CN114608187B - Method, device, equipment and storage medium for determining cooling machine adjusting mode - Google Patents
Method, device, equipment and storage medium for determining cooling machine adjusting mode Download PDFInfo
- Publication number
- CN114608187B CN114608187B CN202210194281.3A CN202210194281A CN114608187B CN 114608187 B CN114608187 B CN 114608187B CN 202210194281 A CN202210194281 A CN 202210194281A CN 114608187 B CN114608187 B CN 114608187B
- Authority
- CN
- China
- Prior art keywords
- strategy
- determining
- regulation
- content
- fixed
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/89—Arrangement or mounting of control or safety devices
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/88—Electrical aspects, e.g. circuits
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/044—Recurrent networks, e.g. Hopfield networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
Abstract
The embodiment of the invention discloses a method, a device, equipment and a storage medium for determining a cooling machine adjusting mode. The method comprises the following steps: acquiring environmental data; determining strategy content of a cold machine regulation strategy according to the environment data and the cold machine working data; acquiring the strategy content generation time of the cold machine regulation strategy, and determining the strategy type of the cold machine regulation strategy according to the generation time; wherein the policy types include an adjustment policy and a non-fixed adjustment policy; and determining a cold machine adjusting mode according to the strategy content and the strategy type of the cold machine adjusting strategy. The embodiment of the invention can realize the dynamic response and regulation of the building to the environmental fluctuation, improve the accuracy and the effectiveness of the regulation mode of the building cold station equipment, and reduce the energy utilization level of the building cold source measurement.
Description
Technical Field
The embodiment of the invention relates to the field of building energy conservation and intelligent control, in particular to a method, a device, equipment and a storage medium for determining a cooling machine adjusting mode.
Background
Along with the continuous improvement of living standard, the demand for regulating and controlling the temperature in indoor environments such as living, storage and the like is higher and higher, so that the constant temperature is required to be kept for a certain time, and the flexible temperature regulating capability is also required to be kept, so as to achieve the balance of the environmental quality and the energy consumption of cold source equipment. Cold stations are one of the important electromechanical systems in building systems, and are commonly used for temperature regulation due to their complex system, high energy consumption and good refrigeration effect.
The outdoor weather fluctuation in the transitional season is larger, and the requirements on the operation and adjustment flexibility of the cold station are higher. For the adjustment of a cold machine, the operation is currently adjusted mainly by experience of a cold station operation engineer, certain manual operation inertia exists, excessive or lack of cold supply of the temperature in the field is caused, energy consumption is wasted, and proper temperature cannot be ensured.
Disclosure of Invention
The embodiment of the invention provides a method, a device, equipment and a storage medium for determining a cold machine adjusting mode, which can realize dynamic response and regulation and control on environmental fluctuation and improve the accuracy and effectiveness of the adjusting mode.
In a first aspect, an embodiment of the present invention provides a method for determining a cooling machine adjustment mode, where the method includes:
acquiring environmental data;
determining strategy content of a cold machine regulation strategy according to the environment data and the cold machine working data;
acquiring the strategy content generation time of the cold machine regulation strategy, and determining the strategy type of the cold machine regulation strategy according to the generation time; wherein the policy types include fixed regulation policies and non-fixed regulation policies;
and determining a cold machine adjusting mode according to the strategy content and the strategy type of the cold machine adjusting strategy.
In a second aspect, an embodiment of the present invention further provides a device for determining a cooling machine adjustment mode, where the device includes:
the environment data acquisition module is used for acquiring environment data;
the strategy content determining module is used for determining strategy content of a cold machine regulating strategy according to the environment data and the cold machine working data;
the strategy type determining module is used for obtaining the strategy content generation time of the cold machine regulation strategy and determining the strategy type of the cold machine regulation strategy according to the generation time; wherein the policy types include fixed regulation policies and non-fixed regulation policies;
and the adjustment mode determining module is used for determining the adjustment mode of the chiller according to the strategy content and the strategy type of the chiller adjustment strategy.
In a third aspect, an embodiment of the present invention further provides an electronic device, including:
one or more processors;
a storage means for storing one or more programs;
when the one or more programs are executed by the one or more processors, the one or more processors implement the method for determining a cooling machine adjustment mode according to any embodiment of the present invention.
In a fourth aspect, an embodiment of the present invention further provides a storage medium containing computer executable instructions, where the computer executable instructions are stored, where the computer executable instructions when executed by a computer processor implement the method for determining a cooling machine adjustment mode according to any embodiment of the present invention.
According to the embodiment of the invention, the strategy content of the cold machine regulation strategy is flexibly generated according to the environment data and the working data, so that the dynamic response and regulation of the environment fluctuation are realized; and the regulation mode is determined by combining the strategy type, so that the accuracy and the effectiveness of the regulation mode are improved, the efficient operation of the cold station is ensured, and the temperature control and energy saving effects are ensured.
Drawings
Other features, objects and advantages of the present invention will become more apparent upon reading of the detailed description of non-limiting embodiments, made with reference to the accompanying drawings in which:
FIG. 1A is a schematic diagram of a method for determining a cooling machine adjustment mode according to an embodiment of the present invention;
FIG. 1B is a flow chart of determining chiller cooling based on environmental data according to an embodiment of the present invention;
FIG. 1C is a schematic diagram of a cold station intelligent modeling and model usage flow provided in accordance with an embodiment of the invention;
FIG. 2A is a schematic diagram of a method for determining a cooling machine adjustment mode according to another embodiment of the present invention;
FIG. 2B is a schematic diagram of a policy content determination flow provided in accordance with yet another embodiment of the present invention;
FIG. 2C is a schematic representation of a policy pushing moment intent provided in accordance with yet another embodiment of the present invention;
FIG. 2D is a flow chart of a method for determining adjustment according to policy content and policy type according to still another embodiment of the present invention;
FIG. 3 is a schematic view of a device for determining a cooling machine adjusting mode according to another embodiment of the present invention;
fig. 4 is a block diagram of an electronic device for implementing an embodiment of the invention.
Detailed Description
The invention is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting thereof. It should be further noted that, for convenience of description, only some, but not all of the structures related to the present invention are shown in the drawings.
Fig. 1A is a flowchart of a cold machine adjustment mode determination according to an embodiment of the present invention, where the embodiment is applicable to a cold machine adjustment according to environmental data, and the method may be performed by a cold machine adjustment mode determining device, and the device may be implemented in a software and/or hardware manner. The device can be configured in an electronic device with corresponding data processing capability, and the method specifically comprises the following steps:
s110, acquiring environment data.
Wherein, the environmental data is indoor and outdoor data such as temperature, humidity, wind speed and the like which can influence the refrigerating effect or the refrigerating experience.
Specifically, for the acquisition of indoor environment data, measurement and acquisition can be performed through a sensor so as to improve the accuracy and reliability of the data; for the acquisition of outdoor environment data, the acquisition can be performed based on weather forecast service, so as to improve the acquisition rate of the environment data and reduce the running cost.
And S120, determining strategy contents of a cold machine regulation strategy according to the environment data and the cold machine working data.
The cold machine working data are running data of the cold machine when in work, such as data of the number of cold machine starting, real-time power, cold machine water outlet temperature, working time length and the like. The strategy content is to adjust the cooling machine, such as turning off, increasing the number of the cooling machines to increase the outlet water temperature. The chiller may be, for example, a cold source device of a public building central air conditioning system.
Specifically, the working environment of the chiller is determined based on the environmental data, and the operating state of the chiller is determined based on the working data. And combining the current working environment and the working state of the cold machine to determine whether to adjust the working state of the cold machine and a specific adjusting strategy. The strategy content of the regulation strategy is determined by combining the environment data and the working data, so that the correlation between the strategy content and the objective condition is ensured, and the effectiveness of the strategy content is improved.
S130, acquiring the strategy content generation time of the chiller regulation strategy, and determining the strategy type of the chiller regulation strategy according to the generation time; wherein the policy types include fixed and non-fixed regulation policies.
The generating time may be the time of determining the policy content, or may be the time of acquiring the environmental data and the working data, where the environmental data and the working data may carry corresponding time stamps. The fixed adjustment policy may be regarded as a non-additive policy, which is a pushed policy during a fixed adjustment policy period. While the non-fixed adjustment policy may be considered an addition policy, an addition policy that is issued is increased when the environmental data satisfies a certain condition and is in a non-fixed adjustment policy period.
Specifically, when the adjustment control system of the chiller starts in a fixed time period, surrounding environment information is actively acquired, and at least one refrigeration capacity adjustment strategy is generated and used as the fixed adjustment strategy. In the non-fixed time period of the running of the cold machine, the cold quantity regulating strategy is not forced to be generated, and whether the regulating strategy is generated or not is mainly determined by whether the working state and the working environment of the cold machine meet certain conditions or not at the moment, and the generated cold quantity regulating strategy is used as the non-fixed regulating strategy. In the method, the generation time of the strategy content is determined, the generation time is compared with the strategy pushing timetable according to the generation time of the strategy content, the strategy pushing timetable records a fixed time period and a non-fixed time period, and a time interval to which the generation time belongs is determined. And determining the strategy type of the regulation strategy according to the corresponding relation between the time interval and the strategy type.
And S140, determining a chiller adjusting mode according to the strategy content and the strategy type of the chiller adjusting strategy.
Specifically, the strategy content and the strategy type of the cold machine regulation strategy are compared, and the deep logic relationship between the current strategy content and the strategy type is determined. If the logic relationship meets the requirement, determining that the strategy content is a cold machine adjusting mode and executing; if the logical relationship is not satisfactory, then it is indicated that the current policy content may not be suitable for execution at the current point in time or time period, and execution of the policy content may be aborted and the current operational state continued.
According to the embodiment of the invention, the strategy content of the cold machine regulation strategy is flexibly generated according to the environment data and the working data, so that the dynamic response and regulation of the environment fluctuation are realized; and the regulation mode is determined by combining the strategy type, so that the accuracy and the effectiveness of the regulation mode are improved, the efficient operation of the cold station is ensured, and the temperature control and energy saving effects are ensured.
Optionally, the policy content of the chiller adjustment policy includes at least one of:
issuing a shutdown instruction, emptying the startup time, pushing a regulation suggestion and regulating the cold quantity of the cold machine.
Exemplary, fig. 1B is a schematic flow chart of determining cooling capacity of a chiller according to environmental data. The specific cold quantity adjusting mode is determined by acquiring the indoor highest temperature, the indoor target temperature, the outlet water temperature of the cold machine and the initial value of the outlet water temperature of the cold machine. It should be noted that, the actual temperature values shown in fig. 1B may be set according to different situations and requirements, and fig. 1B is only specifically limited for the sake of understanding. As long as the specific adjustment mode of the cold quantity is determined by using the flow, even if other values are selected, the method is within the coverage range of the invention.
Exemplary, fig. 1C is a schematic diagram of a cold station intelligent modeling and model usage flow provided by the present invention. The intelligent control model is established to learn the corresponding relation between different indoor temperatures, the number of cold machines and the set value of the water outlet temperature of the cold machine and the cold quantity through historical data, and learn the characteristic relation of target data corresponding to different working conditions. Data amount required for model training-typically, a historical data adjustment strategy requires at least one month of historical data adjustment strategy requires dynamic historical data of 7 days in the past for continuous learning and updating of the model. The cold quantity is predicted by adopting a Long short-term memory (LSTM) algorithm, the cold quantity requirement before the next strategy is predicted by inputting internal and external data at the calculation moment, and the cold machine combination with the highest efficiency is selected according to the static information of the cold machine to be used as the strategy output.
Optionally, the cold energy adjustment of the cold machine comprises adding the cold energy adjustment of the machine and subtracting the cold energy adjustment of the machine; when the strategy content is the adding machine cooling capacity in the cooling capacity adjusting process, executing the following steps: detecting whether a small cooler is started or not; if the small cooler is started, the small cooler is regulated to be a large cooler, otherwise, the other small cooler is started; when the strategy content is the reducing machine cooling capacity in the cooling capacity adjusting process, executing the following steps: detecting whether a small cooler is started or not; if the small cooler is started, the small cooler is closed, otherwise, the other large cooler is set to be used as the small cooler, and the water outlet temperature of the cooler is set to be a preset temperature value.
Specifically, when the output cooling capacity of the chiller needs to be adjusted to reach the target cooling state, the required target cooling capacity is usually not an integral multiple of the cooling capacity which can be output by each chiller, and the target chiller cooling capacity is wanted to be output by controlling the starting quantity of the chillers, and a part of useless cooling capacity exceeding the target cooling capacity is certainly output, so that ineffective power consumption is brought. In the invention, small coolers and large coolers with different output cold energy are arranged in the refrigerating system in advance, the small coolers are used for outputting lower cold energy, and the large coolers are used for outputting higher cold energy. When the number of the opened chillers is required to be increased to increase the output cold quantity, the small chillers are preferably selected to be closed, the large chillers are opened, and if the large chillers are already opened, the small chillers are opened; when the number of the cold machines needs to be reduced to reduce the output cold quantity, the small cold machine is preferably selected to be closed, and if the small cold machine is already closed, the water outlet temperature of the large cold machine is set to be a lower value to be used as the small cold machine. The type and the running state of the opened cold machine are intelligently adjusted to reduce the excessive invalid output of the cold quantity of the cold machine, and the running power consumption of the cold machine refrigerating system is effectively reduced. The size of the cooling machine is distinguished by the relative height of the rated cooling capacity and the rated power, the cooling machine with higher rated cooling capacity and rated power is a large cooling machine, and the cooling machine with higher rated cooling capacity and rated power is a small cooling machine.
In the embodiment of the invention, the following possible groups of the cold machine and the set temperature can be selected according to the cold machine cold energy target output and the cold energy demand interval: 1) The number of the freeze pump is consistent with that of the cold machine, and the frequency is set to be 50Hz; 2) The number of the cooling pumps is consistent with that of the cold machine, and the frequency is set to be 50Hz; 3) The number of the cooling towers is consistent with that of the cooling towers, and the frequency is set to be 50Hz.
Fig. 2A is a flowchart of a method for determining a cooling machine adjusting method according to another embodiment of the present invention, where the method is optimized and improved based on the foregoing embodiments. Specifically, referring to fig. 2A, the method may include:
s210, acquiring environment data.
S220, determining the credibility of the environmental data, the outdoor environmental data and the indoor environmental data according to the environmental data; determining a cold machine start-stop state and a plate exchanger installation state according to cold machine working data; and determining strategy content of a cold machine regulation strategy based on the cold machine start-stop state, the outdoor environment data, the indoor environment data, the environment data credibility and the plate exchanger installation state.
Wherein the plate exchanger is a device for system energy recovery in a chiller system. The outdoor environment data may include real-time temperature and real-time humidity outdoors, and the indoor environment data may include indoor temperature targets and indoor maximum temperatures.
Specifically, the specific strategy content is determined by 6 layers of nested condition judgment logic, namely whether the environment data is reliable, the cold machine start-stop state, the difference value between the indoor target temperature and the indoor highest temperature, and whether a plate exchanger is installed or not.
Fig. 2B is a schematic diagram of a policy content determining process according to the present invention. The advice content of push advice a and C may be "warm advice: outdoor temperature one hour in the future is × and a fresh air cooling measure is recommended, wherein the recommended content of the push suggestions B and D can be "warm prompt: current outdoor wet bulb temperature is × c, requesting free cool on).
Illustratively, policy content determination is also limited by the following business condition constraints: 1) The outdoor dry bulb temperature is less than or equal to 15 ℃ and the refrigerator cannot be started; 2) The maximum number of cold machine starts, constrained by the configuration information 'maximum number of cold machine starts'; 3) The starting condition of the cooling machine every hour is constrained by the earliest early start and the latest early close in the configuration information; 4) When the outdoor temperature is less than or equal to 16 ℃ and the outdoor relative humidity is less than or equal to 80%, or when a free cooling system exists and the outdoor wet bulb temperature is less than or equal to 15 ℃ in the current and future 4 hours, a strategy without starting the cooling machine is provided, and the priority is higher than the limit of the minimum number of the cooling machines.
S230, if the generation time is within the non-fixed time period, determining that the strategy type of the chiller regulation strategy is a non-fixed regulation strategy; if the generation time is within a fixed time period, determining the strategy type of the cooling machine regulation strategy as a fixed regulation strategy; wherein the fixed time period and the non-fixed time period are determined by a plurality of preset strategy type determination time points.
The time point of the preset strategy type determination is determined according to specific requirements, and the time point of the outdoor temperature change or the change trend in a day can be determined as the time point of the preset strategy type determination, such as sunrise time, the time points of the highest and lowest temperatures in a day and sunset time points.
Specifically, according to the generation time of the policy content, the generation time is compared with the policy pushing schedule, so that the time period in which the current policy content falls is determined, and the policy type of the current policy content is determined according to the time period in which the current policy content falls.
Fig. 2C is a schematic diagram of a policy pushing moment according to the present invention.
The time policy pushing schedule may be obtained by determining a time point (i.e., a fixed time) and a time period length T (e.g., 60 minutes) according to five preset policy types of T1, T2, T3, T4, and T5. Wherein [ T1, T1+ T ], [ T2, T2+ T ], [ T3, T3+ T ] are fixed time periods, the corresponding strategy type of the strategy content with the generated time falling in the three time periods is a fixed regulation strategy, otherwise, the strategy type is a non-fixed regulation strategy, and it is required to be noted that the time length between any two preset strategy type determination time points is larger than the time period length T.
It should be noted that, for the policy push schedule, the following rules exist: 1) In the time period length T after the fixed time T1/T2/T3, the non-fixed adjustment strategy is not needed, so that the computing resource is saved, and the repeated execution of different strategies in a short time is avoided. 2) The non-fixed adjustment strategy must not be shut down during the fixed time-preset time length L (which may be 30 minutes, for example) time period. 3) If the machine-added cooling capacity regulating strategy with non-fixed time exists in the time period of the fixed time-preset time length L and the fixed time, the fixed regulating strategy is consistent with the non-fixed regulating strategy given by the fixed time-preset time length L and the fixed time. Wherein, the "fixed time-preset time length L" represents the last time point separated from the fixed time by a preset time length, for example, the fixed time is 12 points, the preset time length L is 30 minutes, and then the last time point is 11:30.
S240, when the policy type is a non-fixed adjustment policy: if the strategy content is a shutdown instruction issued and the time length of any preset strategy type determination time point in the generation time interval does not exceed a preset time length threshold, determining the continuous current working state as an adjustment mode; otherwise, determining the strategy content as a regulating mode.
S250, when the strategy type is a fixed adjustment strategy: if the generation time is after the target preset strategy type determination time point or the generation time does not exceed the next preset strategy type determination time point, determining the continuous current working state as an adjustment mode; the target preset strategy type determining time point is the latest preset strategy type determining time point; otherwise, determining the strategy content as a regulating mode.
Specifically, if the shutdown instruction is executed when the preset strategy type is close to the determined time point, the refrigerator is turned on again to generate a fixed regulation strategy when the refrigerator reaches a fixed time in a short time after being turned off, so that the refrigerator is repeatedly turned on and off in a short time. In the invention, when the strategy type is a non-fixed regulation strategy, if the time length of any preset strategy type determination time point between the generation time of issuing the shutdown instruction is detected to be smaller than the preset time length threshold value, the execution of the strategy content of issuing the shutdown instruction is abandoned, and the original working state is continued, so that the repeated startup and shutdown of the refrigerator in a short time is avoided.
Specifically, the target preset policy type determining time point is taken as the latest preset policy type determining time point, and is usually the business ending time point. At this time, a certain amount of redundant time (e.g., [ T4, T5] in fig. 2C) may be reserved for sorting the goods, statistics, sanitation, etc. and redundant activities, so that the chiller is turned off only after the time reaches the determined time point of the target preset policy type. And in the redundancy time immediately before shutdown, if an unfixed regulation strategy with strategy content being a shutdown instruction is generated, the unfixed regulation strategy is not executed, and the shutdown process and normal redundancy activities are prevented from being repeatedly executed again when the redundancy time is over. In addition, when the generation time of the non-fixed adjustment strategy exceeds the next strategy type determination time point, in order to avoid conflict with the fixed adjustment strategy to be generated or repeated execution, if the generation time exceeds the next preset strategy type determination time point, the execution of strategy content is abandoned, and the current working state is continued to be determined as an adjustment mode.
Fig. 2D is a schematic flow chart of an adjustment method according to the policy content and the policy type. With reference to this figure, the logic of how the final adjustment is determined according to policy content and policy type in the present invention is carefully described in the form of a flow chart.
The embodiment of the invention determines the strategy type of the chiller adjusting strategy according to the time period of the generation time, thereby determining whether the strategy content needs to be executed according to the strategy type and the generation time. On the basis of realizing dynamic response and regulation to environmental fluctuation, repeated and invalid execution of the cold machine regulation strategy is avoided, and the effectiveness and accuracy of the cold machine regulation strategy are further improved.
Fig. 3 is a block diagram of a cold machine adjustment mode determining device according to another embodiment of the present invention, where the embodiment is applicable to adjustment of a cold machine working state according to environmental data, and the device may be implemented in a software and/or hardware manner. The apparatus may be configured in an electronic device having corresponding data processing capabilities. As shown in fig. 3, the apparatus may include:
an environmental data acquisition module 310, configured to acquire environmental data;
a policy content determining module 320, configured to determine policy content of a chiller adjustment policy according to the environmental data and the chiller operation data;
the policy type determining module 330 is configured to obtain a policy content generation time of the chiller adjustment policy, and determine a policy type of the chiller adjustment policy according to the generation time; wherein the policy types include fixed regulation policies and non-fixed regulation policies;
and the adjustment mode determining module 340 is configured to determine a chiller adjustment mode according to the policy content and the policy type of the chiller adjustment policy.
The device and the module can execute the cold machine adjusting mode determining method provided by any embodiment of the invention, and have the corresponding functional modules and beneficial effects of the executing method.
Optionally, the policy content determining module 320 is specifically configured to:
determining the credibility of the environmental data, the outdoor environmental data and the indoor environmental data according to the environmental data; determining a cold machine start-stop state and a plate exchanger installation state according to cold machine working data; and determining strategy content of a cold machine regulation strategy based on the cold machine start-stop state, the outdoor environment data, the indoor environment data, the environment data credibility and the plate exchanger installation state.
Optionally, the policy content of the chiller adjustment policy includes at least one of:
issuing a shutdown instruction, emptying the startup time, pushing a regulation suggestion and regulating the cold quantity of the cold machine.
Optionally, the policy type determining module 330 further includes a chiller adjustment cooling capacity determining unit, configured to:
when the strategy content is the adding machine cooling capacity in the cooling capacity adjusting process, executing the following steps: detecting whether a small cooler is started or not; if the small cooler is started, the small cooler is regulated to be a large cooler, otherwise, the other small cooler is started;
when the strategy content is the reducing machine cooling capacity in the cooling capacity adjusting process, executing the following steps: detecting whether a small cooler is started or not; if the small cooler is started, the small cooler is closed, otherwise, the other large cooler is set to be used as the small cooler, and the water outlet temperature of the cooler is set to be a preset temperature value;
the large cooler and the small cooler are distinguished according to rated refrigerating capacity and rated power.
Optionally, the policy type determining module 330 is specifically configured to:
if the generation time is within the non-fixed time period, determining that the strategy type of the chiller regulation strategy is a non-fixed regulation strategy; if the generation time is within a fixed time period, determining the strategy type of the cooling machine regulation strategy as a fixed regulation strategy; wherein the fixed time and the non-fixed time period are determined by a plurality of preset policy type determination time points.
Optionally, when the policy type is a non-fixed adjustment policy, the adjustment mode determining module 340 is specifically configured to:
if the strategy content is a shutdown instruction issued and the time length of any preset strategy type determination time point in the generation time interval does not exceed a preset time length threshold, determining the continuous current working state as an adjustment mode; otherwise, determining the strategy content as a regulating mode.
When the policy type is a fixed adjustment policy, the adjustment mode determining module 340 is specifically configured to:
if the generation time is after the target preset strategy type determination time point or the generation time does not exceed the next preset strategy type determination time point, determining the continuous current working state as an adjustment mode; the target preset strategy type determining time point is the latest preset strategy type determining time point; otherwise, determining the strategy content as a regulating mode
The device and the module after further description can execute the cold machine adjusting mode determining method provided by any embodiment of the invention, and have the corresponding functional modules and beneficial effects of the executing method.
Fig. 4 is a schematic structural diagram of an electronic device according to another embodiment of the present invention, and as shown in fig. 4, the device includes a processor 40, a memory 41, an input device 42 and an output device 43; the number of processors 40 in the device may be one or more, fig. 4 taking one processor 40 as an example; the processor 40, memory 41, input means 42 and output means 43 of the device may be connected by a bus or other means, in fig. 4 by way of example.
The memory 41 is used as a computer readable storage medium for storing software programs, computer executable programs, and modules, such as program instructions/modules (e.g., the environment data acquisition module 310, the policy content determination module 320, the policy type determination module 330, and the adjustment mode determination module 340) corresponding to the theme update method in the embodiment of the present invention. The processor 40 executes various functional applications of the apparatus and data processing by running software programs, instructions and modules stored in the memory 41, i.e., implements the above-described cold conditioning mode determining method.
The memory 41 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, at least one application program required for functions; the storage data area may store data created according to the use of the terminal, etc. In addition, memory 41 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage device. In some examples, memory 41 may further include memory located remotely from processor 40, which may be connected to the device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input means 42 may be used to receive entered numeric or character information and to generate key signal inputs related to user settings and function control of the device. The output means 43 may comprise a display device such as a display screen.
Yet another embodiment of the present invention provides a storage medium containing computer-executable instructions, which when executed by a computer processor, are for performing a search method, the method comprising:
acquiring environmental data;
determining strategy content of a cold machine regulation strategy according to the environment data and the cold machine working data;
acquiring the strategy content generation time of the cold machine regulation strategy, and determining the strategy type of the cold machine regulation strategy according to the generation time; wherein the policy types include fixed regulation policies and non-fixed regulation policies;
and determining a cold machine adjusting mode according to the strategy content and the strategy type of the cold machine adjusting strategy.
Of course, the storage medium containing the computer executable instructions provided in the embodiments of the present invention is not limited to the above-described method operations, and may also perform the related operations in the method for determining the cooling machine adjustment mode provided in any embodiment of the present invention.
From the above description of embodiments, it will be clear to a person skilled in the art that the present invention may be implemented by means of software and necessary general purpose hardware, but of course also by means of hardware, although in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, etc., and include several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments of the present invention.
It should be noted that, in the above-mentioned embodiments of the search apparatus, each unit and module included are only divided according to the functional logic, but not limited to the above-mentioned division, as long as the corresponding functions can be implemented; in addition, the specific names of the functional units are also only for distinguishing from each other, and are not used to limit the protection scope of the present invention.
Note that the above is only a preferred embodiment of the present invention and the technical principle applied. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, while the invention has been described in connection with the above embodiments, the invention is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the invention, which is set forth in the following claims.
Claims (7)
1. The method for determining the adjustment mode of the refrigerator is characterized by comprising the following steps of:
acquiring environmental data;
determining strategy content of a cold machine regulation strategy according to the environment data and the cold machine working data;
acquiring the strategy content generation time of the cold machine regulation strategy, and determining the strategy type of the cold machine regulation strategy according to the generation time; wherein the policy types include fixed regulation policies and non-fixed regulation policies;
determining a chiller adjusting mode according to the strategy content and the strategy type of the chiller adjusting strategy;
wherein, the determining the strategy type of the chiller adjusting strategy according to the generating time comprises:
if the generation time is within the non-fixed time period, determining that the strategy type of the chiller regulation strategy is a non-fixed regulation strategy;
if the generation time is within a fixed time period, determining the strategy type of the cooling machine regulation strategy as a fixed regulation strategy;
the fixed time and the non-fixed time period are determined through a plurality of preset strategy type determining time points, wherein the preset strategy type determining time points are time points with obvious outdoor temperature change or change trend in one day; the fixed regulation strategy is a non-adding strategy pushed in a fixed time period, and the non-fixed regulation strategy is an adding strategy which is increased and sent when the environment data meets a certain condition and is in the non-fixed time period;
when the strategy type is a non-fixed regulation strategy, determining a cold machine regulation mode according to the strategy content and the strategy type of the cold machine regulation strategy comprises:
if the strategy content is a shutdown instruction issued and the time length of any preset strategy type determination time point in the generation time interval does not exceed a preset time length threshold, determining the continuous current working state as an adjustment mode;
otherwise, determining the strategy content as an adjustment mode;
when the policy type is a fixed adjustment policy, determining a chiller adjustment mode according to policy content and policy type of the chiller adjustment policy includes:
if the generation time is after the target preset strategy type determination time point or the generation time does not exceed the next preset strategy type determination time point, determining the continuous current working state as an adjustment mode; the target preset strategy type determining time point is the latest preset strategy type determining time point;
otherwise, determining the strategy content as a regulating mode.
2. The method of claim 1, wherein determining policy content for a chiller tuning policy based on the environmental data and chiller operation data comprises:
determining the credibility of the environmental data, the outdoor environmental data and the indoor environmental data according to the environmental data;
determining a cold machine start-stop state and a plate exchanger installation state according to cold machine working data;
and determining strategy content of a cold machine regulation strategy based on the cold machine start-stop state, the outdoor environment data, the indoor environment data, the environment data credibility and the plate exchanger installation state.
3. The method according to claim 1 or 2, wherein the policy content of the chiller conditioning policy comprises at least one of:
issuing a shutdown instruction, emptying the startup time, pushing a regulation suggestion and regulating the cold quantity of the cold machine.
4. The method of claim 3, wherein said adjusting chiller refrigeration comprises adding chiller refrigeration and subtracting chiller refrigeration;
when the strategy content is the adding machine cooling capacity in the cooling capacity adjusting process, executing the following steps: detecting whether a small cooler is started or not; if the small cooler is started, the small cooler is regulated to be a large cooler, otherwise, the other small cooler is started;
when the strategy content is the reducing machine cooling capacity in the cooling capacity adjusting process, executing the following steps: detecting whether a small cooler is started or not; if the small cooler is started, the small cooler is closed, otherwise, the other large cooler is set to be used as the small cooler, and the water outlet temperature of the cooler is set to be a preset temperature value;
the large cooler and the small cooler are distinguished according to rated refrigerating capacity and rated power.
5. A chiller adjustment determination apparatus comprising:
the environment data acquisition module is used for acquiring environment data;
the strategy content determining module is used for determining strategy content of a cold machine regulating strategy according to the environment data and the cold machine working data;
the strategy type determining module is used for obtaining the strategy content generation time of the cold machine regulation strategy and determining the strategy type of the cold machine regulation strategy according to the generation time; wherein the policy types include fixed regulation policies and non-fixed regulation policies;
the adjustment mode determining module is used for determining a cold machine adjustment mode according to the strategy content and the strategy type of the cold machine adjustment strategy;
the policy type determining module is specifically configured to: if the generation time is within the non-fixed time period, determining that the strategy type of the chiller regulation strategy is a non-fixed regulation strategy; if the generation time is within a fixed time period, determining the strategy type of the cooling machine regulation strategy as a fixed regulation strategy;
the fixed time and the non-fixed time period are determined through a plurality of preset strategy type determining time points, wherein the preset strategy type determining time points are time points with obvious outdoor temperature change or change trend in one day; the fixed regulation strategy is a non-adding strategy pushed in a fixed time period, and the non-fixed regulation strategy is an adding strategy which is increased and sent when the environment data meets a certain condition and is in the non-fixed time period;
when the policy type is a non-fixed adjustment policy, the adjustment mode determining module is specifically configured to:
if the strategy content is a shutdown instruction issued and the time length of any preset strategy type determination time point in the generation time interval does not exceed a preset time length threshold, determining the continuous current working state as an adjustment mode; otherwise, determining the strategy content as an adjustment mode, wherein when the strategy type is a fixed adjustment strategy, the adjustment mode determining module is specifically configured to:
if the generation time is after the target preset strategy type determination time point or the generation time does not exceed the next preset strategy type determination time point, determining the continuous current working state as an adjustment mode; the target preset strategy type determining time point is the latest preset strategy type determining time point; otherwise, determining the strategy content as a regulating mode.
6. An electronic device, the device comprising:
one or more processors;
storage means for storing one or more programs,
when executed by the one or more processors, causes the one or more processors to implement the chiller adjustment mode determination method of any one of claims 1-4.
7. A storage medium containing computer executable instructions for performing the cold machine adjustment mode determination method of any one of claims 1-4 when executed by a computer processor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210194281.3A CN114608187B (en) | 2022-03-01 | 2022-03-01 | Method, device, equipment and storage medium for determining cooling machine adjusting mode |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210194281.3A CN114608187B (en) | 2022-03-01 | 2022-03-01 | Method, device, equipment and storage medium for determining cooling machine adjusting mode |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114608187A CN114608187A (en) | 2022-06-10 |
| CN114608187B true CN114608187B (en) | 2023-09-26 |
Family
ID=81860385
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210194281.3A Active CN114608187B (en) | 2022-03-01 | 2022-03-01 | Method, device, equipment and storage medium for determining cooling machine adjusting mode |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114608187B (en) |
Citations (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH08210686A (en) * | 1995-02-03 | 1996-08-20 | Hitachi Plant Eng & Constr Co Ltd | Air-conditioning system |
| KR19980044111A (en) * | 1996-12-05 | 1998-09-05 | 양승택 | Redundant Processor Function Verification Method in Asynchronous Transfer Mode Switching System |
| JP2011215880A (en) * | 2010-03-31 | 2011-10-27 | Sanyo Electric Co Ltd | Shutdown control device |
| EP2609473A1 (en) * | 2010-08-25 | 2013-07-03 | Schneider Electric Industries SAS | Method for determining parameters for controlling an hvac system |
| EP3029539A2 (en) * | 2014-12-04 | 2016-06-08 | Delta Electronics, Inc. | Temperature control system and temperature control method |
| CN105674651A (en) * | 2016-02-17 | 2016-06-15 | 广东美芝制冷设备有限公司 | Air conditioner and method for regulating content of refrigerant of air conditioner |
| CN107241440A (en) * | 2017-07-28 | 2017-10-10 | 曙光信息产业(北京)有限公司 | A kind of method for the Energy Saving Strategy for determining cluster |
| WO2018084849A1 (en) * | 2016-11-04 | 2018-05-11 | Ecoer Inc. | A dc variable speed compressor control method and control system |
| CN110285528A (en) * | 2019-06-26 | 2019-09-27 | 珠海格力电器股份有限公司 | The control method and device of air conditioner, air conditioner, household electrical appliance |
| KR20200052437A (en) * | 2018-10-29 | 2020-05-15 | 에스케이텔레콤 주식회사 | Method And Apparatus for Predicting Cooling Load in Small Scale Data |
| WO2020119038A1 (en) * | 2018-12-14 | 2020-06-18 | 广东美的暖通设备有限公司 | Method and device for controlling air conditioner and air conditioner |
| CN111891687A (en) * | 2020-07-29 | 2020-11-06 | 惠科股份有限公司 | Cargo carrying method and system |
| WO2020237468A1 (en) * | 2019-05-27 | 2020-12-03 | 西门子（中国）有限公司 | Method, apparatus and system for determining temperature setting value, and storage medium and processor |
| WO2020237668A1 (en) * | 2019-05-31 | 2020-12-03 | 亿可能源科技（上海）有限公司 | Air-conditioning system management method, air-conditioning system control method, storage medium and control platform |
| CN112212464A (en) * | 2020-10-27 | 2021-01-12 | 珠海格力电器股份有限公司 | Control system, method and device of data center air conditioning unit and storage medium |
| CN112594873A (en) * | 2020-12-14 | 2021-04-02 | 山东建筑大学 | Building central air conditioner demand response control method and system |
| CN112665143A (en) * | 2020-12-08 | 2021-04-16 | 珠海格力电器股份有限公司 | Method and equipment for intelligently regulating and controlling frequency conversion strategy based on deep learning |
| CN113405223A (en) * | 2021-06-23 | 2021-09-17 | 苏州智而卓数字科技有限公司 | Cold machine number control method based on GRNN and control system thereof |
| CN113757908A (en) * | 2021-09-28 | 2021-12-07 | 广东电网有限责任公司 | Thermal characteristic measuring method and system of air conditioning system, terminal and storage medium |
| CN113803843A (en) * | 2021-09-30 | 2021-12-17 | 珠海格力电器股份有限公司 | Multi-split refrigeration system control method and device, multi-split refrigeration system and air conditioner |
| CN114279075A (en) * | 2021-12-29 | 2022-04-05 | 博锐尚格科技股份有限公司 | Cold station startup control method and device |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9651929B2 (en) * | 2014-09-29 | 2017-05-16 | International Business Machines Corporation | HVAC system control integrated with demand response, on-site energy storage system and on-site energy generation system |
| JP6363126B2 (en) * | 2016-03-31 | 2018-07-25 | ゼネラル・エレクトリック・カンパニイ | Magnetic resonance imaging apparatus and program |
| US10223656B2 (en) * | 2016-05-10 | 2019-03-05 | Conectric, Llc | Method and system for minimizing time-variant energy demand and consumption of built environment |
| US11274849B2 (en) * | 2017-04-28 | 2022-03-15 | Johnson Controls Tyco IP Holdings LLP | Smart thermostat with model predictive control and demand response integration |
| JP7156217B2 (en) * | 2019-09-06 | 2022-10-19 | トヨタ自動車株式会社 | Vehicle remote indication system |
-
2022
- 2022-03-01 CN CN202210194281.3A patent/CN114608187B/en active Active
Patent Citations (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH08210686A (en) * | 1995-02-03 | 1996-08-20 | Hitachi Plant Eng & Constr Co Ltd | Air-conditioning system |
| KR19980044111A (en) * | 1996-12-05 | 1998-09-05 | 양승택 | Redundant Processor Function Verification Method in Asynchronous Transfer Mode Switching System |
| JP2011215880A (en) * | 2010-03-31 | 2011-10-27 | Sanyo Electric Co Ltd | Shutdown control device |
| EP2609473A1 (en) * | 2010-08-25 | 2013-07-03 | Schneider Electric Industries SAS | Method for determining parameters for controlling an hvac system |
| EP3029539A2 (en) * | 2014-12-04 | 2016-06-08 | Delta Electronics, Inc. | Temperature control system and temperature control method |
| CN105674651A (en) * | 2016-02-17 | 2016-06-15 | 广东美芝制冷设备有限公司 | Air conditioner and method for regulating content of refrigerant of air conditioner |
| WO2018084849A1 (en) * | 2016-11-04 | 2018-05-11 | Ecoer Inc. | A dc variable speed compressor control method and control system |
| CN107241440A (en) * | 2017-07-28 | 2017-10-10 | 曙光信息产业(北京)有限公司 | A kind of method for the Energy Saving Strategy for determining cluster |
| KR20200052437A (en) * | 2018-10-29 | 2020-05-15 | 에스케이텔레콤 주식회사 | Method And Apparatus for Predicting Cooling Load in Small Scale Data |
| WO2020119038A1 (en) * | 2018-12-14 | 2020-06-18 | 广东美的暖通设备有限公司 | Method and device for controlling air conditioner and air conditioner |
| WO2020237468A1 (en) * | 2019-05-27 | 2020-12-03 | 西门子（中国）有限公司 | Method, apparatus and system for determining temperature setting value, and storage medium and processor |
| WO2020237668A1 (en) * | 2019-05-31 | 2020-12-03 | 亿可能源科技（上海）有限公司 | Air-conditioning system management method, air-conditioning system control method, storage medium and control platform |
| CN110285528A (en) * | 2019-06-26 | 2019-09-27 | 珠海格力电器股份有限公司 | The control method and device of air conditioner, air conditioner, household electrical appliance |
| CN111891687A (en) * | 2020-07-29 | 2020-11-06 | 惠科股份有限公司 | Cargo carrying method and system |
| CN112212464A (en) * | 2020-10-27 | 2021-01-12 | 珠海格力电器股份有限公司 | Control system, method and device of data center air conditioning unit and storage medium |
| CN112665143A (en) * | 2020-12-08 | 2021-04-16 | 珠海格力电器股份有限公司 | Method and equipment for intelligently regulating and controlling frequency conversion strategy based on deep learning |
| CN112594873A (en) * | 2020-12-14 | 2021-04-02 | 山东建筑大学 | Building central air conditioner demand response control method and system |
| CN113405223A (en) * | 2021-06-23 | 2021-09-17 | 苏州智而卓数字科技有限公司 | Cold machine number control method based on GRNN and control system thereof |
| CN113757908A (en) * | 2021-09-28 | 2021-12-07 | 广东电网有限责任公司 | Thermal characteristic measuring method and system of air conditioning system, terminal and storage medium |
| CN113803843A (en) * | 2021-09-30 | 2021-12-17 | 珠海格力电器股份有限公司 | Multi-split refrigeration system control method and device, multi-split refrigeration system and air conditioner |
| CN114279075A (en) * | 2021-12-29 | 2022-04-05 | 博锐尚格科技股份有限公司 | Cold station startup control method and device |
Non-Patent Citations (1)
| Title |
|---|
| 崔凯莉 ; 赵麟 ; 白日磊 ; 周仲一 ; 杨志伟.基于动态数据的冷站智慧控制系统及应用案例分析.《建设科技》.2021,63-67. * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114608187A (en) | 2022-06-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11796205B2 (en) | Systems and methods of optimizing HVAC control in a building or network of buildings | |
| CN111237989B (en) | Building ventilation air conditioner control method and device based on load prediction | |
| CN101424436A (en) | Intelligent optimizing control system and method for central air-conditioning | |
| US20210222899A1 (en) | Method and device for controlling electric heating of air conditioner compressor | |
| CN110222398B (en) | Artificial intelligence control method and device for water chilling unit, storage medium and terminal equipment | |
| CN112283890A (en) | Cold and heat quantity control method and device suitable for building heating and ventilation equipment monitoring system | |
| CN112747419B (en) | Wind-water linkage control method, device and equipment for central air conditioner and storage medium | |
| CN106969477A (en) | Air conditioning control method and air-conditioner controller | |
| CN201335488Y (en) | Intelligently optimized control device for central air conditioner | |
| JP2001065959A (en) | Control method for energy supplying instrument and device for the same | |
| CN113847715B (en) | Method and device for regulating and controlling air conditioner of base station, electronic equipment and medium | |
| CA3145359A1 (en) | Heating, ventilation, and air conditioning system control using adaptive occupancy scheduling | |
| CN114608187B (en) | Method, device, equipment and storage medium for determining cooling machine adjusting mode | |
| Beghi et al. | Load forecasting for the efficient energy management of HVAC systems | |
| CN110762739B (en) | Data center air conditioner control method, device, equipment and storage medium | |
| CN116261300A (en) | Combined optimization method and device for refrigerating equipment and airflow organization of data center | |
| CN114126350A (en) | Control method and device of indirect evaporative cooling system and electronic equipment | |
| CN115560430A (en) | Air conditioner model optimization method and system based on cloud-edge computing | |
| CN114222477A (en) | Energy-saving control method and device for data center, storage medium and program product | |
| CN213630850U (en) | Global optimal control system of refrigeration station | |
| CN115235046B (en) | Optimized control method for central air conditioner cold source demand side response | |
| CN111474858B (en) | Building energy management control system and method | |
| CN115289602B (en) | Method, computing device, and medium for determining target operation time of central air conditioning system | |
| JPH10300167A (en) | Management control device of air conditioning heat source equipment | |
| CN112902392A (en) | Adaptive adjustment air conditioner control method, electronic equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |