CN113959148A - Method and system for detecting cold air leakage of intelligent refrigeration equipment - Google Patents
Method and system for detecting cold air leakage of intelligent refrigeration equipment Download PDFInfo
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- CN113959148A CN113959148A CN202111429827.0A CN202111429827A CN113959148A CN 113959148 A CN113959148 A CN 113959148A CN 202111429827 A CN202111429827 A CN 202111429827A CN 113959148 A CN113959148 A CN 113959148A
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F25—REFRIGERATION OR COOLING; COMBINED HEATING AND REFRIGERATION SYSTEMS; HEAT PUMP SYSTEMS; MANUFACTURE OR STORAGE OF ICE; LIQUEFACTION SOLIDIFICATION OF GASES
- F25D—REFRIGERATORS; COLD ROOMS; ICE-BOXES; COOLING OR FREEZING APPARATUS NOT OTHERWISE PROVIDED FOR
- F25D11/00—Self-contained movable devices, e.g. domestic refrigerators
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F25—REFRIGERATION OR COOLING; COMBINED HEATING AND REFRIGERATION SYSTEMS; HEAT PUMP SYSTEMS; MANUFACTURE OR STORAGE OF ICE; LIQUEFACTION SOLIDIFICATION OF GASES
- F25D—REFRIGERATORS; COLD ROOMS; ICE-BOXES; COOLING OR FREEZING APPARATUS NOT OTHERWISE PROVIDED FOR
- F25D29/00—Arrangement or mounting of control or safety devices
- F25D29/003—Arrangement or mounting of control or safety devices for movable devices
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F25—REFRIGERATION OR COOLING; COMBINED HEATING AND REFRIGERATION SYSTEMS; HEAT PUMP SYSTEMS; MANUFACTURE OR STORAGE OF ICE; LIQUEFACTION SOLIDIFICATION OF GASES
- F25D—REFRIGERATORS; COLD ROOMS; ICE-BOXES; COOLING OR FREEZING APPARATUS NOT OTHERWISE PROVIDED FOR
- F25D29/00—Arrangement or mounting of control or safety devices
- F25D29/005—Mounting of control devices
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F25—REFRIGERATION OR COOLING; COMBINED HEATING AND REFRIGERATION SYSTEMS; HEAT PUMP SYSTEMS; MANUFACTURE OR STORAGE OF ICE; LIQUEFACTION SOLIDIFICATION OF GASES
- F25D—REFRIGERATORS; COLD ROOMS; ICE-BOXES; COOLING OR FREEZING APPARATUS NOT OTHERWISE PROVIDED FOR
- F25D29/00—Arrangement or mounting of control or safety devices
- F25D29/008—Alarm devices
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F25—REFRIGERATION OR COOLING; COMBINED HEATING AND REFRIGERATION SYSTEMS; HEAT PUMP SYSTEMS; MANUFACTURE OR STORAGE OF ICE; LIQUEFACTION SOLIDIFICATION OF GASES
- F25D—REFRIGERATORS; COLD ROOMS; ICE-BOXES; COOLING OR FREEZING APPARATUS NOT OTHERWISE PROVIDED FOR
- F25D2600/00—Control issues
- F25D2600/06—Controlling according to a predetermined profile
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- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Devices That Are Associated With Refrigeration Equipment (AREA)
- Air Conditioning Control Device (AREA)
Abstract
The invention provides a method and a system for detecting cold air leakage of an intelligent refrigeration device, wherein the method comprises the following steps: when the door switch sensor detects that the intelligent refrigeration equipment opens or closes the door, generating and sending trigger information; after receiving the trigger information, the central processing unit acquires humidity information, temperature change information within a preset time length and humidity change information within the preset time length in the intelligent refrigeration equipment; the central processing unit generates a first detection result of cold air leakage of the intelligent refrigeration equipment according to the humidity information and a preset humidity threshold value; classifying based on a machine learning classifier to obtain a second detection result; determining a final detection result according to the first detection result and the second detection result; and the central processing unit generates a detection result of cold air leakage of the intelligent refrigeration equipment according to the humidity information and a preset humidity threshold value. The intelligent refrigeration equipment cold air leakage detection device can accurately and efficiently detect whether the cold air of the intelligent refrigeration equipment leaks or not.
Description
Technical Field
The disclosure relates to the field of intelligent refrigeration equipment detection, in particular to a method and a system for detecting cold air leakage of intelligent refrigeration equipment.
Background
Along with the increasing popularization rate of intelligent refrigerator products and the increasing demand on the convenience of use of the products, the method solves the problems that a user intelligent refrigerator cannot timely inform and treat the user intelligent refrigerator after cold air in the refrigerator leaks due to various reasons, and the refrigerator freezes due to the leakage of the cold air, stored goods are decayed, electricity is wasted, and the like.
Therefore, how to accurately and efficiently detect whether the cold air of the intelligent refrigeration equipment leaks is a technical problem to be solved urgently.
Disclosure of Invention
An object of the disclosed embodiment is to provide a method and a system for detecting leakage of cold air of an intelligent refrigeration device, which can accurately and efficiently detect whether the cold air of the intelligent refrigeration device leaks.
In a first aspect, the present invention provides a method for detecting cold air leakage of an intelligent refrigeration device, comprising:
when the door switch sensor detects that the intelligent refrigeration equipment opens or closes the door, generating and sending trigger information;
after receiving the trigger information, the central processing unit acquires humidity information, temperature change information within a preset time length and humidity change information within a preset time length in the intelligent refrigeration equipment;
the central processing unit generates a first detection result of cold air leakage of the intelligent refrigeration equipment according to the humidity information and a preset humidity threshold value;
classifying based on a machine learning classifier according to the humidity information, the temperature change information within the preset time length and the humidity change information within the preset time length to obtain a second detection result;
and determining a final detection result according to the first detection result and the second detection result.
Further, the preset humidity threshold comprises a preset humidity upper limit threshold;
the step that the central processing unit generates a detection result of the cold air leakage of the intelligent refrigeration equipment according to the humidity information and a preset humidity threshold value comprises the following steps:
after receiving the trigger information, the central processing unit determines that the humidity information continuously rises within a preset first time period, and when the humidity information is greater than a preset humidity upper limit threshold value, a detection result used for representing leakage of the cold air of the intelligent refrigeration equipment is generated.
Further, the preset humidity threshold comprises a preset humidity target threshold;
the step that the central processing unit generates a detection result of the cold air leakage of the intelligent refrigeration equipment according to the humidity information and a preset humidity threshold value comprises the following steps:
after the triggering information is received, the central processing unit generates a detection result for representing the leakage of the cold air of the intelligent refrigeration equipment when the humidity information is determined not to reach the preset humidity target threshold value in the preset second time period.
Further, the method for detecting cold air leakage of intelligent refrigeration equipment further comprises the following steps:
the alarm module is receiving the testing result perhaps behind the testing result, the generation is used for the sign intelligence refrigeration plant air conditioning takes place the alarm information of revealing.
Further, the method for detecting cold air leakage of the intelligent refrigeration equipment further comprises the following steps:
and the central processing unit reports the detection result or the detection result to a cloud server.
Further, the step of classifying based on a machine learning classifier according to the humidity information, the temperature change information within a preset time length, and the humidity change information within the preset time length to obtain a second detection result includes:
inputting the humidity information, the temperature change information within a preset time length and the humidity change information within the preset time length into a support vector machine for classification to obtain a second detection result;
wherein the kernel function of the support vector machine is any one of the following kernel functions:
(a) linear kernel function: k (x, x)i)＝(x,xi)
(b) Polynomial kernel function: k (x, x)i)＝[(x,xi)+1]dWherein d is an order
(c) Gauss radial basis kernel function:
(d) sigmoid kernel function: k (x, x)i)＝tanh[s(x,xi)+c]
The step of determining the final detection result according to the first detection result and the second detection result comprises:
when the first detection result and the second detection result are consistent, the final detection result is the first detection result or the second detection result;
and when the first detection result is inconsistent with the second detection result, the final detection result is a detection result used for representing leakage of the cold air of the intelligent refrigeration equipment in the first detection result and the second detection result.
In a second aspect, the present invention provides an intelligent refrigeration equipment cold air leakage detection system, comprising:
the door opening and closing sensor is used for detecting the door opening or closing action of the intelligent refrigeration equipment and generating and sending trigger information when detecting the door opening or closing;
the central processing unit is used for acquiring humidity information in the intelligent refrigeration equipment after receiving the trigger information, generating temperature information, temperature change information within a preset time length and humidity change information within a preset time length for cold air leakage of the intelligent refrigeration equipment according to the humidity information and a preset humidity threshold value, and generating a first detection result for the cold air leakage of the intelligent refrigeration equipment according to the humidity information and the preset humidity threshold value; classifying based on a machine learning classifier according to the humidity information, the temperature change information within the preset time length and the humidity change information within the preset time length to obtain a second detection result; and determining a final detection result according to the first detection result and the second detection result.
Further, the preset humidity threshold includes a preset humidity upper limit threshold and a preset humidity target threshold;
the central processing unit is further used for receiving after the trigger information, confirming that the humidity information continuously rises in a preset first time period, and when the humidity information is larger than a preset humidity upper limit threshold value, generating and representing the detection result of leakage of the cold air of the intelligent refrigeration equipment, or/and, confirming that the humidity information does not reach a preset humidity target threshold value in a preset second time period, generating and representing the detection result of leakage of the cold air of the intelligent refrigeration equipment.
Further, the intelligent cold air leakage detection system for the refrigeration equipment further comprises:
and the alarm module is used for receiving the detection result or after the detection result, generating alarm information for representing leakage of the cold air of the intelligent refrigeration equipment.
Further, the intelligent cold air leakage detection system for the refrigeration equipment further comprises: the cloud server and a humidity sensor are arranged in the intelligent refrigeration equipment;
the central processing unit is further used for inputting the humidity information, the temperature change information within a preset time length and the humidity change information within the preset time length into a support vector machine for classification to obtain a second detection result; when the first detection result and the second detection result are consistent, the final detection result is the first detection result or the second detection result; when the first detection result and the second detection result are inconsistent, the final detection result is a detection result used for representing leakage of cold air of the intelligent refrigeration equipment in the first detection result and the second detection result, wherein a kernel function of the support vector machine is any one of the following kernel functions:
(a) linear kernel function: k (x, x)i)＝(x,xi)
(b) Polynomial kernel function: k (x, x)i)＝[(x,xi)+1]dWherein d is an order
(c) Gauss radial basis kernel function:
(d) sigmoid kernel function: k (x, x)i)＝tanh[s(x,xi)+c]。
According to the cold air leakage detection method and system for the intelligent refrigeration equipment, the current humidity value in the intelligent refrigeration equipment is collected in real time, the central processing unit carries out cold air leakage detection according to the humidity threshold value and the current humidity value to generate a first detection result of the cold air leakage of the intelligent refrigeration equipment, and a second detection result is obtained by classifying the cold air leakage detection method and system based on a machine learning classifier according to the humidity information, the temperature change information within a preset time length and the humidity change information within the preset time length; confirm final testing result according to first testing result and second testing result, can accurately detect intelligent refrigeration plant air conditioning effectively and whether take place to reveal.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a flowchart of a method for detecting cold air leakage of an intelligent refrigeration device according to a first embodiment of the present disclosure.
Fig. 2 is a flowchart of a cold air leakage detection method for an intelligent refrigeration device according to a second embodiment of the present disclosure.
Fig. 3 is a schematic block diagram of an intelligent refrigeration device cold air leakage detection system according to a third embodiment of the present disclosure.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
It should be noted that, in the case of no conflict, the features in the following embodiments and examples may be combined with each other; moreover, all other embodiments that can be derived by one of ordinary skill in the art from the embodiments disclosed herein without making any creative effort fall within the scope of the present disclosure.
It is noted that various aspects of the embodiments are described below within the scope of the appended claims. It should be apparent that the aspects described herein may be embodied in a wide variety of forms and that any specific structure and/or function described herein is merely illustrative. Based on the disclosure, one skilled in the art should appreciate that one aspect described herein may be implemented independently of any other aspects and that two or more of these aspects may be combined in various ways. For example, an apparatus may be implemented and/or a method practiced using any number of the aspects set forth herein. Additionally, such an apparatus may be implemented and/or such a method may be practiced using other structure and/or functionality in addition to one or more of the aspects set forth herein.
Fig. 1 is a flowchart of a method for detecting cold air leakage of an intelligent refrigeration device according to a first embodiment of the present disclosure. As shown in fig. 1:
and S101, generating and sending trigger information when the door opening and closing sensor detects that the intelligent refrigeration equipment is opened or closed.
Step S102, after receiving the trigger information, the central processing unit acquires humidity information, temperature change information within a preset time length and humidity change information within the preset time length in the intelligent refrigeration equipment;
step S103, the central processing unit generates a first detection result of cold air leakage of the intelligent refrigeration equipment according to the humidity information and a preset humidity threshold value;
step S104, classifying the humidity information, the temperature change information within a preset time length and the humidity change information within the preset time length based on a machine learning classifier to obtain a second detection result;
and step S105, determining a final detection result according to the first detection result and the second detection result.
Specifically, the step of classifying based on a machine learning classifier according to the humidity information, the temperature change information within a preset time length, and the humidity change information within the preset time length to obtain a second detection result includes:
inputting the humidity information, the temperature change information within a preset time length and the humidity change information within the preset time length into a support vector machine for classification to obtain a second detection result;
wherein the kernel function of the support vector machine is any one of the following kernel functions:
(a) linear kernel function: k (x, x)i)＝(x,xi)
(b) Polynomial kernel function: k (x, x)i)＝[(x,xi)+1]dWherein d is an order
(c) Gauss radial basis kernel function:
(d) sigmoid kernel function: k (x, x)i)＝tanh[s(x,xi)+c]
The step of determining the final detection result according to the first detection result and the second detection result comprises:
when the first detection result and the second detection result are consistent, the final detection result is the first detection result or the second detection result;
and when the first detection result is inconsistent with the second detection result, the final detection result is a detection result used for representing leakage of the cold air of the intelligent refrigeration equipment in the first detection result and the second detection result.
In the embodiment, the current humidity value in the intelligent refrigeration equipment is collected in real time, the central processing unit carries out cold air leakage detection according to a humidity threshold value and the current humidity value to generate a first detection result of the cold air leakage of the intelligent refrigeration equipment, and a machine learning classifier is used for classifying according to the humidity information, the temperature change information within a preset time length and the humidity change information within the preset time length to obtain a second detection result; whether the cold air of the intelligent refrigeration equipment is leaked or not can be accurately and efficiently detected according to the first detection result and the second detection result.
Fig. 2 is a flowchart of a cold air leakage detection method for an intelligent refrigeration device according to a second embodiment of the present disclosure. Fig. 2 is a preferred implementation of the embodiment shown in fig. 1, and as shown in fig. 2, specifically includes:
step A01, detecting that the equipment has an opening and closing operation;
a02, when the opening and closing operations of the equipment are detected, the Soc central processing unit acquires the humidity value of the temperature and humidity sensor;
step A03, the Soc central processing unit continuously monitors humidity through a humidity sensor and compares the humidity with the obtained humidity value;
step A04, when the humidity continuously rises and exceeds the alarm humidity upper limit value HH within a certain time;
step A05, or a certain time class does not reach the alarm humidity target value HT;
and step A06, the alarm module sends out an alarm and sends the alarm to the cloud end through the Soc central processing unit to remind the user.
In the embodiment, whether the door is opened or closed is detected through a door opening and closing sensor, and a humidity sensor acquires the current humidity in the refrigerator; soc central processing unit sets up humidity threshold value and target humidity value to compare with the humidity value that acquires, and send alarm information for the high in the clouds after judging that the refrigerator appears the air conditioning and leaks, the warning module is judged to appear the air conditioning and is leaked the back and is reported to the police, thereby accurately detects intelligent refrigeration plant air conditioning high-efficiently and whether take place to leak.
Fig. 3 is a schematic block diagram of an intelligent refrigeration device cold air leakage detection system according to a third embodiment of the present disclosure. The method embodiments shown in fig. 1 and 2 can be used to explain the present embodiment. As shown in fig. 3: intelligent refrigeration equipment cold air leakage detection system includes: a door sensor 101; soc central processing unit 102: a humidity sensor; 103: and an alarm module 104; the door sensor module 101 is configured to detect whether there is a door opening or closing motion, and then send a door closing signal. And the Soc central processing unit 102 is used for setting a humidity threshold value and a target humidity value, comparing the humidity threshold value with the obtained humidity value, and sending alarm information to the cloud after judging that the refrigerator has cold air leakage. And the humidity sensor module 103 is used for acquiring the humidity value in the refrigerator in real time. And the alarm module 104 is used for alarming after judging that the cold air leakage occurs.
As shown in fig. 3, the door sensor module 101 specifically includes a door opening and closing sensor 1011 for detecting door opening or door closing. The Soc central processing unit 102 includes a humidity threshold module 1021 and a humidity target value module 1022; a humidity threshold module 1021 for comparing with a humidity value obtained from a humidity sensor; the humidity target value module 1022 is used to compare the humidity value obtained from the humidity sensor. The humidity sensor module 103 includes a humidity sensor 1031 for acquiring a humidity value inside the refrigerator in real time. The alarm module 104 includes an alarm 1041 for alarming after the occurrence of cold air leakage. And the cloud (server) 105 is used for forwarding the alarm information sent by the Soc central processing unit to the user.
Specifically, the intelligent refrigeration device may be an intelligent refrigerator product or the like, and is not particularly limited herein.
This detection system is revealed to intelligence refrigeration plant air conditioning is through each unit collaborative work such as door sensor, humidity transducer, Soc central processing unit, alarm module: the intelligent refrigerator that contains a sensor module detects there is the action of opening the door, closing the door, and Soc central processing unit passes through humidity transducer after a period and acquires current humidity in the refrigerator to continuously monitor humidity, appear continuously rising in the refrigerator when inside humidity appears for a period, or when not reaching the target humidity in the certain time, the refrigerator sends the warning and reminds the user through the high in the clouds.
In addition, the central processing unit can be further configured to obtain humidity information, temperature change information within a preset time length, and humidity change information within the preset time length in the intelligent refrigeration equipment after receiving the trigger information, and generate a first detection result of cold air leakage of the intelligent refrigeration equipment according to the humidity information and a preset humidity threshold; classifying based on a machine learning classifier according to the humidity information, the temperature change information within the preset time length and the humidity change information within the preset time length to obtain a second detection result; and determining a final detection result according to the first detection result and the second detection result.
Specifically, the central processing unit is further configured to input the information according to the humidity information, the temperature change information within a preset time length, and the humidity change information within the preset time length into a support vector machine for classification, so as to obtain a second detection result; when the first detection result and the second detection result are consistent, the final detection result is the first detection result or the second detection result; when the first detection result and the second detection result are inconsistent, the final detection result is a detection result used for representing leakage of cold air of the intelligent refrigeration equipment in the first detection result and the second detection result, wherein a kernel function of the support vector machine is any one of the following kernel functions:
(a) linear kernel function: k (x, x)i)＝(x,xi)
(b) Polynomial kernel function: k (x, x)i)＝[(x,xi)+1]dWherein d is an order
(c) Gauss radial basis kernel function:
(d) sigmoid kernel function: k (x, x)i)＝tanh[s(x,xi)+c]。
In this case, the program code itself read from the storage medium can realize the functions of any of the above-described embodiments, and thus the program code and the storage medium storing the program code constitute a part of the present invention.
Examples of the storage medium for supplying the program code include a floppy disk, a hard disk, a magneto-optical disk, an optical disk (e.g., CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD + RW), a magnetic tape, a nonvolatile memory card, and a ROM. Alternatively, the program code may be downloaded from a server computer via a communications network.
Further, it should be clear that the functions of any one of the above-described embodiments may be implemented not only by executing the program code read out by the computer, but also by causing an operating system or the like operating on the computer to perform a part or all of the actual operations based on instructions of the program code.
Further, it is to be understood that the program code read out from the storage medium is written to a memory provided in an expansion board inserted into the computer or to a memory provided in an expansion unit connected to the computer, and then causes a CPU or the like mounted on the expansion board or the expansion unit to perform part or all of the actual operations based on instructions of the program code, thereby realizing the functions of any of the above-described embodiments.
It should be noted that not all steps and modules in the above flows and system structure diagrams are necessary, and some steps or modules may be omitted according to actual needs. The execution order of the steps is not fixed and can be adjusted as required. The system structure described in the above embodiments may be a physical structure or a logical structure, that is, some modules may be implemented by the same physical entity, or some modules may be implemented by a plurality of physical entities, or some components in a plurality of independent devices may be implemented together.
In the above embodiments, the hardware unit may be implemented mechanically or electrically. For example, a hardware element may comprise permanently dedicated circuitry or logic (such as a dedicated processor, FPGA or ASIC) to perform the corresponding operations. The hardware elements may also comprise programmable logic or circuitry, such as a general purpose processor or other programmable processor, that may be temporarily configured by software to perform the corresponding operations. The specific implementation (mechanical, or dedicated permanent, or temporarily set) may be determined based on cost and time considerations.
While the invention has been shown and described in detail in the drawings and in the preferred embodiments, it is not intended to limit the invention to the embodiments disclosed, and it will be apparent to those skilled in the art that various combinations of the code auditing means in the various embodiments described above may be used to obtain further embodiments of the invention, which are also within the scope of the invention.
Claims (10)
1. A cold air leakage detection method for an intelligent refrigeration device is characterized by comprising the following steps:
when the door switch sensor detects that the intelligent refrigeration equipment opens or closes the door, generating and sending trigger information;
after receiving the trigger information, the central processing unit acquires humidity information, temperature change information within a preset time length and humidity change information within a preset time length in the intelligent refrigeration equipment;
the central processing unit generates a first detection result of cold air leakage of the intelligent refrigeration equipment according to the humidity information and a preset humidity threshold value;
classifying based on a machine learning classifier according to the humidity information, the temperature change information within the preset time length and the humidity change information within the preset time length to obtain a second detection result;
and determining a final detection result according to the first detection result and the second detection result.
2. A cold air leakage detection method for an intelligent cold storage device as claimed in claim 1, wherein said preset humidity threshold comprises a preset upper humidity threshold;
the step that the central processing unit generates a detection result of the cold air leakage of the intelligent refrigeration equipment according to the humidity information and a preset humidity threshold value comprises the following steps:
after receiving the trigger information, the central processing unit determines that the humidity information continuously rises within a preset first time period, and when the humidity information is greater than a preset humidity upper limit threshold value, a detection result used for representing leakage of the cold air of the intelligent refrigeration equipment is generated.
3. A cold air leakage detecting method for an intelligent refrigerating apparatus as recited in claim 2 wherein said preset humidity threshold comprises a preset humidity target threshold;
the step that the central processing unit generates a detection result of the cold air leakage of the intelligent refrigeration equipment according to the humidity information and a preset humidity threshold value comprises the following steps:
after the triggering information is received, the central processing unit generates a detection result for representing the leakage of the cold air of the intelligent refrigeration equipment when the humidity information is determined not to reach the preset humidity target threshold value in the preset second time period.
4. A cold air leakage detecting method for an intelligent refrigerating apparatus as recited in claim 3 further comprising:
the alarm module is receiving the testing result perhaps behind the testing result, the generation is used for the sign intelligence refrigeration plant air conditioning takes place the alarm information of revealing.
5. A cold air leakage detection method for an intelligent cold storage device according to claim 4, further comprising:
and the central processing unit reports the detection result or the detection result to a cloud server.
6. An intelligent cold air leakage detection method for a cold storage device as claimed in any one of claims 1-5, wherein the step of classifying according to the humidity information, the temperature variation information within a preset time period and the humidity variation information within a preset time period based on a machine learning classifier to obtain a second detection result comprises:
inputting the humidity information, the temperature change information within a preset time length and the humidity change information within the preset time length into a support vector machine for classification to obtain a second detection result;
wherein the kernel function of the support vector machine is any one of the following kernel functions:
(a) linear kernel function: k (x, x)i)＝(x,xi)
(b) Polynomial kernel function: k (x, x)i)＝[(x,xi)+1]dWherein d is an order
(c) Gauss radial basis kernel function:
(d) sigmoid kernel function: k (x, x)i)＝tanh[s(x,xi)+c]
The step of determining the final detection result according to the first detection result and the second detection result comprises:
when the first detection result and the second detection result are consistent, the final detection result is the first detection result or the second detection result;
and when the first detection result is inconsistent with the second detection result, the final detection result is a detection result used for representing leakage of the cold air of the intelligent refrigeration equipment in the first detection result and the second detection result.
7. The utility model provides an intelligence refrigeration plant air conditioning leaks detecting system which characterized in that includes:
the door opening and closing sensor is used for detecting the door opening or closing action of the intelligent refrigeration equipment and generating and sending trigger information when detecting the door opening or closing;
the central processing unit is used for acquiring humidity information, temperature change information within a preset time length and humidity change information within the preset time length in the intelligent refrigeration equipment after receiving the trigger information, and generating a first detection result of cold air leakage of the intelligent refrigeration equipment according to the humidity information and a preset humidity threshold value; classifying based on a machine learning classifier according to the humidity information, the temperature change information within the preset time length and the humidity change information within the preset time length to obtain a second detection result; and determining a final detection result according to the first detection result and the second detection result.
8. An intelligent cold air leakage detection system for a cold air storage device as claimed in claim 7, wherein said preset humidity threshold comprises a preset upper humidity threshold and a preset target humidity threshold;
the central processing unit is further used for receiving after the trigger information, confirming that the humidity information continuously rises in a preset first time period, and when the humidity information is larger than a preset humidity upper limit threshold value, generating and representing the detection result of leakage of the cold air of the intelligent refrigeration equipment, or/and, confirming that the humidity information does not reach a preset humidity target threshold value in a preset second time period, generating and representing the detection result of leakage of the cold air of the intelligent refrigeration equipment.
9. A smart refrigeration chiller cold gas leak detection system as recited in claim 8 further comprising:
and the alarm module is used for receiving the detection result or after the detection result, generating alarm information for representing leakage of the cold air of the intelligent refrigeration equipment.
10. A smart refrigeration chiller cold gas leak detection system as recited in claim 9 further comprising: the cloud server and a humidity sensor are arranged in the intelligent refrigeration equipment;
the central processing unit is further used for inputting the humidity information, the temperature change information within a preset time length and the humidity change information within the preset time length into a support vector machine for classification to obtain a second detection result; when the first detection result and the second detection result are consistent, the final detection result is the first detection result or the second detection result; when the first detection result and the second detection result are inconsistent, the final detection result is a detection result used for representing leakage of cold air of the intelligent refrigeration equipment in the first detection result and the second detection result, wherein a kernel function of the support vector machine is any one of the following kernel functions:
(a) linear kernel function: k (x, x)i)＝(x,xi)
(b) Polynomial kernel function: k (x, x)i)＝[(x,xi)+1]dWherein d is an order
(c) Gauss radial basis kernel function:
(d) sigmoid kernel function: k (x, x)i)＝tanh[s(x,xi)+c]。
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| CN202111429827.0A CN113959148B (en) | 2021-11-29 | 2021-11-29 | Method and system for detecting cold air leakage of intelligent refrigeration equipment |
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