CN115674988A - Air conditioner adaptive adjustment method, system, electronic device, storage medium and vehicle - Google Patents
Air conditioner adaptive adjustment method, system, electronic device, storage medium and vehicle Download PDFInfo
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Abstract
The invention provides an air conditioner self-adaptive adjusting method, an air conditioner self-adaptive adjusting system, electronic equipment, a storage medium and a vehicle, and relates to the field of automobiles. The automatic air conditioner has the advantages of wide range of using data, more adjusting parameters, more accurate analysis and capability of meeting the driving habits of drivers, and better use experience of the automatic air conditioner.
Description
Technical Field
The invention relates to the field of automobiles, in particular to an air conditioner self-adaptive adjusting method, an air conditioner self-adaptive adjusting system, electronic equipment, a storage medium and an automobile.
Background
For the driving comfort of the automobile, the most influential factor is whether the temperature in the automobile is proper, so that the automatic adjusting function of the air conditioner is generated by taking measures to bring more comfortable driving experience to users. In the current air conditioner automatic regulation technology, the air speed and the temperature of the air conditioner are set by taking temperature data provided by a temperature sensor in a cabin as a judgment basis.
However, the applicant finds that according to research and big data analysis results, most drivers can perform manual adjustment after the air conditioner is automatically adjusted due to poor automatic adjustment effect of the air conditioner, and the use experience of the automatic air conditioner is reduced.
Disclosure of Invention
The embodiment of the invention provides an air conditioner self-adaptive adjusting method, an air conditioner self-adaptive adjusting system, electronic equipment, a storage medium and a vehicle, and aims to solve the problem of poor automatic adjusting effect of an air conditioner.
In order to solve the above problem, in a first aspect, an embodiment of the present invention discloses an adaptive air conditioner adjustment method, which is applied to a vehicle-mounted air conditioner, and includes:
acquiring first environment data, first user data and first vehicle sensor data;
according to the first environment data, the first user data and the first vehicle sensor data, an algorithm model is built to obtain a result value and a characteristic equation;
performing statistical calculation according to the result value and the characteristic equation to obtain a prediction model;
calculating and analyzing according to the prediction model and second environmental data, second vehicle sensor data and second user data acquired by the whole vehicle, and predicting a set behavior result of the client air conditioner;
and setting the vehicle-mounted air conditioner parameters according to the predicted client air conditioner setting behavior result.
In a second aspect, an embodiment of the present invention further includes an adaptive air conditioner adjustment system, including:
the third-party server is used for acquiring the first environmental data and the second environmental data, wherein the TSP platform is in data connection with the third-party server to acquire weather information, altitude and geographical position;
the vehicle end is used for acquiring user behavior and vehicle state data, monitoring information such as user fatigue degree, facial expressions, facial orientation and the like through a DMS (distributed management system), and uploading the information to a cloud TSP (short message service) platform through a gateway and a T-BOX (T-BOX) after personal information is removed;
the cloud end is used for carrying out data analysis, model training and model deployment, and obtaining analyzed data from the TSP platform;
and the application terminal comprises a vehicle-mounted air conditioner and an AC controller, and is an application terminal of the air conditioner adaptive adjustment method, and the vehicle-mounted air conditioner obtains the adaptive adjustment function after the cloud terminal issues the characteristic equation to the AC controller through the FOTA function.
In a third aspect, an embodiment of the present invention further includes an electronic device, including: at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the air conditioner adaptive adjustment method of any one of claims 1 to 5.
In a fourth aspect, an embodiment of the present invention further includes a computer-readable storage medium storing a computer program, where the computer program is executed by a processor to implement the adaptive air conditioner adjusting method according to any one of claims 1 to 5.
In a fifth aspect, the embodiment of the invention further includes a vehicle, which includes an on-board air conditioner and the air conditioner adaptive adjustment system according to claim 7.
In addition, according to the air conditioner self-adaptive adjustment method provided by the embodiment of the invention, the algorithm model building comprises the steps of carrying out data preprocessing according to the first environment data, the first user data and the first vehicle sensor data to obtain effective data; the data preprocessing comprises deleting invalid signals and abnormal signals in the data to obtain valid data. According to the effective data, carrying out a fuzzy algorithm and neural network training and building a model to obtain a result value and a characteristic equation; the building model comprises analyzing to obtain characteristic signals of relevant parameters according to the first environment data, the first user data and the first vehicle sensor data; and (4) discriminating strong correlation quantity according to the characteristic signals of the correlation parameters, and using a fuzzy algorithm and neural network training to obtain a result value and a characteristic equation.
In addition, according to the air conditioner adaptive adjustment method provided by the embodiment of the invention, the second environmental data, the second user data and the second vehicle sensor data include data acquisition by combining functions of kafka, producer and consumer according to a real-time network condition, and when the vehicle networking data flow is less, the data acquisition is carried out to obtain the second environmental data, the second user data and the second vehicle sensor data. And data acquisition is carried out by combining functions of kafka, producer and consumer, when the data flow of the vehicle networking is more, the data needing to be uploaded is stored in a data pool, and after the network is unobstructed, the data is uploaded to obtain the second environment data, the second user data and the second whole vehicle sensor data.
In addition, according to the air conditioner adaptive adjustment method provided by the embodiment of the present invention, the second user data and the second vehicle sensor data include user manual adjustment information, vehicle interior temperature data, vehicle interior humidity data, vehicle interior air speed data, interior and exterior circulation opening state data, vehicle speed, vehicle running time, user face orientation, fatigue state, accelerator pedal position, average fuel consumption, engine output torque, transmission input torque, gear signal, coolant temperature, air conditioner switch, window state, tire pressure, and remaining fuel amount.
In addition, according to the air conditioner adaptive adjustment method provided by the embodiment of the invention, the vehicle-mounted air conditioner parameters comprise wind speed, temperature, wind direction, internal and external circulation, air filtration and drying.
In addition, according to the air conditioner adaptive adjustment method provided by the embodiment of the invention, the neural network training comprises respectively establishing models through a decision tree algorithm, an XGboost algorithm and a random forest model algorithm according to the first environment data, the first user data and the first vehicle sensor data to obtain three algorithm models; and performing small sample verification according to the three algorithm models to obtain an optimal model, and deploying the optimal model to a carrier with computing power, such as a cloud end or a vehicle end.
The embodiment of the invention has the following advantages:
in the embodiment of the invention, the internal state of the vehicle is obtained, the environmental data and the user data are also obtained for model building, the internal state of the vehicle is considered, the scene state and the user state outside the vehicle are also considered, the data consideration is more comprehensive, the coverage scene is wider, the driving habit of a driver can be more accurately analyzed and satisfied, and the use experience of the automatic air conditioner is better.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of an adaptive air conditioner adjustment method according to an embodiment of the present invention;
FIG. 2 is a flow chart of algorithm model building in the air conditioner adaptive adjustment method provided by the embodiment of the invention;
FIG. 3 is a flow chart of neural network training in an adaptive air conditioning control method according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a system of an adaptive air conditioner adjustment method according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram provided in an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
The method, system, electronic device, storage medium, and automobile for semantic recognition provided by the embodiments of the present application are described in detail below with reference to the accompanying drawings.
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application without making any creative effort belong to the protection scope of the present application.
One embodiment of the invention relates to an air conditioner adaptive adjustment method, which is applied to a vehicle-mounted air conditioner, and the flow of the method is shown in figure 1, and comprises the following steps:
step 101: the method comprises the steps of obtaining first environment data, first user data and first vehicle sensor data.
In this embodiment, the first environmental data and the second environmental data include weather, altitude, geographical location, and the like provided by the third-party server. The second vehicle sensor data comprises user manual adjustment information, vehicle temperature data, vehicle humidity data, vehicle wind speed data, internal and external circulation opening state data, vehicle speed, vehicle running time, user face orientation, fatigue state, accelerator pedal position, average fuel consumption, engine output torque, transmission input torque, gear signal, coolant temperature, air conditioner switch, vehicle window state, tire pressure, residual oil quantity and the like. And the second user data is obtained by manually adjusting the parameter data after the air conditioner is automatically adjusted by the individual vehicle owner. Of course, the above is only a specific example, and the first environment data and the second environment data may also include other data in an actual using process, which is not described in detail here.
The second environment data, the second user data and the second vehicle sensor data comprise data acquisition by combining functions of kafka, producer and consumer, and are uploaded when the data flow of the vehicle networking is less to obtain the second environment data, the second user data and the second vehicle sensor data;
and (4) data acquisition is carried out by combining functions of kafka, producer and consumer, when the data flow of the Internet of vehicles is more, the data required to be uploaded are stored in a data pool, and after a data channel is unobstructed, the data are uploaded to obtain second environment data, second user data and second whole vehicle sensor data.
Because the data acquisition cycle is less than or equal to 1s, if the vehicle utilization peak period is met, the data flow of the internet of vehicles is too much, the data push failure, the data loss and other conditions are easily caused, and regarding data uploading, in principle, the shorter the data uploading time is, the better the data uploading time is, the data uploading time interval can be verified and confirmed according to the real vehicle, and the server side can be configured. If meet with the vehicle peak period, the car networking data flow is too much, then leads to data push failure easily, the data condition such as lose, has consequently divided a data pool in this application for deposit data, upload when waiting for the network unobstructed. When the uploaded data are vehicle-end data, the data are transmitted in a binary mode in an uploading format, are acquired through a CAN (controller area network) through a TBOX (tunnel boring machine), and are uploaded to a private cloud in a 4G (fourth generation) transmission mode. When the uploaded data are data of the third party server side, the third party server and the private cloud are used for building HTTP/HTTPS communication to carry out data interactive connection. The private cloud adopts a server architecture with high performance, high availability and load balance of LVS + KeepAlived + Nginx, and can provide high-computing-power service.
The private cloud comprises a data acquisition and analysis service layer, a storage layer and an application layer. The analysis service layer architecture mainly takes HighHarts + Datatables as main components, and comprises Redis, mysql, mongopb, HBase and the like, and the database is selected according to data processing requirements. The application layer comprises algorithm model building, namely parameter screening, importance sorting and weight distribution.
In addition, it should be noted that the first environment data, the first user data, and the first vehicle sensor data include big data obtained from the internet of vehicles, and the second environment data, the second user data, and the second vehicle sensor data include internet of vehicles data and real-time data of individual users. The data range is wider, the generalization error of the model is reduced, the analysis result is more universal, and the result calculated by the big data analysis model can embody the characteristics of thousands of people and thousands of faces due to different data of each vehicle, so that the setting of the air conditioner is more in line with the common habits of drivers.
Step 102: and building an algorithm model according to the first environment data, the first user data and the first vehicle sensor data to obtain a result value and a characteristic equation.
In the embodiment, a fuzzy algorithm and a neural network algorithm are mainly used, and the basic processing process includes calculating the influence of one parameter on the related field, the accumulated sum of certain characteristic values of the related parameters and the corresponding weight to obtain a result value and a characteristic equation. The result value is a value representing the weight in numerical form after analyzing the weight.
Step 103: performing statistical calculation according to the result value and the characteristic equation to obtain a prediction model;
step 104: and performing calculation analysis according to the prediction model and second environmental data, second vehicle sensor data and second user data acquired by the whole vehicle, and predicting the set behavior result of the client air conditioner.
In the present embodiment, the prediction model is a model for predicting the most comfortable driving condition of the driver for the behavior habit of the driver of the individual vehicle.
Step 105: and predicting the setting behavior result of the client air conditioner and setting the parameters of the vehicle-mounted air conditioner.
In this embodiment, the result of the air conditioner setting behavior is predicted, including but not limited to air conditioner related parameters, and the vehicle-mounted air conditioner is subjected to parameter adjustment according to the predicted parameter result, where the adjustment parameters include wind speed, temperature, wind direction, internal and external circulation, air filtration, and drying. In the prior art, the parameters of the air conditioner self-adaptive adjustment comprise the wind speed and the temperature, and compared with the method, the parameters are adjusted more comprehensively, the requirements of users can be met more accurately, and the use experience of the automatic air conditioner is better.
In an adaptive air conditioner adjusting method according to an embodiment of the present invention, an algorithm building model is shown in fig. 2, and includes:
step 201: and preprocessing data according to the first environment data, the first user data and the first vehicle sensor data to obtain effective data.
In this embodiment, the data preprocessing includes deleting invalid signals and abnormal signals in the data to obtain valid data. In the implementation process, faults caused by self factors or environmental factors may occur during the running of the vehicle, driving data collection is affected, and the collected data result is invalid or abnormal, so that in order to eliminate training data which bring errors to a driving behavior analysis model due to emergency situations in a journey, the data needs to be processed, and invalid signals and abnormal signals in the obtained data are deleted according to the validity and reasonableness of the data. The validity of the data is fed back by a numerical value, for example, when the feedback value is 0, the data is an invalid signal.
Step 202: and training a fuzzy algorithm and a neural network training and building model according to the effective data to obtain a result value and a characteristic equation.
In the embodiment, the model building method comprises the steps of analyzing and obtaining characteristic signals of relevant parameters according to first environment data, first user data and first vehicle sensor data; and (4) discriminating strong correlation quantity according to the characteristic signals of the correlation parameters, and using a fuzzy algorithm and neural network training to obtain a result value and a characteristic equation. The data range used by training is wide, the generalization error of the model is reduced, and the analysis result is more universal.
In an adaptive air conditioner adjusting method according to an embodiment of the present invention, a neural network training is shown in fig. 3, and includes:
step 301: according to the first environment data, the first user data and the first vehicle sensor data, models are respectively established through a decision tree algorithm, an XGboost algorithm and a random forest model algorithm, and three algorithm models are obtained.
In the embodiment, three algorithms are used for respectively building the model, the consideration range is wider, the obtained result has better effect than the model built by only considering one algorithm, and better experience can be brought to a user.
Step 302: and (4) carrying out small sample verification according to the three algorithm models to obtain an optimal model, and deploying the optimal model to a carrier with computing power, such as a cloud end or a vehicle end.
In this embodiment, after obtaining the models respectively established by the three algorithms, verification is required to determine which algorithm is the optimal one. The small sample is verified to be that vehicles are randomly searched in the market, three training models are respectively used, and the model is judged to be the optimal model according to the self-adaptive adjustment effect of the air conditioner. After the verification is sufficient, the vehicle networking data is deployed to a carrier for calculation, then the vehicle networking data is accessed to a model for calculation, the air conditioner is automatically adjusted according to an output calculation result, and meanwhile, the vehicle networking data is continuously used for carrying out iterative optimization on the model.
Another embodiment of the present invention relates to an air conditioner adaptive adjustment system, including:
step 401: the third-party server is used for acquiring the first environmental data and the second environmental data, wherein the TSP platform is in data connection with the third-party server to acquire weather information, altitude and geographical position;
step 402: the vehicle end is used for acquiring user behavior and vehicle state data, monitoring information such as user fatigue degree, facial expressions and facial orientation through a DMS system, and uploading the information to the cloud TSP platform through the gateway and the T-BOX after personal information is removed;
step 403: the cloud end is used for carrying out data analysis, model training and model deployment, and obtaining analyzed data from the TSP platform;
step 404: the application terminal comprises a vehicle-mounted air conditioner and an AC controller, and is an air conditioner adaptive adjustment method application terminal.
Compared with the prior art, the method uses a large amount of market car networking data, and uses a supervised learning algorithm to train the model, so that the generalization error of the model is reduced, and the analysis result is more universal. The data of each vehicle are different, the result calculated through the big data analysis model can reflect the characteristics of thousands of people, and the setting of the air conditioner is more in line with the common habits of drivers. The model is established by adopting not only the original data characteristics, but also the characteristic set of the cross scene, such as the vehicle speed and the ambient temperature for starting the air conditioner at the same time, so that the coverage scene is wider.
Another embodiment of the present invention relates to an electronic device, as shown in fig. 5, including:
at least one processor 501;
and a memory 502 communicatively coupled to the at least one processor 501;
the memory 502 stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor 501, so that the at least one processor 501 can execute the air conditioner adaptive adjustment method according to the embodiment of the present invention.
Where the memory and processor are connected by a bus, the bus may comprise any number of interconnected buses and bridges, the bus connecting together various circuits of the memory and the processor or processors. The bus may also connect various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. A bus interface provides an interface between the bus and the transceiver. The transceiver may be one element or a plurality of elements, such as a plurality of receivers and transmitters, providing a means for communicating with various other apparatus over a transmission medium. The data processed by the processor is transmitted over a wireless medium via an antenna, which further receives the data and transmits the data to the processor.
The processor is responsible for managing the bus and general processing and may also provide various functions including timing, peripheral interfaces, voltage regulation, power management, and other control functions. And the memory may be used to store data used by the processor in performing operations.
Another embodiment of the present invention relates to a computer-readable storage medium storing a computer program. The computer program realizes the above-described method embodiments when executed by a processor.
That is, as can be understood by those skilled in the art, all or part of the steps in the method of the embodiments described above may be implemented by a program instructing related hardware, where the program is stored in a storage medium and includes several instructions to enable a device (which may be a single chip, a chip, or the like) or a processor (processor) to execute all or part of the steps in the method of the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The embodiment of the invention also provides a vehicle, which specifically comprises: vehicle air conditioner and air conditioner self-adaptation governing system. The beneficial effects of the vehicle are the same as those of the air conditioner self-adaptive adjusting system, and are not described herein.
The technical solutions provided by the present application are described in detail above, and the principles and embodiments of the present application are described herein by using specific examples, which are only used to help understanding the present application, and the content of the present description should not be construed as limiting the present application. While various modifications of the illustrative embodiments and applications will be apparent to those skilled in the art based upon this disclosure, it is not necessary or necessary to exhaustively enumerate all embodiments, and all obvious variations and modifications can be resorted to, falling within the scope of the disclosure.
The embodiments in the present specification are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
Embodiments of the present invention are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing terminal to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing terminal to cause a series of operational steps to be performed on the computer or other programmable terminal to produce a computer implemented process such that the instructions which execute on the computer or other programmable terminal provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications of these embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the embodiments of the invention.
Finally, it should also be noted that, in this document, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "include", "including" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or terminal device including a series of elements includes not only those elements but also other elements not explicitly listed or inherent to such process, method, article, or terminal device. Without further limitation, an element defined by the phrase "comprising a … …" does not exclude the presence of another identical element in a process, method, article, or terminal that comprises the element.
The foregoing detailed description of the adaptive air conditioning control method, system, electronic device, storage medium and vehicle provided by the present invention has been presented, and specific examples are applied herein to illustrate the principles and embodiments of the present invention, and the description of the foregoing examples is only used to help understand the method and the core idea of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
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| CN116664980A (en) * | 2023-06-12 | 2023-08-29 | 中国第一汽车股份有限公司 | Model training method, vehicle air conditioner automatic temperature adjustment method, device and equipment |
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