CN109899935B - Rail transit refrigerating system and intelligent adjusting method and device thereof - Google Patents
Rail transit refrigerating system and intelligent adjusting method and device thereof Download PDFInfo
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Abstract
The invention discloses a rail transit refrigeration system and an intelligent adjusting method and device thereof, wherein the method comprises the following steps: estimating the load demand in the field after a preset time period in the future, and determining the load demand deviation between the field after the preset time period and the field at the current moment; adjusting the operating parameters of the refrigeration system according to the load demand deviation; and continuously adjusting the operation parameters according to the target temperature and the return air temperature of the refrigeration system after the operation parameters are adjusted so as to meet the load requirement in the field after the future preset time period. Therefore, when the flow of people changes rapidly, the operation parameters of the refrigerating system can be adjusted in time according to the load demand deviation, so that the purpose of improving the comfort level of a user while ensuring the energy efficiency is achieved. And after the operation parameters are adjusted according to the load demand deviation, the operation parameters can be continuously adjusted according to the return air temperature, so that the delay problem caused by the fact that the refrigerating system is far away from a user area can be solved, and the adjustment is more accurate.
Description
Technical Field
The invention relates to the technical field of rail transit, in particular to a rail transit refrigeration system and an intelligent adjusting method and device thereof.
Background
At present, the construction of large-scale rail transit hubs in domestic cities is rapidly developed, the construction of various supporting facilities of the rail transit hubs represented by subway stations and railway stations is also particularly important, and the selection of a refrigeration system is more important. However, the requirements of the refrigeration system of the rail transit are different from those of other stably operating central air conditioning units. In the rail transit, the people flow rate is extremely changed in time-sharing mode, so that the cold load is changed violently, if the cold load cannot be adjusted in time, the electricity is wasted, the energy efficiency is reduced, and the comfort level of passengers is also affected negatively. And considering the effect of noise on passengers, a rail transit terminal typically places the air conditioning units far from the customer premises, and the resulting time delay effect further affects the real-time adjustability of the refrigeration system.
Aiming at the problems that in the related art, the adjusting mode of a refrigerating system in rail transit is relatively fixed, intelligence is low, energy waste is easily caused, and the comfort level of a user is influenced, an effective solution is not provided at present.
Disclosure of Invention
In order to solve the problems that in the related art, the adjusting mode of a refrigerating system in rail transit is relatively fixed, intelligence is low, energy waste is easily caused, and the comfort level of a user is influenced, the embodiment of the invention provides a rail transit system and an intelligent adjusting method and device thereof.
In a first aspect, an embodiment of the present invention provides an intelligent adjustment method for a rail transit refrigeration system, where the method includes:
estimating the load demand in the field after a preset time period in the future, and determining the load demand deviation between the field after the preset time period and the field at the current moment;
adjusting an operating parameter of the refrigeration system according to the load demand deviation;
and continuously adjusting the operation parameters according to the target temperature and the return air temperature of the refrigeration system after the operation parameters are adjusted so as to meet the load requirement in the field after the future preset time period.
Further, predicting the load demand within the yard after a predetermined period of time in the future includes:
determining the total number of people in the current time field and the load demand in the current time field;
predicting the net increment of the personnel in a future preset time period;
and predicting the load demand in the field after a preset period of time in the future according to the total number of the personnel, the load demand in the field at the current moment and the net increment of the personnel.
Further, the load demand in the field after a preset period of time in the future is estimated according to the total number of the personnel, the load demand in the field at the current moment and the net increment of the personnel, and the method is realized through the following formula:
φ1＝φ*(Q1/Q)；
wherein, Φ 1 is the load demand in the yard after the future preset time period, Φ is the load demand in the yard at the current time, Q1 is the total number of people after the future preset time period, Q is the total number of people at the current time, Q1 is Q + Δ Q, and Δ Q is the net increment of people.
Further, the net human gain Δ Q is determined by the following equation:
ΔQ＝α*ΔQp+β*ΔQm-ΔQc；
and alpha and beta are correction coefficients, alpha + beta is 1, delta Qp is the total number of people who get on the station in the future preset time period counted by the ticketing system of the rail transit, delta Qm is the total number of people who get on the station in the past preset time period counted by the access control system of the rail transit, and delta Qc is the total number of people who get on the station and leave the station in the future preset time period counted by the management system of the arrival and departure time of the station airliner.
Further, the deviation between the preset time period and the load demand in the current time field is determined according to the load demand, and is determined through the following formula:
Δφ＝φ1-φ；
and delta phi is the load demand deviation, phi 1 is the load demand in the field after the future preset time period, and phi is the load demand in the field at the current moment.
Further, adjusting the operating parameter of the refrigeration system based on the load demand deviation includes:
judging whether the load demand deviation is zero or not;
if yes, keeping the current operation parameters unchanged;
if not, correcting the load demand deviation;
and adjusting the operating parameters according to the corrected load demand deviation.
Further, if not, correcting the load demand deviation, including:
determining the heat loss of an air supply pipeline of the refrigerating system at the current moment;
and correcting the load demand deviation according to the heat loss and the positive and negative conditions of the load demand deviation.
Further, correcting the load demand deviation according to the heat loss and the positive and negative conditions of the load demand deviation comprises:
if the load demand deviation is a positive value, the corrected load demand deviation is delta phi + qs gamma;
if the load demand deviation is a negative value, the corrected load demand deviation is delta phi-qs gamma;
wherein Δ φ is the load demand deviation, qs is the heat loss, and γ is a correction coefficient.
Further, the operating parameters include at least: the operation frequency of the compressor and the rotating speed of the fan are adjusted according to the corrected load demand deviation, and the operation parameters comprise:
when the corrected load demand deviation is a positive value, increasing the running frequency of the compressor and increasing the rotating speed of the fan;
and when the corrected load demand deviation is a negative value, reducing the operating frequency of the compressor and reducing the rotating speed of the fan.
Further, the operation parameters at least include the operation frequency of the compressor and the rotation speed of the fan, and the operation parameters are continuously adjusted according to the target temperature and the return air temperature of the refrigeration system after the operation parameters are adjusted, including:
determining a target temperature deviation value and a temperature drop rate;
judging whether the target temperature deviation value and the temperature drop rate meet a first preset condition or a second preset condition;
if the first preset condition is met, increasing the running frequency of the compressor and increasing the rotating speed of the fan;
if the second preset condition is met, reducing the running frequency of the compressor and the rotating speed of the fan;
and if the first preset condition is not met, and the second preset condition is not met, keeping the current operating frequency of the compressor and the current rotating speed of the fan unchanged.
Further, the first preset condition is that: the target temperature deviation is greater than a preset value and the temperature drop rate is greater than or equal to 0;
the second preset condition is as follows: the target temperature difference is smaller than the opposite number of the preset value, and the temperature drop rate is smaller than or equal to 0;
wherein the preset value is a positive number.
Further, the target temperature deviation Δ T is determined by the following formula:
ΔT＝T1–T；
the temperature drop rate dT is determined by the following equation:
dT＝T1–T2；
wherein T1 is the return air temperature, T is the target temperature, and T2 is the return air temperature before a preset time.
In a second aspect, an embodiment of the present invention provides an intelligent adjusting device for a rail transit refrigeration system, the device being configured to perform the method of the first aspect, and the device including:
the estimation module is used for estimating the load demand in the field after the future preset time period;
the determining module is used for determining the load demand deviation between the preset time period and the current time field according to the load demand estimated by the estimating module;
the adjusting module is used for adjusting the operation parameters of the refrigerating system according to the load demand deviation;
and the determining module is further used for continuously adjusting the operation parameters according to the target temperature and the return air temperature of the refrigeration system after the operation parameters are adjusted, so as to meet the load requirements in the yard after the future preset time period.
In a third aspect, the embodiment of the invention provides a rail transit refrigeration system, which comprises the device in the second aspect,
the rail transit refrigerating system is a rail transit water-cooling direct refrigeration type magnetic suspension air conditioning unit.
By applying the technical scheme of the invention, the load demand in the field after the future preset time period is estimated, and the load demand deviation between the field after the preset time period and the current time field is determined; adjusting the operating parameters of the refrigeration system according to the load demand deviation; and continuously adjusting the operation parameters according to the target temperature and the return air temperature of the refrigeration system after the operation parameters are adjusted so as to meet the load requirement in the field after the future preset time period. Therefore, when the flow of people changes rapidly, the operation parameters of the refrigerating system can be adjusted in time according to the load demand deviation, so that the purpose of improving the comfort level of a user while ensuring the energy efficiency is achieved. And after the operation parameters are adjusted according to the load demand deviation, the operation parameters can be continuously adjusted according to the return air temperature, so that the delay problem caused by the fact that the refrigerating system is far away from a user area can be solved, and the adjustment is more accurate.
Drawings
Fig. 1 is a flowchart of an intelligent adjustment method of a rail transit refrigeration system according to an embodiment of the invention;
fig. 2 is a flowchart of an intelligent adjustment method of a rail transit refrigeration system according to an embodiment of the invention;
fig. 3 is a flowchart of an intelligent adjustment method of a rail transit refrigeration system according to an embodiment of the invention;
fig. 4 is a flowchart of an intelligent adjustment method of a rail transit refrigeration system according to an embodiment of the invention;
fig. 5 is a flowchart of an intelligent adjustment method of a rail transit refrigeration system according to an embodiment of the invention;
FIG. 6 is a schematic structural diagram of a rail transit refrigeration system and site relationship according to an embodiment of the present invention;
fig. 7 is a flowchart of an intelligent adjustment method of a rail transit refrigeration system according to an embodiment of the invention;
fig. 8 is a flowchart of a method for intelligent adjustment of a rail transit refrigeration system according to an embodiment of the present invention;
fig. 9 is a block diagram of a rail transit refrigeration system according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the following drawings and specific embodiments, it being understood that the specific embodiments described herein are merely illustrative of the invention and are not to be construed as limiting the invention.
In the following description, suffixes such as "module", "component", or "unit" used to denote elements are used only for facilitating the explanation of the present invention, and have no specific meaning in itself. Thus, "module", "component" or "unit" may be used mixedly.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
In order to solve the problems that in the related art, the adjustment mode of a refrigeration system in rail transit is relatively fixed, intelligence is low, energy waste is easily caused, and user comfort is affected, an embodiment of the invention provides an intelligent adjustment method of a rail transit refrigeration system, and as shown in fig. 1, the method includes:
s101, estimating the load demand in the field after a future preset time period;
s102, determining the deviation between the preset time interval and the load demand in the current time field according to the load demand;
step S103, adjusting the operation parameters of the refrigeration system according to the load demand deviation;
step S104, determining the return air temperature of the refrigeration system after the operation parameters are adjusted;
and S105, continuously adjusting the operation parameters according to the return air temperature and the target temperature so as to meet the load requirement in the field after the future preset time period.
From this, when the flow of people changes rapidly, can in time adjust refrigerating system's operating parameter according to load demand deviation, can improve the adaptability of track traffic refrigerating system to the violent change of load in the short time, promote track traffic refrigerating system to the regulation real-time nature of customer zone load change, with when guaranteeing the efficiency, improve user's comfort level, and after adjusting operating parameter according to load demand deviation, can continue to adjust according to return air temperature, can solve refrigerating system because the delay problem that arouses far away from the user area, make the regulation more accurate.
It is understood that the rail transit refrigeration system may be an air conditioner, for example, a rail transit water-cooled direct refrigeration type magnetic levitation air conditioning unit. The adjustment mode may be real-time adjustment, or may be time-division adjustment, periodic adjustment, or intermittent adjustment, and may be set according to actual conditions, which is not limited in the present invention.
In one possible implementation, the operating parameters of the refrigeration system may be the frequency of the compressor, the fan speed, etc., and the return air temperature may be measured by a temperature sensor, etc. And determining the deviation between the preset time interval and the load demand in the current time field according to the load demand, and determining through the following formula: delta phi is phi 1-phi; wherein, Δ φ is the load demand deviation, φ 1 is the load demand in the field after a preset time period in the future, and φ is the load demand in the field at the current time.
In one possible implementation, as shown in fig. 2, the step S101 of predicting the load demand in the yard after the preset period of time in the future includes:
step S201, determining the total number of people in the current time field and the load demand in the current time field;
s202, estimating the net increment of the personnel in a future preset time period;
and S203, estimating the load demand in the field after the preset period of time in the future according to the total number of the personnel, the load demand in the field at the current moment and the net increment of the personnel.
Wherein the load demand in the yard after a preset time period is determined by the following formula:
phi 1 phi (Q1/Q); wherein, Φ 1 is the load demand in the yard after the future preset time period, Φ is the load demand in the yard at the current time, Q1 is the total number of people after the future preset time period, Q is the total number of people at the current time, Q1 is Q + Δ Q, and Δ Q is the net increment of people.
The load demand refers to a load in a certain specified time (train station, subway station). The time point can be arbitrarily specified. The cold load is determined by the heat transferred from the outside (by means of heat conduction, convection, radiation), the heat generating equipment in the station and the number of people in the station. The heat generating equipment in the station comprises lighting equipment, working equipment (such as a computer and a display screen), and the like, and the heat needs to be estimated according to the power consumption of the equipment. Since this portion of the heat varies little with time, it can be set as a constant term. The heat transmitted from the outside changes in one day, but the heat stagnation is large, the change is slow, and the influence on the real-time adjustment of the refrigeration system is small. The main factor influencing the load demand is personnel mobility, so the load demand needs to be calculated according to the number of the personnel (the load demand of a single person can be calculated according to a constant, and the value can be calculated by referring to 100-300W/person).
Wherein the net human gain Δ Q is determined by the following equation:
ΔQ＝α*ΔQp+β*ΔQm-ΔQc；
the correction coefficients are alpha and beta, alpha + beta is 1, delta Qp is the total number of people who get in the station in the future preset time period counted by the ticketing system of the rail transit, delta Qm is the total number of people who get in the station in the past preset time period counted by the access control system of the rail transit, and delta Qc is the total number of people who get out of the station in the future preset time period counted by the management system for the arrival and departure time of the station airliner.
Compared with other places, the large-scale rail transit system station such as a railway station and a subway station has a perfect ticket system, an access control system and a station regular bus arrival and departure time management system, the systems can be linked with the refrigerating system, the people flow change can be predicted, the refrigerating system can be assisted to carry out prejudgment and adjustment according to the people flow change, and the technical effect of improving the adjustment instantaneity is achieved.
The statistical conditions of the ticketing system, the access control system and the station regular bus arrival and departure time management system are briefly described below.
The ticketing system can count the number of people through a counter, and because most of the current large stations adopt the real-name ticketing entry mode, the upper limit of the number of people entering the station within the time delta t can be estimated according to the records of the ticketing system. For example, when the current time is t1 and the time after the time Δ t is t2, the passengers entering the station in the time range [ t1 and t2] have the riding time approximately in the time range [ t1+ Δ t1 and t2+ Δ t2], and the ticket system can find out that the departure time is in the area, namely the total number Δ Qp of the passengers about to take a bus. The parameter Δ t1 represents the minimum time for passengers to arrive at the station in advance, and the parameter Δ t2 represents the maximum time for passengers to arrive at the station in advance, and the two parameters can be sampled and counted according to the history record of the station, and are set by taking the average value, and can be dynamically adjusted according to the continuous record.
When the access control system counts the number of people, the assumption is that the flow of people is uniform in a certain time period. The total number of people can be counted through the access control system. It should be noted that although the way of performing people counting by independently adopting the ticketing system and the access control system is feasible, the errors are large. Therefore, in order to reduce the error between the estimated total number of people arriving at the station and the actual number of people arriving at the station, the statistical result of the two statistical methods can be corrected by using the formula Δ Q ═ α × Δ Qp + β × Δ Qm- Δ Qc, so as to improve the accuracy of people number determination. In the formula, both α and β are correction coefficients, which can be determined according to actual conditions, and because in a normal condition, the access control system adopts a mode of counting the number of people, when people pass security inspection and access control, counting is performed according to a counter, and an error is relatively small, the value of β can be larger, the value of α is between (0.2-0.5), and α + β is 1.
When the number of people leaving the station is counted by the station regular bus arrival and departure time management system, people who cannot take the bus on time due to some reason can be ignored, and the total number of people leaving the station in a future preset time period is equal to the number of people who get on the station and take all the bus numbers passing the station or the station from the beginning to the end in the time period. Denoted as Δ Qc.
In one possible implementation, as shown in fig. 3, the step S103 of adjusting the operation parameters of the refrigeration system according to the load demand deviation includes:
step S301, judging whether the load demand deviation is zero or not; if yes, go to step S302; if not, executing step S303;
step S302, keeping the current operation parameters unchanged;
step S303, correcting the load demand deviation;
and step S304, adjusting the operation parameters according to the corrected load demand deviation.
It can be understood that when the load demand deviation is zero, no adjustment is needed, and when the cold load deviation is non-zero, the load demand deviation can be corrected, and then the operation parameters are adjusted according to the corrected cold load deviation, namely, an adjustment signal is sent to the refrigeration system, so that the adjustment accuracy is improved.
In one possible implementation manner, as shown in fig. 4, the step S303 of correcting the load demand deviation includes:
step S3031, determining heat loss of an air supply pipeline of the refrigeration system at the current moment;
step 3032, correcting the load demand deviation according to the heat loss and the positive and negative conditions of the load demand deviation.
Wherein, revise load demand deviation according to the positive and negative condition of heat loss, load demand deviation, include: if the load demand deviation is a positive value, the corrected load demand deviation is delta phi + qs gamma; if the load demand deviation is a negative value, the corrected load demand deviation is delta phi-qs gamma; where Δ φ is the load demand deviation, qs is the heat loss, and γ is the correction coefficient.
Heat loss qs is m cp (T3-T4), where m is air volume (m3/s), cp is the constant pressure specific heat capacity (J/(kg ℃)), T4 is the outlet temperature of the refrigeration system, and T3 is the outlet temperature inside the station (the structural diagram shown in fig. 6 can more clearly show the points of T3 and T4).
When the temperature delta phi is greater than 0, the load of the unit is increased, when the external temperature is constant (high temperature), the outlet air temperature of the refrigeration system is required to be reduced, the temperature in the air outlet pipeline also needs to be reduced, the heat loss of the air outlet pipeline is increased along with the increase of the temperature difference between the inside and the outside of the pipe, and therefore the value of the load demand deviation needs to be increased properly. That is, Δ Φ + qs γ (γ is 0.05 to 0.1)
When the delta phi is less than 0, the load of the unit is increased, when the external temperature is constant (high temperature), the air outlet temperature of the air conditioner is required to be increased, the temperature in the air outlet pipeline also needs to be increased, the heat loss of the air outlet pipeline is reduced along with the reduction of the temperature difference between the inside and the outside of the air outlet pipeline, and therefore the value of the load demand deviation can be properly reduced. Namely, Δ Φ -qs γ (γ is 0.05 to 0.1).
The heat dissipation intensity of the pipeline is changed due to the positive and negative conditions of the load demand deviation, so that the load demand deviation can be further corrected according to the heat loss, and the accuracy of determining the load demand deviation is improved.
In one possible implementation, the operating parameters include at least: the operation frequency of the compressor and the rotating speed of the fan are adjusted according to the corrected load demand deviation, and the operation parameters comprise: when the corrected load demand deviation is a positive value, the running frequency of the compressor is increased, and the rotating speed of the fan is increased; and when the corrected load demand deviation is a negative value, reducing the operating frequency of the compressor and reducing the rotating speed of the fan.
It can be understood that the above-mentioned adjustment mode is a load pre-judgment adjustment mechanism, and can be completed within a half of the time of the future preset time period, and the rest time can further adjust the refrigeration system according to the return air temperature, so that after the future preset time period, the refrigerating capacity/heating capacity of the refrigeration system is closer to the real load demand, so as to further improve the comfort level of the user while ensuring the energy efficiency. The following implementation briefly describes the manner in which the adjustment is made based on the return air temperature.
In one possible implementation manner, as shown in fig. 5, the operation parameters at least include the compressor operation frequency and the fan rotation speed, and the step S105 of continuously adjusting the operation parameters according to the return air temperature and the target temperature to meet the load demand includes:
step S501, determining a target temperature deviation value and a temperature drop rate;
step S502, judging whether the target temperature deviation value and the temperature drop rate meet a first preset condition or a second preset condition;
step S503, if a first preset condition is met, increasing the running frequency of the compressor and increasing the rotating speed of the fan;
step S504, if a second preset condition is met, reducing the running frequency of the compressor and the rotating speed of the fan;
and step S505, if the first preset condition is not met, and the second preset condition is not met, keeping the current operating frequency of the compressor and the current rotating speed of the fan unchanged.
In one possible implementation manner, the first preset condition is: the target temperature deviation is greater than a preset value and the temperature drop rate is greater than or equal to 0; the second preset condition is as follows: the target temperature difference is smaller than the opposite number of the preset value, and the temperature drop rate is smaller than or equal to 0; wherein the preset value is a positive number. The target temperature deviation Δ T is determined by the following formula: Δ T ═ T1-T; the temperature drop rate dT is determined by the following equation: dT-T1-T2; wherein, T1 is the return air temperature, T is the target temperature, and T2 is the return air temperature before the preset time. The structural diagram shown in fig. 6 can more clearly indicate the point of T1. The preset value may be 2, and the preset duration may be 60S. It can be understood that when the frequency of the compressor is increased or decreased, the amplitude can be delta F, the delta F can be 1-5 Hz, and the value of the delta F can be set according to the space size of a station.
It can be understood that the adjustment mode can be periodic adjustment, and after the adjustment according to the return air temperature is completed, the adjustment can be continuously performed according to the deviation between the return air temperature and the target temperature in the next period. If the mode of adjusting according to the return air temperature conflicts with the mode of adjusting according to the estimated load demand deviation, the adjustment can be performed according to the estimated load demand deviation preferentially.
Fig. 7 is a flowchart illustrating a method for intelligently adjusting a rail transit refrigeration system according to an embodiment of the present invention, where the method includes, as shown in fig. 7:
s701, knowing the air outlet temperature T1 of the air conditioning unit, the temperature T2 of an air outlet inside the station and a pre-determined load deviation delta phi;
step S702, calculating the heat loss of the air supply pipeline at the time t: qs (m cp) (T2-T1);
step S703, Δ Φ > 0? If yes, executing step S704, if no, executing step S705;
step S704, Δ Φ + qs γ; then step S708 is executed;
step S705, Δ Φ < 0? If yes, executing step S706, if no, executing step S707;
step S706, Δ Φ -qs γ; then step S708 is executed;
step S707, no adjustment is needed, and then step S709 is executed;
step S708, adjusting the frequency F of the compressor to meet the load deviation requirement;
step S709 exits the load deviation control.
Therefore, the load demand deviation can be further corrected according to the heat loss, so that the accuracy of determining the load demand deviation is improved.
Fig. 8 is a flowchart illustrating a method for intelligently adjusting a rail transit refrigeration system according to an embodiment of the present invention, where the method includes, as shown in fig. 8:
step S801, recording and calling a current return air temperature detection value T1, calling a 60S-previous return air temperature detection value T2 (before 60S), and calling a current set temperature T in the station;
step S802, setting the target temperature deviation delta T as T1-T; the temperature drop rate dT is T1-T2 (before 60 s);
step S803, Δ T >2 and dT ≧ 0? If yes, executing step S804, if no, executing step S805;
step S804, F + Δ F; then step S808 is executed;
step S805, Δ T < -2 and dT ≦ 0? If yes, go to step S806; if not, go to step S807;
step S806, F ═ F — Δ F; then step S808 is executed;
step S807, the frequency of the compressor is unchanged; then step S809 is executed;
step S808, finishing the frequency adjustment of the compressor;
step S809 exits the temperature deviation control.
Therefore, the refrigerating capacity/heating capacity of the refrigerating system can be further adjusted according to the return air temperature, so that after a preset time period in the future, the refrigerating capacity/heating capacity of the refrigerating system is closer to the real load demand, and the comfort level of a user is further improved while the energy efficiency is further ensured.
Fig. 9 shows an intelligent adjusting device of a rail transit refrigeration system, which is used for executing the method shown in fig. 1, according to an embodiment of the invention, and the device comprises:
the estimation module 901 is used for estimating the load demand in the field after a future preset time period;
a determining module 902, configured to determine, according to the load demand estimated by the estimating module 901, a load demand deviation between a preset time period and a current time field;
an adjusting module 903, configured to adjust an operating parameter of the refrigeration system according to the load demand deviation;
a determining module 902, further configured to determine a return air temperature of the refrigeration system after the operating parameter is adjusted;
and the adjusting module 903 is further configured to continue adjusting the operating parameters according to the return air temperature and the target temperature, so as to meet the load demand in the yard after a preset time period in the future.
From this, when the flow of people changes rapidly, can in time adjust refrigerating system's operating parameter according to load demand deviation to when guaranteeing the energy efficiency, improve user's comfort level, and after adjusting operating parameter according to load demand deviation, can continue to adjust according to return air temperature, can solve refrigerating system because the delay problem that arouses far away from the user area makes the regulation more accurate.
The embodiment of the invention also provides a rail transit refrigeration system which comprises the device shown in the figure 7 and is a rail transit water-cooling direct refrigeration type magnetic suspension air conditioning unit.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a mobile terminal (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
While the present invention has been described with reference to the embodiments illustrated in the drawings, the present invention is not limited to the embodiments, which are illustrative rather than restrictive, and it will be apparent to those skilled in the art that many more modifications and variations can be made without departing from the spirit of the invention and the scope of the appended claims.
Claims (15)
1. An intelligent adjustment method for a rail transit refrigeration system, the method comprising:
estimating the load demand in the field after a preset time period in the future, and determining the load demand deviation between the field after the preset time period and the field at the current moment;
adjusting an operating parameter of the refrigeration system according to the load demand deviation;
continuously adjusting the operation parameters according to the target temperature and the return air temperature of the refrigeration system after the operation parameters are adjusted so as to meet the load requirements in the field after the future preset time period;
adjusting an operating parameter of the refrigeration system based on the load demand deviation comprises:
judging whether the load demand deviation is zero or not;
if yes, keeping the current operation parameters unchanged;
if not, correcting the load demand deviation; and adjusting the operating parameters according to the corrected load demand deviation.
2. The method of claim 1, wherein predicting the load demand within the yard after a predetermined period of time in the future comprises:
determining the total number of people in the current time field and the load demand in the current time field;
predicting the net increment of the personnel in a future preset time period;
and predicting the load demand in the field after a preset period of time in the future according to the total number of the personnel, the load demand in the field at the current moment and the net increment of the personnel.
3. The method of claim 2, wherein the total number of people, the load demand in the current time field, and the net delta of people are predicted to be the load demand in the field after a preset period of time in the future, and the method is implemented by the following formula:
φ1＝φ*(Q1/Q)；
wherein, Φ 1 is the load demand in the yard after the future preset time period, Φ is the load demand in the yard at the current time, Q1 is the total number of people after the future preset time period, Q is the total number of people at the current time, Q1 is Q + Δ Q, and Δ Q is the net increment of people.
4. The method of claim 3, wherein the net human gain Δ Q is determined by the equation:
ΔQ＝α*ΔQp+β*ΔQm-ΔQc；
and alpha and beta are correction coefficients, alpha + beta is 1, delta Qp is the total number of people who get on the station in the future preset time period counted by the ticketing system of the rail transit, delta Qm is the total number of people who get on the station in the past preset time period counted by the access control system of the rail transit, and delta Qc is the total number of people who get on the station and leave the station in the future preset time period counted by the management system of the arrival and departure time of the station airliner.
5. The method of claim 1, wherein determining the deviation of the load demand from the current time field after the predetermined period of time is determined by the following equation:
Δφ＝φ1-φ；
and delta phi is the load demand deviation, phi 1 is the load demand in the field after the future preset time period, and phi is the load demand in the field at the current moment.
6. The method of claim 1, wherein if not, correcting the load demand deviation comprises:
determining the heat loss of an air supply pipeline of the refrigeration system at the current moment;
and correcting the load demand deviation according to the heat loss and the positive and negative conditions of the load demand deviation.
7. The method of claim 6, wherein correcting the load demand deviation based on the heat loss, the positive or negative of the load demand deviation comprises:
if the load demand deviation is a positive value, the corrected load demand deviation is delta phi + qs gamma;
if the load demand deviation is a negative value, the corrected load demand deviation is delta phi-qs gamma;
wherein Δ φ is the load demand deviation, qs is the heat loss, and γ is a correction coefficient.
8. The method according to claim 1, characterized in that said operating parameters comprise at least: the operation frequency of the compressor and the rotating speed of the fan are adjusted according to the corrected load demand deviation, and the operation parameters comprise:
when the corrected load demand deviation is a positive value, increasing the running frequency of the compressor and increasing the rotating speed of the fan;
and when the corrected load demand deviation is a negative value, reducing the operating frequency of the compressor and reducing the rotating speed of the fan.
9. The method of claim 1, wherein the operating parameters include at least compressor operating frequency, fan speed, and continuing to adjust the operating parameters based on the target temperature and the return air temperature of the refrigeration system after adjusting the operating parameters comprises:
determining a target temperature deviation value and a temperature drop rate;
judging whether the target temperature deviation value and the temperature drop rate meet a first preset condition or a second preset condition;
if the first preset condition is met, increasing the running frequency of the compressor and increasing the rotating speed of the fan;
if the second preset condition is met, reducing the running frequency of the compressor and the rotating speed of the fan;
and if the first preset condition is not met, and the second preset condition is not met, keeping the current operating frequency of the compressor and the current rotating speed of the fan unchanged.
10. The method of claim 9,
the first preset condition is as follows: the target temperature deviation is greater than a preset value and the temperature drop rate is greater than or equal to 0;
the second preset condition is as follows: the target temperature deviation is smaller than the opposite number of the preset value, and the temperature drop rate is smaller than or equal to 0;
wherein the preset value is a positive number.
11. The method of claim 9,
the target temperature deviation Δ T is determined by the following equation:
ΔT＝T1–T；
the temperature drop rate dT is determined by the following equation:
dT＝T1–T2；
wherein T1 is the return air temperature, T is the target temperature, and T2 is the return air temperature before a preset time.
12. An intelligent adjustment device for a rail transit refrigeration system, the device being configured to perform the method of any one of claims 1 to 11, the device comprising:
the estimation module is used for estimating the load demand in the field after the future preset time period;
the determining module is used for determining the load demand deviation between the preset time period and the current time field according to the load demand estimated by the estimating module;
the adjusting module is used for adjusting the operation parameters of the refrigerating system according to the load demand deviation;
and the determining module is further used for continuously adjusting the operation parameters according to the target temperature and the return air temperature of the refrigeration system after the operation parameters are adjusted, so as to meet the load requirements in the yard after the future preset time period.
13. A rail transit refrigeration system comprising the apparatus of claim 12,
the rail transit refrigerating system is a rail transit water-cooling direct refrigeration type magnetic suspension air conditioning unit.
14. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor, when executing the program, implements the intelligent adjustment method of a rail transit refrigeration system according to any of claims 1-11.
15. A storage medium containing computer executable instructions for performing the intelligent tuning method of a rail transit refrigeration system as claimed in any one of claims 1-11 when executed by a computer processor.
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