CN116048161B - Intelligent temperature regulation control method for heating wearable equipment - Google Patents
Intelligent temperature regulation control method for heating wearable equipment Download PDFInfo
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- CN116048161B CN116048161B CN202310186021.6A CN202310186021A CN116048161B CN 116048161 B CN116048161 B CN 116048161B CN 202310186021 A CN202310186021 A CN 202310186021A CN 116048161 B CN116048161 B CN 116048161B
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- 230000009466 transformation Effects 0.000 claims description 3
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D23/00—Control of temperature
- G05D23/19—Control of temperature characterised by the use of electric means
- G05D23/20—Control of temperature characterised by the use of electric means with sensing elements having variation of electric or magnetic properties with change of temperature
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Abstract
The invention discloses an intelligent temperature regulation control method for heating wearable equipment, which belongs to the technical field of temperature regulation of the wearable equipment and comprises the following steps: step one: establishing a cloud model library; the cloud model library is used for storing various different types of temperature analysis models; step two: when a user activates a heating wearable device client, guiding the user to register information to obtain user information; step three: the obtained user information is sent to a cloud model library for matching, and a corresponding temperature analysis model is obtained; step four: acquiring temperature characteristic data, inputting the acquired temperature characteristic data into a temperature analysis model for analysis, and obtaining corresponding regulation and control temperature; step five: controlling the heating wearable equipment to perform corresponding temperature adjustment according to the obtained regulation temperature; by the temperature control method provided by the invention, intelligent temperature adjustment of the heating wearable equipment can be realized, and the problem that a user needs to manually adjust the temperature frequently under various conditions is solved.
Description
Technical Field
The invention belongs to the technical field of temperature regulation of wearable equipment, and particularly relates to an intelligent temperature regulation control method for heating the wearable equipment.
Background
In cold weather, a large amount of clothes are required to be worn for keeping warm, which causes inconvenient movement and has a load feeling, and with rapid development of science and technology, the heating wearing equipment is invented and fixed on the clothes in a patch mode for keeping warm, so that the warm effect is greatly improved, and the heating wearing equipment is particularly important in severe cold weather, such as a skiing sport environment; however, the current temperature regulation and control mode of the heating wearable equipment is not intelligent enough, and a user is often required to manually regulate parameters such as the temperature, time and gear of heating, when the user moves, takes a rest and the like, the heat generated by the user can be changed, and the required control temperature can be changed, so that the user is required to perform frequent manual regulation; therefore, in order to solve the problem of intelligent temperature regulation of the heating wearable equipment, the invention provides an intelligent temperature regulation control method of the heating wearable equipment.
Disclosure of Invention
In order to solve the problems of the scheme, the invention provides an intelligent temperature regulation control method for heating wearable equipment.
The aim of the invention can be achieved by the following technical scheme:
an intelligent temperature regulation control method for a heating wearable device, comprising:
step one: establishing a cloud model library; the cloud model library is used for storing various different types of temperature analysis models;
further, the cloud model library is arranged at the cloud.
Further, the cloud model library comprises a first storage node, a second storage node and a third storage node, the temperature analysis model is stored in the first storage node, the second storage node is used for storing model learning data, and the third storage node is used for storing user classification detail data and an information registry. The user classification details data includes data such as user classifications, associated temperature analysis models, and classification ranges.
Further, the temperature analysis model in the first storage node learns data by the storage model in the second storage node to perform self-learning.
Step two: when a user activates a heating wearable device client, guiding the user to register information to obtain user information;
further, the method for guiding the user to register the information comprises the following steps:
when a user activates a client of the heating wearable device, the client generates a corresponding registration instruction, the generated registration instruction is sent to a cloud model library, when the cloud model library receives the registration instruction, an information registry stored by a third storage node is sent to the corresponding client, and the user fills in personal information according to the information registry received by the client, and obtains user information after filling in is completed.
Step three: the obtained user information is sent to a cloud model library for matching, and a corresponding temperature analysis model is obtained;
further, the method for sending the obtained user information to the cloud model library for matching comprises the following steps:
the method comprises the steps of carrying out feature transformation on obtained user information to obtain user features, identifying a classification range corresponding to each temperature analysis model, calculating a matching value between the user features and the classification range, analyzing the user information to obtain corresponding user correction values and correction coefficients, marking the obtained matching values, the obtained user correction values and the obtained correction coefficients as PZ, XU and beta respectively, calculating corresponding model values according to a formula QU=beta× (b1×PZ+b2×XU), wherein b1 and b2 are proportionality coefficients, the value range is 0< b1 less than or equal to 1,0< b2 less than or equal to 1, selecting the temperature analysis model with the highest model value for output, and sending the model values to a client.
Further, the method for calculating the matching value between the user characteristic and the classification range comprises the following steps:
identifying feature items in a user classification range, setting weight coefficients corresponding to the feature items, calculating deviation values between each feature item data in the user features and the corresponding feature item range in the user classification range, analyzing the obtained deviation values and the corresponding feature items to obtain corresponding single item matching values, and marking the feature items as i, wherein i=1, 2, … …, n and n are positive integers; marking the obtained weight coefficient as qi, marking the single matching value as DPi, and according to a formulaA corresponding matching value is calculated.
Step four: acquiring temperature characteristic data, inputting the acquired temperature characteristic data into a temperature analysis model for analysis, and obtaining corresponding regulation and control temperature;
step five: controlling the heating wearable equipment to perform corresponding temperature adjustment according to the obtained regulation temperature; and collecting learning data generated by a user, and sending the obtained learning data to a cloud model library.
Compared with the prior art, the invention has the beneficial effects that:
by the temperature control method, intelligent temperature adjustment of the heating wearable equipment can be realized, the problem that a user needs to adjust the temperature frequently and manually under various conditions is solved, and the wearing experience of the user is improved; by arranging the cloud model library in the cloud, the temperature analysis models of the clients of the heating wearable devices are convenient to transmit and update, learning data generated by the clients are collected, the temperature analysis models in the cloud model library are relearned according to the collected learning data, the analysis precision of the corresponding type of temperature analysis models is continuously improved, and continuous update is realized; the rapid application of the registered user is realized by matching a detailed temperature analysis model according to the acquired user information, and the accurate temperature regulation and control of the registered user in the use process is ensured.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings can be obtained according to these drawings without inventive effort to a person skilled in the art.
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1, an intelligent temperature adjustment control method for a heating wearable device includes:
step one: establishing a cloud model library, wherein the cloud model library is used for storing various different types of temperature analysis models;
the temperature analysis model is used for intelligently analyzing the temperature required to be adjusted by the heating wearable equipment; the temperature analysis model is built by corresponding staff at the beginning, specifically, a learnable model built based on a CNN network or a DNN network is trained by setting a corresponding training set in a manual mode, and the temperature analysis model after successful training is uploaded to a cloud model library.
Wherein, manual maintenance, namely increase along with time and data volume, can produce more accurate user classification, just need carry out the interpolation of temperature analysis model this moment, realize user classification's subsidence and refinement, this work needs to maintain through workman personnel.
Thus, the setting of the temperature analysis models is performed with the explicit corresponding user classification, one for each temperature analysis model; the confirmation of user classification is mainly set based on the existing big data analysis and manual setting modes, constitution information of each crowd is analyzed through a large amount of data, requirements of different constitution on temperature are analyzed, corresponding user classification such as gender, age, body fat rate, quantity of motion and the like can be used as measurement standards, and the user classification is specifically or actually carried out.
The cloud model library is arranged at the cloud, so that the temperature analysis models of the heating wearable equipment clients can be conveniently transmitted, updated and the like; a plurality of storage nodes are arranged in the cloud model library, and different data are stored in different storage nodes, such as: the first storage node for storing the temperature analysis model, the second storage node for storing the learning data, the third storage node for storing the user classification detail data and the information registry, and the like can be specifically adjusted according to management requirements, such as adding and deleting the storage nodes, replacing the storage content of each storage node, and the like.
Step two: when a user activates a heating wearable device client, guiding the user to register information to obtain user information;
when a user purchases the heating wearable equipment for initial use, the user needs to register information, the user enters a registration function, the client automatically generates a registration instruction and sends the registration instruction to the cloud model library, when the cloud model library receives the registration instruction, the stored information registry is sent to the client, the user receives the information registry according to the client to fill in personal information, and after the user information is obtained after the user is filled in.
The method for obtaining the latest information registry by the client may have various methods, not just the above description, for example, a piece of information registry may be pre-stored in the client, and when the client needs to register, whether the pre-stored information registry is consistent with that stored in the cloud model library or not is checked, and when the pre-stored information registry is inconsistent with that stored in the cloud model library, the corresponding update is performed, or the pre-stored information registry is directly used, and various modes such as checking are not performed.
Step three: the obtained user information is sent to a cloud model library for matching, and a corresponding temperature analysis model is obtained;
the main analysis, matching and other processes are set to be carried out in the cloud, so that the occupation of resources such as memory, calculation power and the like of the client is reduced as much as possible, and meanwhile, various algorithms can be updated conveniently.
When the cloud model library receives user information, performing feature transformation on the obtained user information to obtain user features, wherein the acquired user information is partially irrelevant information, non-numerical information and other data in the user information, and the user information needs to be subjected to corresponding optimization, such as removing information such as names and contact ways, and digitizing non-numerical data such as gender and body data; setting the data types included in the user characteristics, and further identifying and converting corresponding data in the user data to obtain the user characteristics; identifying a classification range corresponding to each user classification, if the classification range has non-numerical conditions, carrying out corresponding conversion through a corresponding assignment method, calculating a matching value PZ between user characteristics and each classification range, establishing a user analysis model based on a CNN network or a DNN network, training by establishing a corresponding training set in a manual mode, analyzing user information through the user analysis model after successful training, and obtaining a corresponding user correction value and correction coefficient, namely correcting deviation possibly caused by direct user information matching through the user analysis model, wherein the neural network is the prior art in the field, so that the specific establishment and training process is not described in detail; and marking the obtained user correction value and correction coefficient as XU and beta respectively, calculating corresponding model values according to a formula QU=beta× (b1×PZ+b2×XU), wherein b1 and b2 are both proportional coefficients, the value range is 0< b1 less than or equal to 1,0< b2 less than or equal to 1, and selecting a temperature analysis model with the highest model value for output, and sending the model values to a client.
The method for calculating the matching value between the user characteristics and the classification range comprises the following steps:
identifying characteristic items in the user classification range, which are the same as the data items of the user characteristics, and setting the correspondence of the characteristic itemsThe weight coefficient is set by an expert group according to the data corresponding to each feature item, the deviation value between each feature item data in the user feature and the corresponding feature item range in the user classification range is calculated, the weight coefficient belongs to the feature item range, the weight coefficient comprises a boundary, the deviation value is zero, the difference value between the feature item data and the corresponding range boundary exceeds the feature item range, namely the deviation value is higher than the upper boundary, the value corresponding to the upper boundary is subtracted, and the weight coefficient is positive; subtracting the value corresponding to the lower boundary from the value below the lower boundary to obtain a negative value; establishing a corresponding deviation conversion model based on a CNN network or a DNN network, establishing a corresponding training set by a manual mode for training, analyzing a deviation value and a corresponding characteristic item through the deviation analysis model after successful training to obtain a corresponding single item matching value, and marking the characteristic item as i, wherein i=1, 2, … …, n and n are positive integers; marking the obtained weight coefficient as qi, marking the single matching value as DPi, and according to a formulaA corresponding matching value is calculated.
Step four: acquiring temperature characteristic data, inputting the acquired temperature characteristic data into a temperature analysis model for analysis, and obtaining corresponding regulation and control temperature;
the temperature characteristic data are set based on specific functions of the heating wearing equipment, for example, the heating wearing equipment is used for adapting to adjustment under different conditions, various application scenes such as outdoor, indoor, sports, skiing, skating and the like are set, a user manually selects according to actual conditions, and various contents included in the temperature characteristic data set by the heating wearing equipment under different conditions are different because functions of the temperature characteristic data are possibly different, so that when a temperature analysis model is built, the temperature characteristic data are synchronously set, and the temperature characteristic data are acquired according to preset acquisition contents.
Step five: and controlling the heating wearable equipment to perform corresponding temperature adjustment according to the obtained regulation temperature, collecting learning data generated by a user, and sending the obtained learning data to a cloud model library.
And generating corresponding learning data according to the re-adjustment record of the user after intelligent temperature adjustment, wherein the learning data is used for learning the temperature analysis model classified by the user, and the specific learning data generation method is generated by the prior art.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas which are obtained by acquiring a large amount of data and performing software simulation to obtain the closest actual situation, and preset parameters and preset thresholds in the formulas are set by a person skilled in the art according to the actual situation or are obtained by simulating a large amount of data.
The above embodiments are only for illustrating the technical method of the present invention and not for limiting the same, and it should be understood by those skilled in the art that the technical method of the present invention may be modified or substituted without departing from the spirit and scope of the technical method of the present invention.
Claims (1)
1. An intelligent temperature regulation control method for heating wearable equipment is characterized by comprising the following steps:
step one: establishing a cloud model library; the cloud model library is used for storing various different types of temperature analysis models;
step two: when a user activates a heating wearable device client, guiding the user to register information to obtain user information;
step three: the obtained user information is sent to a cloud model library for matching, and a corresponding temperature analysis model is obtained;
step four: acquiring temperature characteristic data, inputting the acquired temperature characteristic data into a temperature analysis model for analysis, and obtaining corresponding regulation and control temperature;
step five: controlling the heating wearable equipment to perform corresponding temperature adjustment according to the obtained regulation temperature; collecting learning data generated by a user, and sending the obtained learning data to a cloud model library;
the cloud model library is arranged at the cloud;
the cloud model library comprises a first storage node, a second storage node and a third storage node, the temperature analysis model is stored in the first storage node, the second storage node is used for storing model learning data, and the third storage node is used for storing user classification detail data and an information registry;
the temperature analysis model in the first storage node performs self-learning through model learning data stored in the second storage node;
the method for guiding the user to register the information comprises the following steps:
when a user activates a client of the heating wearable device, the client generates a corresponding registration instruction, the generated registration instruction is sent to a cloud model library, when the cloud model library receives the registration instruction, an information registry stored by a third storage node is sent to the corresponding client, and the user fills in personal information according to the information registry received by the client, and obtains user information after filling in is completed;
the user classification detail data comprises user classification, an associated temperature analysis model and a classification range;
the method for transmitting the obtained user information to the cloud model library for matching comprises the following steps:
carrying out feature transformation on the obtained user information to obtain user features, identifying a classification range corresponding to each temperature analysis model, calculating a matching value between the user features and the classification range, analyzing the user information to obtain corresponding user correction values and correction coefficients, marking the obtained matching values, the user correction values and the correction coefficients as PZ, XU and beta respectively, calculating corresponding model values according to a formula QU=beta× (b1×PZ+b2×XU), wherein b1 and b2 are proportionality coefficients, the value range is 0< b1 less than or equal to 1,0< b2 less than or equal to 1, selecting the temperature analysis model with the highest model value for output, and sending the model values to a client;
the method for calculating the matching value between the user characteristics and the classification range comprises the following steps:
identifying feature items in the classification range, setting weight coefficients corresponding to the feature items, and calculating deviation values between data of the feature items in the user features and the corresponding feature item ranges in the classification rangeAnalyzing the obtained deviation value and the corresponding characteristic item to obtain a corresponding single item matching value, and marking the characteristic item as i, wherein i=1, 2, … …, n and n are positive integers; marking the obtained weight coefficient as qi, marking the single matching value as DPi, and according to a formulaA corresponding matching value is calculated.
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| CN114091336A (en) * | 2021-11-22 | 2022-02-25 | 中国第一汽车股份有限公司 | Air conditioner adjusting method and device, vehicle and storage medium |
| CN216286353U (en) * | 2021-12-10 | 2022-04-12 | 江苏芳香季纺织科技有限公司 | Intelligent heating wearable device cloud control system |
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- 2023-03-01 CN CN202310186021.6A patent/CN116048161B/en active Active
Patent Citations (6)
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| CN108151241A (en) * | 2017-11-10 | 2018-06-12 | 珠海格力电器股份有限公司 | Air conditioning control method and device |
| CN109527671A (en) * | 2018-09-18 | 2019-03-29 | 北京小米移动软件有限公司 | The method, apparatus and storage medium of temperature setting |
| CN112471636A (en) * | 2020-12-04 | 2021-03-12 | 江西智诚健康科技有限公司 | Intelligent heating vest based on portal access |
| CN113903337A (en) * | 2021-10-13 | 2022-01-07 | 海尔(深圳)研发有限责任公司 | Method and device for controlling voice air conditioner, cloud server and storage medium |
| CN114091336A (en) * | 2021-11-22 | 2022-02-25 | 中国第一汽车股份有限公司 | Air conditioner adjusting method and device, vehicle and storage medium |
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