CN114236276A - Method and system for remotely testing electric appliance - Google Patents

Method and system for remotely testing electric appliance Download PDF

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CN114236276A
CN114236276A CN202111488076.XA CN202111488076A CN114236276A CN 114236276 A CN114236276 A CN 114236276A CN 202111488076 A CN202111488076 A CN 202111488076A CN 114236276 A CN114236276 A CN 114236276A
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CN114236276B (en
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徐余德
华少忠
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Anhui Cheari Zhirui Technology Co ltd
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Anhui Cheari Zhirui Technology Co ltd
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Abstract

The embodiment of the specification provides a method and a system for remotely testing an electric appliance. The method for remotely testing the electric appliance is executed by a server and comprises the following steps: the method comprises the steps of obtaining remote test data of electric appliances to be tested distributed in a plurality of environments, wherein the remote test data comprise test energy consumption data and test environment data, and obtaining standard environment data. And combining the remote test data with the standard environment data to determine the test result of the electric appliance to be tested.

Description

Method and system for remotely testing electric appliance
Technical Field
The specification relates to the field of electrical equipment, in particular to a method and a system for remotely testing an electrical appliance.
Background
The performance of the electrical appliances varies under different environments. In order to obtain the performance conditions of the electric appliance in different user environments, such as a factory, a hotel, a home of a user and the like, the device can be directly placed in an actual use environment for testing to obtain remote test data besides a laboratory environment for simulation testing. The remote test data can reflect the operation coefficient of the electric appliance in the actual use state more truly.
Therefore, it is necessary to provide an electrical appliance testing method to obtain the testing data of the electrical appliance in the real use environment as one of the reference data of the electrical appliance performance data.
Disclosure of Invention
One embodiment of the present specification provides a method for remotely testing an electrical appliance. The method is performed by a server and comprises the following steps: the method comprises the steps of obtaining remote test data of electric appliances to be tested distributed in a plurality of environments, wherein the remote test data comprise test energy consumption data and test environment data; acquiring standard environment data; and combining the remote test data with the standard environment data to determine the test result of the electric appliance to be tested.
One embodiment of the present specification provides a system for remotely testing an electrical appliance. The system comprises an acquisition module and a determination module; the acquisition module is used for acquiring remote test data of the electric appliances to be tested distributed in a plurality of environments, and the remote test data comprises test energy consumption data and test environment data; the acquisition module is also used for acquiring standard environment data; the determining module is used for combining the remote testing data with the standard environment data to determine the testing result of the electric appliance to be tested.
One of the embodiments of the present specification provides an apparatus for remotely testing an electrical appliance, which includes a processor, and the processor is used for executing an electrical appliance remote testing method.
One of the embodiments of the present specification provides a computer-readable storage medium, where the storage medium stores computer instructions, and when the computer reads the computer instructions in the storage medium, the computer executes a remote testing method for an electrical appliance.
Drawings
The present description will be further explained by way of exemplary embodiments, which will be described in detail by way of the accompanying drawings. These embodiments are not intended to be limiting, and in these embodiments like numerals are used to indicate like structures, wherein:
FIG. 1 is a schematic diagram of an application scenario of an appliance remote test system according to some embodiments of the present description;
FIG. 2 is an exemplary flow chart of an appliance remote test method according to some embodiments shown herein;
FIG. 3 is an exemplary flow chart for determining test results according to some embodiments of the present application;
FIG. 4 is a flow diagram of a similarity determination method according to some embodiments of the present application;
FIG. 5 is a schematic diagram of an environmental difference model in accordance with some embodiments of the present description.
Detailed Description
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings used in the description of the embodiments will be briefly described below. It is obvious that the drawings in the following description are only examples or embodiments of the present description, and that for a person skilled in the art, the present description can also be applied to other similar scenarios on the basis of these drawings without inventive effort. Unless otherwise apparent from the context, or otherwise indicated, like reference numbers in the figures refer to the same structure or operation.
It should be understood that "system", "apparatus", "unit" and/or "module" as used herein is a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, other words may be substituted by other expressions if they accomplish the same purpose.
As used in this specification and the appended claims, the terms "a," "an," "the," and/or "the" are not intended to be inclusive in the singular, but rather are intended to be inclusive in the plural, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that steps and elements are included which are explicitly identified, that the steps and elements do not form an exclusive list, and that a method or apparatus may include other steps or elements.
Flow charts are used in this description to illustrate operations performed by a system according to embodiments of the present description. It should be understood that the preceding or following operations are not necessarily performed in the exact order in which they are performed. Rather, the various steps may be processed in reverse order or simultaneously. Meanwhile, other operations may be added to the processes, or a certain step or several steps of operations may be removed from the processes.
Fig. 1 is a schematic diagram of an application scenario of an appliance remote test system according to some embodiments of the present description. In some embodiments, appliance remote test system 100 may include server 110, network 120, device environment 130, storage device 140.
The appliance remote test system 100 may perform a test of an appliance and obtain test results in the device environment 130 by implementing the methods and/or processes disclosed herein. In some embodiments, the appliance remote test system 100 may be used to perform various performance tests on an appliance in a user environment where the appliance is actually used. For example, the performance test includes obtaining test environment data and test energy consumption data of the appliances in the device environment 130. For example, the electrical appliance remote test system 100 may be used for various test items such as refrigerator performance, air conditioner enthalpy difference, washing machine performance, compressor life and start-up, electric fan performance, range hood energy efficiency, and the like.
The server 110 may be used to obtain information and process the obtained information. In some embodiments, server 110 may be used to obtain test environment data for an appliance from device environment 130 and further generate an environment representation vector. In some embodiments, the server 110 may be used to retrieve standard environmental data of the appliance from the storage device 140 and further generate a standard environmental representation vector. In some embodiments, the server 110 may calculate a vector distance based on the test environment representation vector and the standard environment representation vector to determine a similarity of the standard environment data and the test environment data, and further determine a test result. In some embodiments, the server 110 may further perform parameter analysis based on the remote test data, pre-determine whether the electrical appliance has a fault, and generate and send a prompt for performing advanced processing on the fault of the electrical appliance if it is determined that the electrical appliance has the fault after the pre-determination.
In some embodiments, the server 110 may include a Central Processing Unit (CPU), a Digital Signal Processor (DSP), the like, and/or any combination thereof. In some embodiments, the server 110 may be local, remote, or implemented on a cloud platform. In some embodiments, the server 110, in whole or in part, may be integrated into the devices of the device environment 130.
The network 120 may provide a conduit for the exchange of information. In some embodiments, information may be exchanged between server 110, device environment 130, and/or storage device 140 via network 120. For example, server 110 may receive remote test data transmitted by remote test equipment in equipment environment 130 over network 120. As another example, server 110 may obtain standard environmental data stored in storage device 140 via network 120.
The device environment 130 refers to an actual usage environment after the electrical appliance leaves the factory. For example, the equipment environment 130 may include, but is not limited to, a factory building, a residential home, a commercial home, an outdoor, and the like. In some embodiments, the equipment environment 130 includes an appliance to be tested and a remote testing device installed on or around the appliance to be tested. In some embodiments, the test energy consumption data and the test environment data of the electrical appliance may be obtained by a remote test device.
In some embodiments, the remote test apparatus may include an instrument that tests and obtains data about the electrical device under test and the environment in which it is located. For example, the remote testing device is provided with a smart meter, a smart socket, and the like, for measuring the power consumption of the electric appliance. In some embodiments, the remote testing device may further comprise a sensor, and the related description of the sensor refers specifically to the related description of fig. 2.
In some embodiments, the remote testing apparatus may interact with the server 110 and the storage device 140 via the network 120. In some embodiments, when the remote testing device obtains an instruction that requires remote testing data, it starts to read the remote testing data on the appliances to be tested around it or where it is located. In some embodiments, the remote test data may be uploaded to the server 110 by the remote test apparatus or may be stored in the storage device 140.
Storage device 140 may be used to store data and/or instructions. For example, the storage device 140 may store standard environmental data as well as a trained environmental representation model. Storage device 140 may obtain data and/or instructions from, for example, server 110, device environment 130, and/or the like. In some embodiments, storage device 140 may store data and/or instructions used by server 110 to perform or use to perform the exemplary methods described in this specification.
In some embodiments, the appliance remote test system 100 may include an acquisition module and a determination module. In some embodiments, the obtaining module is configured to obtain remote test data of the electrical appliances to be tested distributed in a plurality of environments, where the remote test data includes test energy consumption data and test environment data. In some embodiments, the acquisition module is further configured to acquire standard environmental data.
In some embodiments, the determining module is configured to combine the remote test data with the standard environment data to determine a test result of the electrical appliance to be tested. In some embodiments, the determining module is further configured to determine similarity of the test environment data in the remote test data to the standard environment data; judging whether target standard environment data exist or not based on the similarity, wherein the similarity between the target standard environment data and the test environment data meets a preset condition; and responding to the test result, and using the test energy consumption data as the test result of the to-be-tested electric appliance.
It should be understood that the system and its modules shown in FIG. 1 may be implemented in a variety of ways. It should be noted that the above description of the remote appliance testing system 100 and the modules thereof is for convenience only and should not limit the present disclosure to the scope of the illustrated embodiments. It will be appreciated by those skilled in the art that, given the teachings of the present system, any combination of modules or sub-system configurations may be used to connect to other modules without departing from such teachings. In some embodiments, the obtaining module and the determining module disclosed in fig. 1 may be different modules in a system, or may be a module that implements the functions of two or more modules described above. For example, each module may share one memory module, and each module may have its own memory module. Such variations are within the scope of the present disclosure.
FIG. 2 is an exemplary flow chart of a method for remote testing of an appliance according to some embodiments of the present application. As shown in fig. 2, the process 200 includes the following steps.
Step 210, obtaining remote test data of the electric appliances to be tested distributed in a plurality of environments. This step is performed by the acquisition module.
The plurality of environments refer to equipment environments of the to-be-tested electric appliances when the remote testing is performed. Wherein the remote meaning may include: the appliance to be tested is in the device environment and the processor for receiving remote test data and performing data processing may be in a different space and/or time than the appliance to be tested. For example, a processor at a laboratory at a test center obtains remote test data for appliances under test located in the equipment environment over a network. As another example, the processor obtains historical remote test data for the appliance being tested via the memory.
The electric appliance to be tested refers to one or more electric devices which need to be subjected to performance or safety tests. In some embodiments, the types of appliances to be tested may include refrigerators, washing machines, air conditioners, and the like.
Remote test data refers to test data obtained in the device environment relating to the appliance to be tested. In some embodiments, the remote test data includes test energy consumption data and test environment data. The test environment data and the test energy consumption data may correspond. For example, in a specific environment, a set of test environment data and test energy consumption data of the to-be-tested appliance in the device environment can be obtained.
The test environment data refers to at least one data related to the device environment 130 in which the appliance to be tested is located. In some embodiments, the test environment data may include temperature, humidity, PH, air flow, radiation levels, and other relevant environmental data of the device environment 130 associated with the appliance to be tested. In some embodiments, the testing environment data includes usage data of the electrical appliance to be tested, such as door opening and closing data and usage duration data of a refrigerator, temperature and humidity and air volume data of an air conditioner, suction and noise of a range hood, and the like. The test energy consumption data refers to data of the electric appliance to be tested about energy consumed by the electric appliance. For example, the test energy consumption data may include power consumption.
In some embodiments, the remote test data is time dependent, for example, the remote test data may be data over a time period (i.e., data acquired over the time period) or may be a result of fusion of data over the time period. In other words, the test data includes test energy consumption data and test environment data that are time dependent. For example, if the processor acquires remote test data of a certain air conditioner for 4 hours acquired by the remote test device in the time period of 11:00-15:00, the remote test data of the air conditioner for 4 hours acquired by the remote test device (for example, a temperature sensor, a humidity sensor, an air volume sensor, a smart meter and the like) in the time period of 11:00-15:00 is acquired by the server through the network.
In some embodiments, the server sets the time period to a plurality of sub-time periods, and the remote test data may be for each sub-time period. For example, the time period of 11:00-15:00 is divided into one sub-time period of every half hour, and the sub-time periods of 11: 00-11: 30, 11: 30-12: 00, 12: 00-12: 30, 12: 30-13: 00, 13: 00-13: 30, 13: 30-14: 00, 14: 00-14: 30, 14: 30-15: 00 and the like respectively correspond to the remote test data of each sub-time period.
In some embodiments, the remote test data may be obtained in a variety of ways. For example, the remote test data may be entered manually, read automatically, or obtained by a remote testing device. As another example, the server 110 may be obtained from a storage device 140 internal or external to the system. In some embodiments, at least one remote testing device for acquiring remote test data is installed on or around the appliance to be tested.
In some embodiments, the remote testing device includes a sensor that can collect test environment data. In some embodiments, the sensors corresponding to the test environment data may include a temperature sensor, a humidity sensor, a Ph sensor, an air flow sensor, a radiation sensor, an electromagnetic sensor, and/or the like. In some embodiments, the type and installation position of the sensor can be set as appropriate according to the electric appliance to be tested and the test environment data to be acquired. For example, if the electrical appliance to be tested is an air conditioner, the test environment data may include data such as indoor and outdoor temperature, indoor and outdoor humidity, indoor air volume, and outdoor air velocity obtained from a temperature sensor, a humidity sensor, an air volume sensor, and an outdoor air velocity sensor provided on or around the electrical appliance to be tested. For another example, the electrical appliance to be tested is a refrigerator, an electromagnetic sensor may be installed on a refrigerator door, a temperature sensor may be installed on and/or around the refrigerator, and the test environment data may include data such as the number of times the refrigerator is turned on and off, the time of turning on and off, the indoor temperature, and the temperature of the refrigerator.
In some embodiments, the test energy consumption data may be obtained from a smart meter and/or a smart socket that records the power consumption of the appliance to be tested. In some embodiments, the smart meter may be mounted on a plug of the appliance to be tested. In some embodiments, the smart meter is configured to obtain power consumption of the electric appliance to be detected in a preset time period, where the power consumption may be determined according to data recorded by the smart meter in the preset time period. For example, the power consumption amount in a period of time between two time points can be obtained by subtracting the two readings from the reading of the smart meter between the two time points.
Step 220, standard environmental data is obtained. This step is performed by the acquisition module.
The standard environment data refers to environment data which needs to meet requirements when testing the to-be-tested electric appliance. In some embodiments, the standard environmental data may include temperature, humidity, PH, air flow, radiation level, etc. of the standard environment. In some embodiments, the standard environmental data is time dependent, and the standard environmental data may be an average of one or more time periods. For example, the standard environmental data may be an average temperature value and an average humidity value over 1 hour.
The standard environmental data may be preset. The preset standard can be determined according to the type, the use scene and the like of the electric appliance to be tested. For example, if the electrical appliance to be tested is a refrigerator, the standard environment data may be set as the number of times of opening and closing the door of the refrigerator, the time duration of opening and closing the door, and the like. In some embodiments, the standard environment data and the test environment data may be partially or wholly the same data type.
In some embodiments, multiple sets of standard environments may be preset for a certain type of appliance to be tested. The types and/or values (e.g., temperature and humidity and values thereof) of the environmental data included in the plurality of sets of standard environmental data are different. For example, the standard environmental data of the refrigerator to be tested comprises any variation combination of temperature, different door opening and closing times and duration. Illustratively, the average temperature of 3 groups of standard environmental data of the refrigerator to be tested within 1 hour is 16 ℃, the door opening times are 8 times, and the average door opening time is 9 s; the average temperature within 1 hour is 20 ℃, the door opening times are 3 times, and the average door opening time is 8 s; the average temperature within 1 hour is 23 ℃, the door opening times are 12, and the average door opening time is 9 s.
And step 230, combining the remote test data with the standard environment data to determine the test result of the electric appliance to be tested. This step 230 is performed by the determination module.
The test result refers to the performance result of the electric appliance to be tested. The test results may include energy consumption test results. It is understood that the test results represent test energy consumption data obtained from standard environmental data testing. In some embodiments, if the test environment data is close to the standard environment data, the test energy consumption data obtained by the test under the test environment data may be used as the test result.
In some embodiments, it may be determined whether to use the test energy consumption data in the remote test data as the test result of the electrical appliance to be tested based on the test environment data in the remote test data. For example, the similarity between the test environment data of the electrical appliance to be tested and the standard environment data is compared, if the test environment data is similar to a certain group of the multiple groups of standard environment data (e.g., the similarity is smaller than a similarity threshold), the test energy consumption data corresponding to the test environment data is used as a test result, and the test result indicates that the test energy consumption data corresponding to the test environment data belongs to data which can be referred to in the actual use environment of the standard environment data corresponding to the test environment data, that is, the test energy consumption data corresponding to the test environment data belongs to the group of standard environment data).
In some embodiments, the test result may further include performing parameter analysis based on the remote test data, predicting whether the electrical appliance to be tested has a fault, and if the electrical appliance to be tested has a fault, prompting to perform advanced processing on the fault of the electrical appliance to be tested. The parameter analysis comprises comprehensive analysis of test environment data and/or test energy consumption data in remote test data of the electric appliance to be tested. In some embodiments, the prejudgment may be to judge whether one or more items in the remote test data of the to-be-tested electrical appliance meet a preset threshold requirement based on a preset threshold. For example, when the remote test data of a certain to-be-tested electric appliance indicates that the test energy consumption data is far higher than the preset threshold value under the condition that the test environment coefficient is within the preset threshold value range, a prompt of abnormal energy consumption of the to-be-tested electric appliance is generated and sent to remind relevant workers of handling in advance. In some embodiments, the manner of pre-processing may include appliance reclamation, troubleshooting, servicing, and/or reassigning test environments.
In some embodiments, the processor may determine a standard environment representation vector based on the standard environment data, determine a test environment representation vector based on the test environment data, determine a similarity between the test environment data and the standard environment data based on a similarity between the standard environment representation vector and the test environment representation vector, and further determine whether to use test energy consumption data corresponding to the test environment data as a test result. See fig. 3 and its associated description for details regarding the manner of determination of the test results.
In some embodiments of the present specification, an electrical appliance to be tested is placed in a user environment where the electrical appliance is actually used for testing and acquiring data, similarity judgment is performed between test environment data in remote test data acquired in an actual use environment and a standard environment simulated in a laboratory, and test energy consumption data corresponding to the remote test data is attributed to the corresponding standard environment data, so that the electrical appliance can be directly tested and acquired in the actual use environment without paying a cost of simulating the actual environment in the laboratory, and wider and more reference data of the electrical appliance to be tested in the actual use environment can be obtained without being limited to the standard environment data, thereby not only saving the test cost, but also more accurately knowing the performance of the electrical appliance to be tested in the actual use environment. And whether the electric appliance to be tested has faults or not is judged in advance and prompt for processing in advance is carried out, so that the test condition of the electric appliance to be tested can be better monitored, the abnormal test condition is intervened in time, the probability of invalid test data is reduced, and the reliability of the test result is improved.
FIG. 3 is an exemplary flow chart for determining test results according to some embodiments of the present application. As shown in fig. 3, the process 300 includes the following steps. In some embodiments, the flow 300 may be performed by a determination module.
Step 310, determining similarity between the test environment data in the remote test data and the standard environment data.
The similarity may be expressed by a numerical value. For example, the similarity may be expressed in percentage, with the greater the percentage, the more similar between the two data.
In some embodiments, the similarity of the test environment data and the standard environment data may be determined based on a mathematical method or algorithm. For example, if the test environment data and the standard environment data are (x) respectively1，x2，x3) And (y)1，y2，y3) Two data sets, which may be based on (x)1，x2，x3) And (y)1，y2，y3) X in (2)1And y1、x2And y2、x3And y3Determining corresponding difference values, normalizing each difference value, mapping the difference values to 0-1 to respectively obtain z1、z2、z3Then normalizing the difference to obtain z1、z2、z3And calculating an average value z, wherein the average value z is the similarity.
In some embodiments, the test environment data and the standard environment data are characterized to generate a test environment initial vector and a standard environment initial vector respectively, then the test environment representation vector and the standard environment representation vector are generated respectively through the environment representation model, then the vector distance is calculated, and the similarity between the test environment data and the standard environment data is obtained based on the vector distance. Further details of the method of generating the environment representation vector and the determination of the similarity may be found elsewhere in the application, e.g. in fig. 4.
And step 320, judging whether the target standard environment data exists or not based on the similarity.
The target standard environmental data may be standard environmental data when the similarity satisfies a preset condition.
As previously mentioned, the standard environmental data may be a plurality of sets. The target standard environmental data may be a certain set of target standard environmental data satisfying a preset condition among the plurality of sets of standard environmental data. It is understood that the target standard environment data may be one or more sets. For example, three sets of standard environmental data for an air conditioner include (t)1，h1，w1…)、(t2，h2，w2…)、(t3，h3，w3…), where t1、t2、t3Mean temperature data, h, over a certain period of time, respectively, for a laboratory simulation1、h2、h3Mean humidity data, w, over a certain period of time, respectively for a laboratory simulation1、w2、w3Respectively, average wind data over a certain time period of the laboratory simulation. And the time periods corresponding to the data in the same group of standard environment data are the same. If the test environment data T is (T)t，ht，wt) And (t)2，h2，w2…) satisfies a predetermined condition, then (t)2，h2，w2…) is the target standard environment data corresponding to the test environment data T. If the test environment data D is (t)d，hd，wd) And (t)1，h1，w1…) and (t)3，h3，w3…) satisfies a predetermined condition, then (t)1，h1，w1…) and (t)3，h3，w3…) is the target standard environment data corresponding to the test environment D data.
In some embodiments, the target standard environment data may be determined according to whether the similarity is greater than a preset threshold by setting a preset threshold under a preset condition, and one or more standard environment data with the similarity greater than the preset threshold may be used as the target standard environment data. For example, the preset threshold may be N%, and if the similarity is greater than N%, the target standard environmental data may be determined.
In step 330, if it is determined that the target standard environment data exists, the test energy consumption data is used as a test result of the electrical appliance to be tested, specifically please refer to the related description of step 230.
For example, for the same refrigerator, the standard environment data measured in the standard environment is that the average environment temperature within 10min is 10.01 degrees celsius, the number of times the refrigerator door is opened is 6 times, and the average time length of opening the door is 10s, if the average environment temperature within 10min in the test environment data measured in the test environment is also close to 10.01 degrees celsius, the number of times the refrigerator door is opened is 6 times, and the average time length of opening the door is close to 10s, if the similarity of the two environment data is higher than the preset threshold N%, the standard environment data is the target standard environment data. And attributing the test energy consumption data (namely the power consumption) of the to-be-tested electric appliance within the 10min corresponding to the test environment data to the target standard environment data to serve as the test result of the to-be-tested electric appliance in the target standard environment.
Step 340, if the set of standard environment data is not the target standard environment data after the determination based on the test environment data in the remote test data and the set of standard environment data, comparing the set of test environment data with the other sets of standard environment data until the similarity comparison with all the standard environment data is completed, if any set of standard environment data similar to the set of test environment data is still not found at this time, the target standard environment data does not exist, and discarding the set of remote test data.
FIG. 4 is a flow chart of a method of similarity determination shown in accordance with some embodiments of the present application. As shown in fig. 4, the process 400 includes the following steps. In some embodiments, the flow 400 may be performed by a determination module.
In some embodiments, generating the similarity between the standard environmental data and the test environmental data is accomplished by: firstly, standard environment data and test environment data are respectively obtained, and the standard environment data and the test environment data are respectively characterized to obtain a standard environment initial characteristic vector and a test environment initial characteristic vector. And inputting the initial characteristic vector of the standard environment into a trained environment representation model to obtain a standard environment representation vector, and inputting the initial characteristic vector of the test environment into the same environment representation model to obtain a test environment representation vector. And finally, calculating the vector distance between the standard environment representation vector and the test environment representation vector, and finally determining the similarity.
In some embodiments, the standard environment representation vector corresponding to the standard environment data may be obtained in advance and stored in the storage device 140, and may be directly invoked when calculating the vector distance.
In some embodiments, the trained environment representation model acts as a machine learning model. For a detailed description of the training process of the environment representation model, see the description elsewhere in this application, e.g. fig. 5.
The similarity determination method based on the environment representation model will be described in the following steps 410, 420, and 430.
And step 410, characterizing the test environment data and the standard environment data to obtain a standard environment feature vector and a test environment feature vector.
In some embodiments, the test environment data and the standard environment data may be characterized based on rules, resulting in a standard environment initial vector and a test environment initial vector. The test environment data and the standard environment data are characterized in the same way. The present specification takes test environment data as an example for explanation.
The elements in the feature vector of the test environment data are related to the environment data type. Test environmentAn element in the feature vector of the data represents a value of a certain type of environmental data (e.g., temperature, etc.). If the environmental data is related to the number of times, the value of the element is the total number of times corresponding to the test time period. If the environmental data is determined by the actual test result, the value of the element is the average of all values of the test time period. For example, the feature vector A of the test environment data of the refrigerator is (a)1，a2，a3，a4)，a1Represents the indoor temperature, then a1Is the average indoor temperature over the test period; a is2Represents the indoor humidity, then a2Is the indoor humidity within the test period; a is3Representing the number of door openings, then a3The total number of door opening times in the testing time period; a is4Representing the door opening time of the refrigerator, a4Representing the average time each time the refrigerator is opened. The test time period refers to a time period for testing the electric appliance to be tested, and the test time period can be preset in advance.
And step 420, respectively inputting the obtained standard environment characteristic vector and the test environment characteristic vector into the environment representation model to respectively obtain a standard environment representation vector and a test environment representation vector.
In some embodiments, the test time period may be further divided into several sub-time periods, and based on the manner described above, the corresponding test environment initial vector is determined based on each sub-time period. Correspondingly, a plurality of test environment expression vectors can be obtained, and then the plurality of test environment expression vectors are subjected to operations such as averaging or weighted averaging, so that the final test environment expression vector is obtained.
As a simple example only, in the range of 10:00 to 10:50, the temperature sensor and the humidity sensor collect temperature values once every 5min, and cumulatively collect test environment data for 50min, wherein the collected temperature data include 5 ℃, 7 ℃, 8 ℃, 13 ℃, 15 ℃, 16 ℃, 18 ℃, 3 ℃ and 5 ℃, and the collected humidity data include 65% RH, 63% RH, 64% RH, 58% RH, 61% RH, 57% RH, 56% RH, 54% RH, 59% RH and 55% RH. The test time 50min is divided into 5 sub-periods of 10min each. The test environment initial vector is an average value of the temperature in each 10min interval, and the test environment initial vectors corresponding to the temperature and the humidity are determined to be (6, 64), (8, 61), (14, 59), (17, 55) and (4, 57) respectively on the basis of each sub-period. And then respectively inputting the environment initial vectors of 5 time periods into the environment representation model to obtain environment representation vectors corresponding to the 5 time periods, and averaging the 5 environment representation vectors to obtain a test environment representation vector.
The similarity of the vectors can measure the difference between the data. In some embodiments, the similarity between vectors may be measured by calculating the distance between vectors.
And 430, generating similarity by calculating the vector distance between the standard environment representation vector and the test environment representation vector. In some embodiments, the vector distance may include a manhattan distance, a euclidean distance, a chebyshev distance, a cosine distance, a mahalanobis distance, or the like. The numerical value can be substituted for mathematical calculation according to formulas corresponding to different distance types.
In some embodiments, the vector distance between the standard environment representation vector and the test environment representation vector is inversely related to the similarity of the standard environment data and the test environment data, i.e. the greater the distance, the less the similarity. When the similarity exceeds a certain preset threshold, the standard environment data can be judged to be the target standard environment data.
In some embodiments, the type of environment representation model includes a Neural Network (NN), such as MATLAB or other model that can generate feature vectors.
In some embodiments, the environment representation model processes the standard environment initial vector and the test environment initial vector to obtain the standard environment initial vector and the test environment representation vector, so that a function of extracting the representative information of the environment representation features from the original environment information is realized, and collection, conversion and subsequent processing of data in the actual environment are facilitated. In some embodiments of the present description, the test environment data and the standard environment data can be accurately compared through a machine learning model and vector calculation, so that the test result can be conveniently and efficiently obtained.
FIG. 5 is a schematic diagram of an environmental difference model according to some embodiments of the present application. As shown in FIG. 5, diagram 500 includes the steps associated with the environment difference model (steps 510-550).
In some embodiments, the resulting environment representation model may be trained on environment difference models. The environment difference model includes two environment representation layers (i.e., environment representation layer 1 and environment representation layer 2), and a difference determination layer. In some embodiments, the model type of the environment representation layer and the difference determination layer may be NN or the like.
In some embodiments, the two environment representation layers share parameters. After the environment difference model is trained, the parameters of the environment representation layer in the environment difference model can be transferred to the environment representation model, and the trained environment representation model is obtained. It is understood that the training of the environment representation model is based on the training of the environment difference model.
In some embodiments, the environmental difference model may be trained based on multiple sets of training samples. In some embodiments, the sample data may include multiple sets of training samples, each set of training samples including two sets of historical test data. The two sets of historical test data in each set of training samples refer to two sets of test environment data acquired by the same appliance to be tested in the same equipment environment 130, for example, the two sets of historical test data may be the test environment data acquired by the same refrigerator at 1-3 points of the first day and 1-3 points of the second day in the same room. The label of each group of training samples is a difference value between historical testing energy consumption data corresponding to two groups of historical testing data. The following describes an example of a training process of a set of training samples on an environment difference model.
Step 510, a set of training samples is obtained, wherein the set of training samples comprises historical test data 1 and historical test data 2.
Details regarding obtaining historical test data are described elsewhere in this application, such as the description of obtaining test environment data in step 210.
And step 520, respectively characterizing the acquired historical test data 1 and the historical test data 2 to obtain a historical test initial vector 1 and a historical test initial vector 2.
For details on the characterization, see the description of the rest of the application, e.g. fig. 4.
Step 530, inputting the characterized historical test initial vector 1 into an environment representation layer 1 of an environment difference model to obtain a historical test representation vector 1, and simultaneously inputting the characterized historical test initial vector 2 into an environment representation layer 2 of the same environment difference model to obtain a historical test representation vector 2.
And step 540, calculating the vector distance between the historical test representation vector 1 and the historical test representation vector 2 through the vector distance. For details on vector calculation, see the description elsewhere in the specification of the application, e.g. fig. 4.
Step 550, inputting the vector distance between the historical test expression vector 1 and the historical test expression vector 2 into the difference determination layer of the environment difference model to obtain a difference value.
The difference value may indicate a difference between the historical test data 1 and the historical test data 2. In some embodiments, the difference value may be a difference between historical test energy consumption data corresponding to the historical test data 1 and the historical test data 2 obtained by the difference determination layer, respectively. The difference may be represented by a ratio, difference, etc. between the two historical test energy consumption data.
In some embodiments of the present description, the difference determination layer may convert the vector distance into an actually measurable difference value, and even if the conversion is not necessarily a linear conversion, the information that the vector reflects the similarity between two environmental representations or environments may be converted into data of the actually measurable difference value through the neural network.
And finally, completing the training of the environment difference model when the loss function of the environment difference model meets the preset condition, and transferring the parameters of the environment representation layer to the environment representation model to obtain the trained environment representation model. The preset condition may be that the loss function converges, the number of iterations reaches a threshold, and the like.
In some embodiments of the present description, the variable can be effectively controlled by using the historical test data of the same device, so that the training effect is prevented from being affected by the difference of the device itself, and in addition, if the environmental representation model is trained independently, the label is not convenient to obtain, so that the effective training of the environmental representation model is realized by using the environmental representation model obtained by training the environmental difference model, and the execution effect of the environmental representation model is favorably improved.
Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be regarded as illustrative only and not as limiting the present specification. Various modifications, improvements and adaptations to the present description may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present specification and thus fall within the spirit and scope of the exemplary embodiments of the present specification.
Also, the description uses specific words to describe embodiments of the description. Reference to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the specification. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the specification may be combined as appropriate.
Additionally, the order in which the elements and sequences of the process are recited in the specification, the use of alphanumeric characters, or other designations, is not intended to limit the order in which the processes and methods of the specification occur, unless otherwise specified in the claims. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.
Similarly, it should be noted that in the preceding description of embodiments of the present specification, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to imply that more features than are expressly recited in a claim. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.
Numerals describing the number of components, attributes, etc. are used in some embodiments, it being understood that such numerals used in the description of the embodiments are modified in some instances by the use of the modifier "about", "approximately" or "substantially". Unless otherwise indicated, "about", "approximately" or "substantially" indicates that the number allows a variation of ± 20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximations that may vary depending upon the desired properties of the individual embodiments. In some embodiments, the numerical parameter should take into account the specified significant digits and employ a general digit preserving approach. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the range are approximations, in the specific examples, such numerical values are set forth as precisely as possible within the scope of the application.
For each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., cited in this specification, the entire contents of each are hereby incorporated by reference into this specification. Except where the application history document does not conform to or conflict with the contents of the present specification, it is to be understood that the application history document, as used herein in the present specification or appended claims, is intended to define the broadest scope of the present specification (whether presently or later in the specification) rather than the broadest scope of the present specification. It is to be understood that the descriptions, definitions and/or uses of terms in the accompanying materials of this specification shall control if they are inconsistent or contrary to the descriptions and/or uses of terms in this specification.
Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present disclosure. Other variations are also possible within the scope of the present description. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the specification can be considered consistent with the teachings of the specification. Accordingly, the embodiments of the present description are not limited to only those embodiments explicitly described and depicted herein.

Claims (10)

1. A method of remote testing of an appliance, the method performed by a server, comprising:
the method comprises the steps of obtaining remote test data of electric appliances to be tested distributed in a plurality of environments, wherein the remote test data comprise test energy consumption data and test environment data;
acquiring standard environment data;
and combining the remote test data with the standard environment data to determine the test result of the electric appliance to be tested.
2. The method of remotely testing an appliance according to claim 1, the test environment data including usage data of the appliance to be tested;
the use data is obtained through a sensor installed on the electric appliance to be tested, and the sensor is connected with the server through a network.
3. The method for remotely testing electric appliances according to claim 1, wherein the test energy consumption data is obtained by a smart meter recording the power consumption of the electric appliances to be tested, and the smart meter is connected with the server through a network.
4. The method for remotely testing an appliance according to claim 1, wherein the combining the remote test data with the standard environment data to determine the test result of the appliance to be tested comprises:
determining similarity of the test environment data in the remote test data and the standard environment data;
judging whether target standard environment data exist or not based on the similarity, wherein the similarity between the target standard environment data and the test environment data meets a preset condition;
and responding to the test result, and using the test energy consumption data as the test result of the to-be-tested electric appliance.
5. A system for remotely testing an electric appliance comprises an acquisition module and a determination module;
the acquisition module is used for acquiring remote test data of the electric appliances to be tested distributed in a plurality of environments, and the remote test data comprises test energy consumption data and test environment data;
the acquisition module is also used for acquiring standard environment data;
the determining module is used for combining the remote testing data with the standard environment data to determine the testing result of the electric appliance to be tested.
6. The system for remote testing of appliances of claim 5, the test environment data including usage data of appliances to be tested;
the use data is obtained through a sensor installed on the electric appliance to be tested, and the sensor is connected with the server through a network.
7. The system for remotely testing electric appliances according to claim 5, wherein the test energy consumption data is obtained by a smart meter recording the power consumption of the electric appliances to be tested, and the smart meter is connected with the server network.
8. The system for remote testing of appliances of claim 5, the determination module further for:
determining similarity of the test environment data in the remote test data and the standard environment data;
judging whether target standard environment data exist or not based on the similarity, wherein the similarity between the target standard environment data and the test environment data meets a preset condition;
and responding to the test result, and using the test energy consumption data as the test result of the to-be-tested electric appliance.
9. An appliance remote testing device comprising a processor for executing the appliance remote testing method of any one of claims 1 to 4.
10. A computer-readable storage medium storing computer instructions, wherein when the computer instructions in the storage medium are read by a computer, the computer executes the remote testing method for the electrical appliance according to any one of claims 1 to 4.
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