacity of refrigeration system/
Heating capacity is more nearly true workload demand, further while guaranteeing efficiency, to improve the comfort level of user.
Fig. 9 shows a kind of intelligent regulating device of rail traffic refrigeration system according to an embodiment of the present invention, and device is used
In execution method shown in FIG. 1, device includes:
Module 901 is estimated, for estimating the workload demand in the following preset period of time back court；
Determining module 902, for according to estimate the workload demand that module 901 is estimated determine after preset period of time with current time
Workload demand deviation in；
Adjustment module 903, for the operating parameter according to workload demand bias adjustment refrigeration system；
Determining module 902 is also used to determine the return air temperature of the refrigeration system after adjusting operating parameter；
Adjustment module 903 is also used to be continued according to return air temperature and target temperature to adjust operating parameter, following pre- to meet
If the workload demand in period back court.
As a result, when flow of the people changes rapidly, the operating parameter of refrigeration system can be adjusted in time according to workload demand deviation,
While guaranteeing efficiency, to improve the comfort level of user, and after according to workload demand bias adjustment operating parameter, Ji Kegen
Carry out continuing to adjust according to return air temperature, can solve refrigeration system due to apart from user area farther out caused by delay issue, make
It must adjust more accurate.
The embodiment of the present invention also provides a kind of rail traffic refrigeration system, and rail traffic refrigeration system includes shown in Fig. 7
Device, rail traffic refrigeration system are the direct refrigeration-type magnetic suspension air-conditioner set of rail traffic water cooling.
The serial number of the above embodiments of the invention is only for description, does not represent the advantages or disadvantages of the embodiments.
Through the above description of the embodiments, those skilled in the art can be understood that above-described embodiment side
Method can be realized by means of software and necessary general hardware platform, naturally it is also possible to by hardware, but in many cases
The former is more preferably embodiment.Based on this understanding, technical solution of the present invention substantially in other words does the prior art
The part contributed out can be embodied in the form of software products, which is stored in a storage medium
In (such as ROM/RAM, magnetic disk, CD), including some instructions are used so that a mobile terminal (can be mobile phone, computer, clothes
Business device, air conditioner or the network equipment etc.) execute method described in each embodiment of the present invention.
The embodiment of the present invention is described above in conjunction with figure, but the invention is not limited to above-mentioned specific realities
Mode is applied, the above mentioned embodiment is only schematical, rather than restrictive, and those skilled in the art exist
Under enlightenment of the invention, without breaking away from the scope protected by the purposes and claims of the present invention, many shapes can be also made
Formula, all of these belong to the protection of the present invention.
Claims (16)
1. a kind of intelligent adjusting method of rail traffic refrigeration system, which is characterized in that the described method includes:
The workload demand in the following preset period of time back court is estimated, is needed after determining the preset period of time with the load in current time
Seek deviation；
According to the operating parameter of refrigeration system described in the workload demand bias adjustment；
According to the return air temperature of the refrigeration system after target temperature and the adjusting operating parameter, continue to adjust the operation
Parameter, to meet the workload demand in the following preset period of time back court.
2. the method according to claim 1, wherein estimating the workload demand packet in the following preset period of time back court
It includes:
Determine the total number of persons in current time and the workload demand in current time；
Estimate personnel's amount of having a net increase of of the following preset period of time；
When estimating following default according to the total number of persons, the workload demand in the current time, personnel's amount of having a net increase of
Workload demand in section back court.
3. according to the method described in claim 2, it is characterized in that, according to the total number of persons, in the current time
Workload demand, personnel's amount of having a net increase of estimate the workload demand in the following preset period of time back court, are realized by following formula:
φ 1=φ * (Q1/Q)；
Wherein, φ 1 is the workload demand in the following preset period of time back court, and φ is that the load in the current time needs
It asks, Q1 is the total number of persons after the following preset period of time, and Q is the total number of persons at the current time, and Q1=Q+ Δ Q, Δ Q are
Personnel's amount of having a net increase of.
4. according to the method described in claim 3, it is characterized in that, the net increment Delta Q of the personnel is determined by following formula:
Δ Q=α * Δ Qp+ β * Δ Qm- Δ Qc；
Wherein, α, β are correction factor, and alpha+beta=1, Δ Qp is that the future that the ticketing system of the rail traffic counts is pre-
If the total number of persons entered the station in the period, enter the station in the past preset period of time that Δ Qm counts for the access control system of the rail traffic
Total number of persons, Δ Qc are that website regular bus arrives at a station the leaving by bus in the following preset period of time of time of departure management system statistics
Total number of persons.
5. the method according to claim 1, wherein after determining the preset period of time with it is negative in current time
Lotus demand disruption is determined by following formula:
Δ φ=φ 1- φ；
Wherein, Δ φ is the workload demand deviation, and φ 1 is the workload demand in the following preset period of time back court, and φ is institute
State the workload demand in current time.
6. the method according to claim 1, wherein the refrigeration system according to the workload demand bias adjustment
Operating parameter include:
Judge whether the workload demand deviation is zero；
If it is, keeping current operating parameter constant；
If it is not, then being modified to the workload demand deviation；It is run according to revised workload demand bias adjustment
Parameter.
7. according to the method described in claim 6, it is characterized in that, if it is not, then be modified to the workload demand deviation,
Include:
Determine the heat loss of the air supply duct of refrigeration system described in current time；
The workload demand deviation is modified according to the heat loss, the positive and negative situation of the workload demand deviation.
8. the method according to the description of claim 7 is characterized in that just according to the heat loss, the workload demand deviation
Condition of forsaking one's love is modified the workload demand deviation, comprising:
If the workload demand deviation is positive value, revised workload demand deviation=Δ φ+qs* γ；
If the workload demand deviation is negative value, revised workload demand deviation=Δ φ-qs* γ；
Wherein, Δ φ is the workload demand deviation, qs is the heat loss, γ is correction factor.
9. according to the method described in claim 6, it is characterized in that, the operating parameter includes at least: compressor operating frequency,
Rotation speed of fan includes: according to operating parameter described in revised workload demand bias adjustment
When revised workload demand deviation is positive value, improves the running frequency of the compressor, increases the rotation speed of fan；
When revised workload demand deviation is negative value, reduces the running frequency of the compressor, reduces the rotation speed of fan.
10. the method according to claim 1, wherein the operating parameter include at least compressor operating frequency,
Rotation speed of fan continues to adjust institute according to the return air temperature of the refrigeration system after target temperature and the adjusting operating parameter
State operating parameter, comprising:
Determine target temperature deviation and temperature drop rate；
Judge whether the target temperature deviation and the temperature drop rate meet the first preset condition or the second preset condition；
If meeting first preset condition, improves the running frequency of compressor, increases rotation speed of fan；
If meeting second preset condition, reduces the running frequency of compressor, reduces rotation speed of fan；
If being both unsatisfactory for first preset condition, it is also unsatisfactory for second preset condition, then keeps the current of compressor
Running frequency is constant, current rotation speed of fan is constant.
11. according to the method described in claim 10, it is characterized in that,
First preset condition are as follows: the target temperature deviation is greater than preset value and the temperature drop rate is greater than or equal to 0；
Second preset condition are as follows: the target temperature difference is less than the opposite number of the preset value, and the temperature drop rate is less than
Or it is equal to 0；
Wherein, the preset value is positive number.
12. according to the method described in claim 10, it is characterized in that,
The target temperature deviation delta T is determined by following formula:
Δ T=T1-T；
The temperature drop rate dT is determined by following formula:
DT=T1-T2；
Wherein, T1 is the return air temperature, T is the target temperature, T2 is return air temperature before preset duration.
13. a kind of intelligent regulating device of rail traffic refrigeration system, which is characterized in that described device is for right of execution 1 to power
Method described in any one of 12, described device include:
Module is estimated, for estimating the workload demand in the following preset period of time back court；
Determining module, for estimated according to the workload demand that module is estimated determine after the preset period of time with it is current when
Carve the workload demand deviation in field；
Adjustment module, the operating parameter for the refrigeration system according to the workload demand bias adjustment；
The determining module is also used to the return air temperature according to the refrigeration system after target temperature and the adjusting operating parameter
Degree, continues to adjust the operating parameter, to meet the workload demand in the following preset period of time back court.
14. a kind of rail traffic refrigeration system, which is characterized in that the rail traffic refrigeration system includes dress described in power 13
It sets,
The rail traffic refrigeration system is the direct refrigeration-type magnetic suspension air-conditioner set of rail traffic water cooling.
15. a kind of computer equipment including memory, processor and stores the meter that can be run on a memory and on a processor
Calculation machine program, which is characterized in that the processor is realized when executing described program as of any of claims 1-12
The intelligent adjusting method of rail traffic refrigeration system.
16. a kind of storage medium comprising computer executable instructions, the computer executable instructions are by computer disposal
For executing the intelligent adjusting method such as rail traffic refrigeration system of any of claims 1-12 when device executes.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110449313A (en) * | 2019-08-12 | 2019-11-15 | 珠海格力智能装备有限公司 | The control method and device of glue spraying equipment, glue spraying equipment |
| CN110562005A (en) * | 2019-08-22 | 2019-12-13 | 卓尔智联(武汉)研究院有限公司 | vehicle-mounted air conditioner control device, method and computer-readable storage medium |
| CN113375304A (en) * | 2021-06-01 | 2021-09-10 | 深圳地铁建设集团有限公司 | Pre-adjusting method for air conditioning system of subway station |
| CN116820014A (en) * | 2023-08-24 | 2023-09-29 | 山西交通科学研究院集团有限公司 | Intelligent monitoring and early warning method and system for traffic electromechanical equipment |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP4609690B2 (en) * | 2001-08-31 | 2011-01-12 | 清水建設株式会社 | Thermal load pattern calculation system and method, and computer program |
| CN102980272A (en) * | 2012-12-08 | 2013-03-20 | 珠海派诺科技股份有限公司 | Air conditioner system energy saving optimization method based on load prediction |
| CN104236023A (en) * | 2014-10-16 | 2014-12-24 | 珠海格力电器股份有限公司 | Load control method and device |
| CN107576015A (en) * | 2017-09-21 | 2018-01-12 | 新智能源系统控制有限责任公司 | A kind of building air conditioning model predictive control method and device for realizing Demand Side Response |
| CN109130767A (en) * | 2017-06-28 | 2019-01-04 | 北京交通大学 | The intelligent control method of rail traffic station ventilation and air conditioning system based on passenger flow |
-
2019
- 2019-02-22 CN CN201910133635.1A patent/CN109899935B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP4609690B2 (en) * | 2001-08-31 | 2011-01-12 | 清水建設株式会社 | Thermal load pattern calculation system and method, and computer program |
| CN102980272A (en) * | 2012-12-08 | 2013-03-20 | 珠海派诺科技股份有限公司 | Air conditioner system energy saving optimization method based on load prediction |
| CN104236023A (en) * | 2014-10-16 | 2014-12-24 | 珠海格力电器股份有限公司 | Load control method and device |
| CN109130767A (en) * | 2017-06-28 | 2019-01-04 | 北京交通大学 | The intelligent control method of rail traffic station ventilation and air conditioning system based on passenger flow |
| CN107576015A (en) * | 2017-09-21 | 2018-01-12 | 新智能源系统控制有限责任公司 | A kind of building air conditioning model predictive control method and device for realizing Demand Side Response |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110449313A (en) * | 2019-08-12 | 2019-11-15 | 珠海格力智能装备有限公司 | The control method and device of glue spraying equipment, glue spraying equipment |
| CN110562005A (en) * | 2019-08-22 | 2019-12-13 | 卓尔智联(武汉)研究院有限公司 | vehicle-mounted air conditioner control device, method and computer-readable storage medium |
| CN113375304A (en) * | 2021-06-01 | 2021-09-10 | 深圳地铁建设集团有限公司 | Pre-adjusting method for air conditioning system of subway station |
| CN116820014A (en) * | 2023-08-24 | 2023-09-29 | 山西交通科学研究院集团有限公司 | Intelligent monitoring and early warning method and system for traffic electromechanical equipment |
| CN116820014B (en) * | 2023-08-24 | 2023-11-14 | 山西交通科学研究院集团有限公司 | Intelligent monitoring and early warning method and system for traffic electromechanical equipment |
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