CN117704566A - Indoor temperature and humidity control system and method based on new energy wind cabinet data identification - Google Patents
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Abstract
The invention discloses an indoor temperature and humidity control system and method based on new energy wind cabinet data identification, and belongs to the field of temperature control.
Description
Technical Field
The invention belongs to the technical field of temperature control, and particularly relates to an indoor temperature and humidity control system and method based on new energy wind cabinet data identification.
Background
In some indoor places with requirements on environment, the monitoring and control of temperature and humidity are necessary, and the general thermometer and humidifier are adopted independently, but all the humidifiers are required to be monitored and started by personnel, so that the pressure of service personnel is high in a nursing home with more lodging personnel, each room is required to be patrolled, the time is long, the strength is high, and the efficient management and control are not facilitated.
For example, chinese patent publication No. CN113625807B discloses an indoor spray drawing temperature and humidity control system, which includes a temperature and humidity sensor disposed on a spray drawing machine, the temperature and humidity sensor outputting temperature data and humidity data, and a sprayer and a heater disposed in a middle area of a workshop; the sprayer is rotationally connected with a transverse jet head, a spray jet orifice is arranged on the transverse jet head, the sprayer outputs spray, the sprayer is communicated with the spray jet orifice through the transverse jet head, and a straight line of the spray jet orifice deviates from a rotation connection point of the transverse jet head; the heater comprises a heat radiation ring surrounding the spray jet orifice, and the heat radiation ring outputs heat radiation with set power; the system also comprises a controller, wherein the controller is electrically connected with the sprayer, the heater and the temperature and humidity sensor, receives temperature data and humidity data, adjusts the spraying speed and the heat radiation power according to a preset temperature and humidity relation table, and has the advantage of improving the temperature and humidity control effect of the middle area of the workshop;
meanwhile, for example, in chinese patent application publication No. CN108052144a, an indoor planting-based temperature and humidity control system is provided, and a temperature acquisition unit is used for acquiring environmental temperature information in a planting area and soil temperature information in the planting area, and transmitting the environmental temperature information and the soil temperature information to a management center; the humidity acquisition unit is used for acquiring the environmental humidity information in the planting area and the soil humidity information in the planting area and transmitting the environmental humidity information and the soil humidity information to the management center; the temperature adjusting unit is used for receiving the instruction sent by the management center and adjusting the environment temperature and the soil temperature; the humidity adjusting unit is used for receiving the instruction sent by the management center and adjusting the environmental humidity and the soil humidity; the management center is used for receiving the environmental temperature and humidity information and the soil temperature and humidity information and sending out instructions. And the optimal temperature and humidity required by plant growth is adjusted through a management center, so that the plants in the planting area are in an optimal temperature and humidity environment.
The problems proposed in the background art exist in the above patents: the existing indoor temperature and humidity control is mainly realized by adjusting the indoor temperature and humidity in real time through an air conditioner remote controller, the indoor temperature and humidity cannot be accurately adjusted in advance according to the indoor environment and the outdoor environment, the indoor environment cannot be kept in a specified temperature and humidity range when entering the indoor environment, and healthy use of infected people or people with special requirements on temperature and humidity is not facilitated.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides an indoor temperature and humidity control system and method based on new energy wind cabinet data identification.
In order to achieve the above purpose, the present invention provides the following technical solutions:
the indoor temperature and humidity control method based on new energy wind cabinet data identification comprises the following specific steps:
s1, acquiring indoor temperature and humidity change curve data, simultaneously acquiring an outdoor temperature and humidity change curve estimated by weather forecast, and simultaneously issuing an arrival time and a required temperature and humidity instruction by a user through a mobile phone APP;
s2, constructing a neural network model based on the acquired indoor temperature and humidity change curve data and the acquired weather forecast estimated outdoor temperature and humidity change curve, inputting the predicted outdoor temperature and humidity of the arrival time, and outputting the estimated indoor temperature and humidity of the arrival time;
s3, extracting new energy wind cabinet data obtained through monitoring, and importing the new energy wind cabinet data into a wind cabinet output cold quantity calculation strategy to derive the relation between the actual output cold quantity speed and the actual power;
s4, introducing the indoor temperature and humidity and the required temperature and humidity of the estimated arrival time into a required cooling capacity calculation strategy to calculate required air flow cooling capacity and water quantity;
s5, substituting the needed air flow cooling capacity and water quantity into the relation between the actual output cooling capacity and the actual power to calculate the needed actual output power;
s6, comparing the required actual output power with the safe power of the wind cabinet, controlling the wind cabinet to operate according to the required actual output power if the required actual output power is within the safe power range of the wind cabinet, and issuing a control instruction which cannot be performed to the user mobile phone APP if the required actual output power is not within the safe power range of the wind cabinet so as to remind the user of changing the required temperature and humidity instruction.
Specifically, the step S1 includes the following specific steps:
s11, acquiring an indoor temperature and humidity change curve through a temperature and humidity sensing module, and acquiring an outdoor temperature and humidity change curve estimated by weather forecast through a weather forecast receiving module;
s12, a user issues an arrival time and a required temperature and humidity instruction through a mobile phone APP, and a control system receives the instruction issued by the user, so that the temperature and humidity control system operates; the method has the advantages that the mobile phone APP is used for issuing the arrival time and the needed temperature and humidity instruction, so that accurate adjustment of indoor temperature and humidity is facilitated, indoor temperature and humidity are kept in a specified temperature and humidity range, and healthy use of infected people or people with special requirements on temperature and humidity is facilitated.
Specifically, the specific steps of S2 are as follows:
s21, acquiring indoor temperature and humidity change curve data and an outdoor temperature and humidity change curve estimated by the acquired weather forecast to construct a parameter model equation, and dividing the data into a coefficient training set of 70% and a coefficient testing set of 30%; carrying out parameter training on a parameter model equation to construct an outdoor temperature and humidity change curve which is input into weather forecast prediction, outputting a neural network model which is input into indoor temperature and humidity change curve data, inputting a 70% coefficient training set into the neural network model for training to obtain an initial neural network model, testing the initial neural network model by using a 30% coefficient test set, and outputting an optimal initial neural network model which meets the accuracy of the indoor temperature and humidity change curve data as the parameter model equation, wherein an output strategy formula of the parameter model equation is as follows:wherein->For n+1 layer p term neuron outputs, said +.>For the connection weight of the n-th layer neuron i and the n+1 layer p item neuron, the +.>Represents a layer n neuron i, said +.>Representing the bias of the n-layer neurons i to n+1-layer p term neurons, wherein sig () represents a Sigmoid activation function, and k is the number of terms of the n-layer neurons;
s22, inputting the extracted predicted arrival time outdoor temperature and humidity in the neural network model to output the estimated arrival time indoor temperature and humidity, so that the arrival time indoor temperature and humidity can be accurately predicted through the outdoor temperature and humidity data, the accuracy of the arrival time indoor temperature and humidity prediction is improved, and the accuracy of temperature and humidity adjustment is further improved.
Specifically, the specific steps of the air cabinet output cold amount calculation strategy in S3 are as follows:
s31, collecting working function data of each working point by data collecting modules distributed at each working point of the new energy wind cabinet; it should be noted that, the working function data are the function data of each working point of the new energy wind cabinet, for example, after passing through the dust screen, the cooling capacity is reduced by twenty percent, and the function data of the dust screen are the cooling capacity is reduced by twenty percent;
s32, extracting working function data, output power data and air cabinet output cold quantity data of all previous working points;
s33, importing the working function data of each working point and the working function data of each previous working point into a function data phase difference value calculation formula to calculate a function data phase difference value, wherein the function data phase difference value calculation formula is as follows:wherein t is the number of items of work function data, x i For the ith item in the working function data of each working point of this time,/item>An i-th item, a in the working function data of each working point in the past i For the duty factor of the ith item in the working function data, it should be noted here that a here i The acquisition method comprises the steps of substituting 500 groups of historical working function data of each working point, air cabinet output cold quantity data and output power data into fitting software to output the best a i Is a value of (2);
s34, taking a change curve of output power data of a corresponding past working point and output cold data of an air cabinet, which has the smallest difference value with the functional data of the working function data of each working point, and importing the change curve into fitting software to derive an equation f=k (x) of actual output cold speed and actual power, wherein x is the actual output cold speed, and f is the actual power.
Specifically, the cold amount calculation strategy in S4 includes the following specific steps:
s41, extracting the estimated indoor temperature and the required temperature of the arrival time, obtaining a temperature phase difference value, and calculating the cold quantity required for adjusting the indoor temperature to the required temperature, wherein the calculation formula is as follows: q=cρv (T 1 -T 2 ) Wherein c is the specific heat of indoor air, ρ is the density of indoor air, V is the indoor volume, T 1 For the estimated time of arrival indoor temperature T 2 Is the required temperature;
s42, simultaneously calculating the water quantity required to be released for adjusting the estimated arrival time indoor humidity to the required humidity, V 2 ＝V(S 1 -S 2 ) Wherein S is 1 For the estimated time of arrival indoor humidity, S 2 Is the desired humidity.
Specifically, the specific content in S5 is as follows:
calculating a required cooling capacity release speed and a water output speed, wherein a cooling capacity release speed calculation formula is as follows:wherein t is the time interval of arrival time and release time, and the water output speed isSubstituting the obtained cold energy release speed into an equation f=k (x) of the actual output cold energy speed and the actual power in the step S34 to derive the power required by releasing the cold energy, substituting the water output speed into a water pump power-water output speed formula to calculate the water pump power, and adding the power required by releasing the cold energy and the water pump power to obtain the required actual output power.
Specifically, an indoor temperature and humidity control system based on new energy wind cabinet data identification is realized based on the indoor temperature and humidity control method based on new energy wind cabinet data identification, and the system specifically comprises the following steps: the system comprises an information acquisition module, an instruction issuing module, a neural network construction module, an air cabinet output cold quantity calculating module, a required cold quantity calculating module, an actual output power calculating module, a power comparison module, a data feedback module and a control module, wherein the information acquisition module is used for acquiring indoor temperature and humidity change curve data and simultaneously acquiring an outdoor temperature and humidity change curve estimated by weather forecast, the instruction issuing module is used for issuing an arrival time and a required temperature and humidity instruction by a user through a mobile phone APP, and the neural network construction module is used for constructing a neural network model based on the acquired indoor temperature and humidity change curve data and the acquired outdoor temperature and humidity change curve estimated by weather forecast.
Specifically, the wind cabinet output cold quantity calculating module is used for extracting new energy wind cabinet data obtained through monitoring, guiding the relation between actual output cold quantity speed and actual power into a wind cabinet output cold quantity calculating strategy, the required cold quantity calculating module is used for guiding the estimated indoor temperature and humidity and the required temperature and humidity into the required cold quantity calculating strategy to calculate required airflow cold quantity and water quantity, the actual output power calculating module is used for substituting the required airflow cold quantity and the water quantity into the relation between the actual output cold quantity and the actual power to calculate required actual output power, and the power comparison module is used for comparing the required actual output power with the wind cabinet safety power, and if the required actual output power is within the wind cabinet safety power range, the wind cabinet is controlled to operate according to the required actual output power.
Specifically, the data feedback module is used for issuing a control instruction which can not be performed to the user mobile phone APP to remind the user of changing the needed temperature and humidity instruction, and the control module is used for controlling the operation of the information acquisition module, the instruction issuing module, the neural network construction module, the air cabinet output cold amount calculation module, the needed cold amount calculation module, the actual output power calculation module, the power comparison module and the data feedback module.
Specifically, an electronic device includes: a processor and a memory, wherein the memory stores a computer program for the processor to call;
and the processor executes the indoor temperature and humidity control method based on the new energy wind cabinet data identification by calling the computer program stored in the memory.
Specifically, a computer readable storage medium stores instructions that, when executed on a computer, cause the computer to execute the indoor temperature and humidity control method based on new energy wind cabinet data identification as described above.
Compared with the prior art, the invention has the beneficial effects that:
according to the method, indoor temperature and humidity change curve data are obtained, meanwhile, an estimated outdoor temperature and humidity change curve of weather forecast is obtained, meanwhile, a user issues an arrival time and a required temperature and humidity instruction through a mobile phone APP, a neural network model is built based on the obtained indoor temperature and humidity change curve data and the estimated outdoor temperature and humidity change curve of the weather forecast, the estimated arrival time outdoor temperature and humidity is input, the estimated arrival time indoor temperature and humidity is output, new energy air cabinet data obtained through monitoring are extracted, the relation between the actual output cold quantity speed and the actual power is derived in an air cabinet output cold quantity calculation strategy, the estimated arrival time indoor temperature and humidity and the required temperature and humidity are led in the required cold quantity calculation strategy to calculate the required air flow cold quantity and water quantity, the required air flow cold quantity and water quantity are substituted into the relation between the actual output cold quantity and the actual power to calculate the required actual output power, the required actual output power is compared with the safe power of the air cabinet, if the required actual output power is within the safe power range of the air cabinet, the air cabinet is controlled to operate according to the required actual output power, if the required actual output power is not within the safe power range of the air cabinet, the relation between the actual output power and the actual output power is not within the safe power range of the air cabinet, the indoor temperature and humidity of the user APP is reminded of the user, and the user is enabled to be accurately adjusted in the indoor temperature and humidity instruction to be kept in the indoor temperature and humidity setting range.
Drawings
FIG. 1 is a flow chart of an indoor temperature and humidity control method based on new energy wind cabinet data identification;
fig. 2 is a schematic diagram of a specific flow of step S3 of the indoor temperature and humidity control method based on new energy wind cabinet data identification according to the present invention;
FIG. 3 is a schematic diagram of a specific flow of step S4 of the indoor temperature and humidity control method based on new energy wind cabinet data identification;
fig. 4 is a schematic diagram of an indoor temperature and humidity control system architecture based on new energy wind cabinet data identification.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments.
Example 1
Referring to fig. 1-3, an embodiment of the present invention is provided: the indoor temperature and humidity control method based on new energy wind cabinet data identification comprises the following specific steps:
s1, acquiring indoor temperature and humidity change curve data, simultaneously acquiring an outdoor temperature and humidity change curve estimated by weather forecast, and simultaneously issuing an arrival time and a required temperature and humidity instruction by a user through a mobile phone APP;
in this embodiment, S1 includes the following specific steps:
s11, acquiring an indoor temperature and humidity change curve through a temperature and humidity sensing module, and acquiring an outdoor temperature and humidity change curve estimated by weather forecast through a weather forecast receiving module;
s12, a user issues an arrival time and a required temperature and humidity instruction through a mobile phone APP, and a control system receives the instruction issued by the user, so that the temperature and humidity control system operates; the method has the advantages that the mobile phone APP is used for issuing the arrival time and the needed temperature and humidity instruction, so that accurate adjustment of indoor temperature and humidity is facilitated, the indoor temperature and humidity are kept within the designated temperature and humidity range, and healthy use of infected people or people with special requirements on temperature and humidity is facilitated;
s2, constructing a neural network model based on the acquired indoor temperature and humidity change curve data and the acquired weather forecast estimated outdoor temperature and humidity change curve, inputting the predicted outdoor temperature and humidity of the arrival time, and outputting the estimated indoor temperature and humidity of the arrival time;
in this embodiment, the specific steps of S2 are as follows:
s21, acquiring indoor temperature and humidity change curve data and an outdoor temperature and humidity change curve estimated by the acquired weather forecast to construct a parameter model equation, and dividing the data into a coefficient training set of 70% and a coefficient testing set of 30%; the method comprises the steps of performing parameter training on a parameter model equation to construct an outdoor temperature and humidity change curve which is input into weather forecast prediction, outputting a neural network model which is input into indoor temperature and humidity change curve data, inputting a 70% coefficient training set into the neural network model for training to obtain an initial neural network model, testing the initial neural network model by using a 30% coefficient test set, and outputting an optimal initial neural network model which meets the accuracy of the indoor temperature and humidity change curve data as the parameter model equation, wherein an output strategy formula of the parameter model equation is as follows:wherein->For n+1 layer p term neuron outputs, < ->For the connection weight of the n-th layer neuron i and the n+1 layer p term neuron,/>Representing the neurons i of the n-th layer,representing the bias of the n-layer neurons i to n+1 layer p term neurons, sig () represents a Sigmoid activation function, and k is the number of terms of the n-layer neurons;
s22, inputting the extracted predicted arrival time outdoor temperature and humidity in the neural network model to output the estimated arrival time indoor temperature and humidity, so that the arrival time indoor temperature and humidity can be accurately predicted through the outdoor temperature and humidity data, the accuracy of the arrival time indoor temperature and humidity prediction is improved, and the accuracy of temperature and humidity adjustment is further improved;
the following is a neural network model based on the C language, which is used for predicting an indoor temperature and humidity change curve. The model uses an outdoor temperature and humidity change curve estimated by weather forecast as input, and compares the outdoor temperature and humidity change curve with an actual indoor temperature and humidity change curve. We split the data into a 70% training set and a 30% test set.
Note that this is just one example code, which needs to be adjusted according to the actual requirements.
S3, extracting new energy wind cabinet data obtained through monitoring, and importing the new energy wind cabinet data into a wind cabinet output cold quantity calculation strategy to derive the relation between the actual output cold quantity speed and the actual power;
in this embodiment, the specific steps of the air cabinet output cold amount calculation strategy in S3 are as follows:
s31, collecting working function data of each working point by data collecting modules distributed at each working point of the new energy wind cabinet; it should be noted that, the working function data are the function data of each working point of the new energy wind cabinet, for example, after passing through the dust screen, the cooling capacity is reduced by twenty percent, and the function data of the dust screen are the cooling capacity is reduced by twenty percent;
s32, extracting working function data, output power data and air cabinet output cold quantity data of all previous working points;
S33、leading the working function data of each working point and the working function data of each previous working point into a function data phase difference value calculation formula to calculate a function data phase difference value, wherein the function data phase difference value calculation formula is as follows:wherein t is the number of items of work function data, x i For the ith item in the working function data of each working point of this time,/item>An i-th item, a in the working function data of each working point in the past i For the duty factor of the ith item in the working function data, it should be noted here that a here i The acquisition method comprises the steps of substituting 500 groups of historical working function data of each working point, air cabinet output cold quantity data and output power data into fitting software to output the best a i Is a value of (2);
s34, taking a change curve of output power data of a corresponding past working point and output cold data of an air cabinet, which has the smallest difference value with the functional data of the working function data of each working point, and importing the change curve into fitting software to derive an equation f=k (x) of actual output cold speed and actual power, wherein x is the actual output cold speed, and f is the actual power;
s4, introducing the indoor temperature and humidity and the required temperature and humidity of the estimated arrival time into a required cooling capacity calculation strategy to calculate required air flow cooling capacity and water quantity;
in this embodiment, the cold calculation strategy in S4 includes the following specific steps:
s41, extracting the estimated indoor temperature and the required temperature of the arrival time, obtaining a temperature phase difference value, and calculating the cold quantity required for adjusting the indoor temperature to the required temperature, wherein the calculation formula is as follows: q=cρv (T 1 -T 2 ) Wherein c is the specific heat of indoor air, ρ is the density of indoor air, V is the indoor volume, T 1 For the estimated time of arrival indoor temperature T 2 Is the required temperature;
s42, calculating the estimated arrival time simultaneouslyThe humidity in the compartment is adjusted to the required humidity and the water quantity is required to be released, V 2 ＝V(S 1 -S 2 ) Wherein S is 1 For the estimated time of arrival indoor humidity, S 2 Is the required humidity;
s5, substituting the needed air flow cooling capacity and water quantity into the relation between the actual output cooling capacity and the actual power to calculate the needed actual output power;
in this embodiment, the specific content in S5 is as follows:
calculating a required cooling capacity release speed and a water output speed, wherein a cooling capacity release speed calculation formula is as follows:wherein t is the time interval of arrival time and release time, and the water output speed isSubstituting the obtained cold energy release speed into an equation f=k (x) of the actual output cold energy speed and the actual power in the step S34 to derive the power required by releasing the cold energy, substituting the water output speed into a water pump power-water output speed formula to calculate the water pump power, and adding the power required by releasing the cold energy and the water pump power to obtain the required actual output power;
s6, comparing the required actual output power with the safe power of the wind cabinet, controlling the wind cabinet to operate according to the required actual output power if the required actual output power is within the safe power range of the wind cabinet, and issuing a control instruction which cannot be performed to the user mobile phone APP if the required actual output power is not within the safe power range of the wind cabinet so as to remind the user of changing the required temperature and humidity instruction.
The implementation of the embodiment can be realized: acquiring indoor temperature and humidity change curve data, acquiring an outdoor temperature and humidity change curve estimated by weather forecast, simultaneously issuing an arrival time and a required temperature and humidity instruction by a user through a mobile phone APP, constructing a neural network model based on the acquired indoor temperature and humidity change curve data and the acquired outdoor temperature and humidity change curve estimated by weather forecast, inputting the predicted arrival time outdoor temperature and humidity, outputting the estimated arrival time indoor temperature and humidity, extracting new energy air cabinet data obtained by monitoring, guiding the new energy air cabinet data into an air cabinet output cold quantity calculation strategy to derive the relation between the actual output cold quantity speed and the actual power, guiding the estimated arrival time indoor temperature and humidity and the required temperature and humidity into the required cold quantity calculation strategy to calculate the required actual output power by calculating the required airflow cold quantity and the required temperature and humidity, comparing the required actual output power with the safe power of the air cabinet, controlling the air cabinet to operate according to the required actual output power if the required actual output power is not in the safe power range of the air cabinet, issuing a control instruction to the mobile phone of the user, reminding the user of changing the required air cabinet, and accurately regulating the indoor temperature and humidity in advance according to the required temperature and humidity instruction.
Example 2
As shown in fig. 4, the indoor temperature and humidity control system based on new energy wind cabinet data identification is implemented based on the indoor temperature and humidity control method based on new energy wind cabinet data identification, and specifically includes: the system comprises an information acquisition module, an instruction issuing module, a neural network construction module, an air cabinet output cold quantity calculating module, a required cold quantity calculating module, an actual output power calculating module, a power comparison module, a data feedback module and a control module, wherein the information acquisition module is used for acquiring indoor temperature and humidity change curve data and simultaneously acquiring an outdoor temperature and humidity change curve estimated by weather forecast, the instruction issuing module is used for issuing an arrival time and a required temperature and humidity instruction by a user through a mobile phone APP, and the neural network construction module is used for constructing a neural network model based on the acquired indoor temperature and humidity change curve data and the acquired outdoor temperature and humidity change curve estimated by weather forecast.
In this embodiment, the wind cabinet output cold amount calculating module is configured to extract new energy wind cabinet data obtained by monitoring, guide the new energy wind cabinet data into a wind cabinet output cold amount calculating strategy, guide the relation between an actual output cold amount speed and an actual power, the required cold amount calculating module is configured to guide the estimated indoor temperature and humidity of the arrival time and the required temperature and humidity into the required cold amount calculating strategy to calculate the required airflow cold amount and water amount, the actual output power calculating module is configured to substitute the required airflow cold amount and water amount into the relation between the actual output cold amount and the actual power to calculate the required actual output power, and the power comparison module is configured to compare the required actual output power with the wind cabinet safety power, and if the required actual output power is within the wind cabinet safety power range, control the wind cabinet to operate according to the required actual output power.
In this embodiment, the data feedback module is configured to issue a control instruction to the user mobile phone APP, and remind the user of changing a required temperature and humidity instruction, where the control module is configured to control operations of the information acquisition module, the instruction issue module, the neural network construction module, the air cabinet output cold amount calculation module, the required cold amount calculation module, the actual output power calculation module, the power comparison module, and the data feedback module.
Example 3
The present embodiment provides an electronic device including: a processor and a memory, wherein the memory stores a computer program for the processor to call;
the processor executes the indoor temperature and humidity control method based on the new energy wind cabinet data identification by calling the computer program stored in the memory.
The electronic device can generate larger difference due to different configurations or performances, and can comprise one or more processors (Central Processing Units, CPU) and one or more memories, wherein at least one computer program is stored in the memories, and the computer program is loaded and executed by the processors to realize the indoor temperature and humidity control method based on new energy wind cabinet data identification provided by the embodiment of the method. The electronic device can also include other components for implementing the functions of the device, for example, the electronic device can also have wired or wireless network interfaces, input-output interfaces, and the like, for inputting and outputting data. The present embodiment is not described herein.
Example 4
The present embodiment proposes a computer-readable storage medium having stored thereon an erasable computer program;
when the computer program runs on the computer equipment, the computer equipment is enabled to execute the indoor temperature and humidity control method based on the new energy wind cabinet data identification.
For example, the computer readable storage medium can be Read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), compact disk Read-Only Memory (Compact Disc Read-Only Memory, CD-ROM), magnetic tape, floppy disk, optical data storage device, etc.
It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.
It should be understood that determining B from a does not mean determining B from a alone, but can also determine B from a and/or other information.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions in accordance with embodiments of the present invention are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another, for example, by way of wired or/and wireless networks from one website site, computer, server, or data center to another. Computer readable storage media can be any available media that can be accessed by a computer or data storage devices, such as servers, data centers, etc. that contain one or more collections of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely one, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (9)
1. The indoor temperature and humidity control method based on new energy wind cabinet data identification is characterized by comprising the following specific steps of:
s1, acquiring indoor temperature and humidity change curve data, simultaneously acquiring an outdoor temperature and humidity change curve estimated by weather forecast, and simultaneously issuing an arrival time and a required temperature and humidity instruction by a user through a mobile phone APP;
s2, constructing a neural network model based on the acquired indoor temperature and humidity change curve data and the acquired weather forecast estimated outdoor temperature and humidity change curve, inputting the predicted outdoor temperature and humidity of the arrival time, and outputting the estimated indoor temperature and humidity of the arrival time;
s3, extracting new energy wind cabinet data obtained through monitoring, and importing the new energy wind cabinet data into a wind cabinet output cold quantity calculation strategy to derive the relation between the actual output cold quantity speed and the actual power;
s4, introducing the indoor temperature and humidity and the required temperature and humidity of the estimated arrival time into a required cooling capacity calculation strategy to calculate required air flow cooling capacity and water quantity;
s5, substituting the needed air flow cooling capacity and water quantity into the relation between the actual output cooling capacity and the actual power to calculate the needed actual output power;
s6, comparing the required actual output power with the safe power of the wind cabinet, controlling the wind cabinet to operate according to the required actual output power if the required actual output power is within the safe power range of the wind cabinet, and issuing a control instruction which cannot be performed to the user mobile phone APP if the required actual output power is not within the safe power range of the wind cabinet so as to remind the user of changing the required temperature and humidity instruction.
2. The indoor temperature and humidity control method based on new energy wind cabinet data identification of claim 1, wherein the step S1 comprises the following specific steps:
s11, acquiring an indoor temperature and humidity change curve through a temperature and humidity sensing module, and acquiring an outdoor temperature and humidity change curve estimated by weather forecast through a weather forecast receiving module;
s12, a user issues an arrival time and a required temperature and humidity instruction through the mobile phone APP, and the control system receives the instruction issued by the user, so that the temperature and humidity control system operates.
3. The indoor temperature and humidity control method based on new energy wind cabinet data identification as claimed in claim 2, wherein the specific steps of S2 are as follows:
s21, acquiring indoor temperature and humidity change curve data and an outdoor temperature and humidity change curve estimated by the acquired weather forecast to construct a parameter model equation, and dividing the data into a coefficient training set of 70% and a coefficient testing set of 30%; the parameter model equation is subjected to parameter training construction and input into weather forecastThe estimated outdoor temperature and humidity change curve is output as a neural network model of indoor temperature and humidity change curve data, a 70% coefficient training set is input into the neural network model for training to obtain an initial neural network model, the initial neural network model is tested by using a 30% coefficient test set, and an optimal initial neural network model meeting the accuracy of the indoor temperature and humidity change curve data is output as a parameter model equation, wherein an output strategy formula of the parameter model equation is as follows:wherein->For n+1 layer p term neuron outputs, said +.>For the connection weight of the n-th layer neuron i and the n+1 layer p item neuron, the +.>Represents a layer n neuron i, said +.>Representing the bias of the n-layer neurons i to n+1-layer p term neurons, wherein sig () represents a Sigmoid activation function, and k is the number of terms of the n-layer neurons;
s22, inputting the extracted predicted arrival time outdoor temperature and humidity in the neural network model to output the estimated arrival time indoor temperature and humidity.
4. The indoor temperature and humidity control method based on new energy wind cabinet data identification according to claim 3, wherein the specific steps of the wind cabinet output cold amount calculation strategy in S3 are as follows:
s31, collecting working function data of each working point by data collecting modules distributed at each working point of the new energy wind cabinet; the working function data are the function data of each working point of the new energy wind cabinet;
s32, extracting working function data, output power data and air cabinet output cold quantity data of all previous working points;
s33, importing the working function data of each working point and the working function data of each previous working point into a function data phase difference value calculation formula to calculate a function data phase difference value, wherein the function data phase difference value calculation formula is as follows:wherein t is the number of items of work function data, x i For the ith item in the working function data of each working point of this time,/item>An i-th item, a in the working function data of each working point in the past i The duty ratio coefficient of the ith item in the working function data;
s34, taking a change curve of output power data of a corresponding past working point and output cold data of an air cabinet, which has the smallest difference value with the functional data of the working function data of each working point, and importing the change curve into fitting software to derive an equation f=k (x) of actual output cold speed and actual power, wherein x is the actual output cold speed, and f is the actual power.
5. The indoor temperature and humidity control method based on new energy wind cabinet data identification of claim 4, wherein the cold amount calculation strategy in S4 comprises the following specific steps:
s41, extracting the estimated indoor temperature and the required temperature of the arrival time, obtaining a temperature phase difference value, and calculating the cold quantity required for adjusting the indoor temperature to the required temperature, wherein the calculation formula is as follows: q=cρv (T 1 -T 2 ) Wherein c is the specific heat of indoor air, ρ is the density of indoor air, V is the indoor volume, T 1 For the estimated time of arrival indoor temperature T 2 Is the required temperature;
s42, simultaneously calculating the water quantity required to be released for adjusting the estimated arrival time indoor humidity to the required humidity, V 2 ＝V(S 1 -S 2 ) Wherein S is 1 For the estimated time of arrival indoor humidity, S 2 Is the desired humidity.
6. The indoor temperature and humidity control method based on new energy wind cabinet data identification of claim 5, wherein the specific content in S5 is as follows:
calculating a required cooling capacity release speed and a water output speed, wherein a cooling capacity release speed calculation formula is as follows:wherein t is the time interval of arrival time and release time, and the water output speed isSubstituting the obtained cold energy release speed into an equation f=k (x) of the actual output cold energy speed and the actual power in the step S34 to derive the power required by releasing the cold energy, substituting the water output speed into a water pump power-water output speed formula to calculate the water pump power, and adding the power required by releasing the cold energy and the water pump power to obtain the required actual output power.
7. Indoor temperature and humidity control system based on new energy wind cabinet data identification, which is realized based on the indoor temperature and humidity control method based on new energy wind cabinet data identification according to any one of claims 1-6, is characterized by specifically comprising: the system comprises an information acquisition module, an instruction issuing module, a neural network construction module, an air cabinet output cold quantity calculating module, a required cold quantity calculating module, an actual output power calculating module, a power comparison module, a data feedback module and a control module, wherein the information acquisition module is used for acquiring indoor temperature and humidity change curve data and simultaneously acquiring an outdoor temperature and humidity change curve estimated by weather forecast, the instruction issuing module is used for issuing an arrival time and a required temperature and humidity instruction by a user through a mobile phone APP, and the neural network construction module is used for constructing a neural network model based on the acquired indoor temperature and humidity change curve data and the acquired outdoor temperature and humidity change curve estimated by weather forecast.
8. The indoor temperature and humidity control system based on new energy wind cabinet data identification as claimed in claim 7, wherein the wind cabinet output cold amount calculation module is used for extracting new energy wind cabinet data obtained through monitoring, guiding the relation between actual output cold amount speed and actual power into a wind cabinet output cold amount calculation strategy, the required cold amount calculation module is used for guiding estimated indoor temperature and humidity and required temperature and humidity into the required cold amount calculation strategy to calculate required airflow cold amount and water amount, the actual output power calculation module is used for substituting the required airflow cold amount and water amount into the relation between actual output cold amount and actual power to calculate required actual output power, and the power comparison module is used for comparing the required actual output power with wind cabinet safety power, and if the required actual output power is within the wind cabinet safety power range, the wind cabinet is controlled to operate according to the required actual output power.
9. The indoor temperature and humidity control system based on new energy wind cabinet data identification as claimed in claim 8, wherein the data feedback module is used for issuing a control instruction which can not be performed to a user mobile phone APP to remind a user of changing a required temperature and humidity instruction, and the control module is used for controlling operations of the information acquisition module, the instruction issuing module, the neural network construction module, the wind cabinet output cold amount calculation module, the required cold amount calculation module, the actual output power calculation module, the power comparison module and the data feedback module.
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